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year = {2002},
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number = {10137-10143}
}
@article{Abarbanel03,
author = {H.D.I. Abarbanel and R. Huerta and M.I. Rabinovich},
title = {Dynamical model of long-term synaptic plasticity},
journal = {Proc. Natl. Academy of Sci. USA},
year = {2002},
volume = {59},
number = {10137-10143}
}
@article{Abarbanel04,
author = {H.D.I. Abarbanel and G.B. Mindlin and S. Talathi and L. Gibb and
M.I. Rabinovich},
title = {Dynamic model of birdsong maintenance and control},
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pages = {051911}
}
@article{Abarbanel05,
author = {H.D.I. Abarbanel and S.S. Talathi and L. Gibb and M.I. Rabinovich},
title = {Synaptic plasticity with discrete state synapses},
journal = {Phys. Rev. E},
year = {2005},
volume = {72},
pages = {031914}
}
@article{Abarbanel04a,
author = {H. D. I. Abarbanel and L. Gibb and G. B. Mindlin and S. Talathi},
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journal = {J. Neurophysiology},
year = {2004},
volume = {92},
pages = {96-110}
}
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author = {L.F. Abbott},
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year = {1994},
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pages = {291-331}
}
@incollection{Abbott99,
author = {L.F. Abbott and Sen Song},
title = {Temporally asymmetric Hebbian learning, spike timing and neuronal
response variability},
booktitle = {Advances in Neural Information Processing Systems 11},
publisher = {MIT-Press},
year = {1999},
editor = {S. Kearns and S. A . Solla and D.A. Cohn},
pages = {69-75},
address = {Cambridge}
}
@article{Abbott91,
author = {L. F. Abbott},
title = {Realistic synaptic inputs for model neural networks.},
journal = {Network},
year = {1991},
volume = {2},
pages = {245--258}
}
@article{Abbott90,
author = {Abbott, L F},
title = {A network of oscillators},
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year = {1990},
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}
@article{Abbott96a,
author = {L. F. Abbott and Kenneth I. Blum},
title = {Functional significance of long-term potentiation for sequence learning
and prediction},
journal = {Cerebral Cortex},
year = {1996},
volume = {6},
pages = {406-416}
}
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author = {L. F. Abbott and E. Fahri and S. Gutmann},
title = {The path integral for dendritic trees},
journal = {Biol. Cybern.},
year = {1991},
volume = {66},
pages = {49-60}
}
@incollection{Abbott90b,
author = {L. F. Abbott and T. B. Kepler},
title = {Model neurons: from \protect{H}odgkin-\protect{H}uxley to \protect{H}opfield},
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author = {L. F. Abbott and S. B. Nelson},
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journal = {Nature Neuroscience},
year = {2000},
volume = {3},
pages = {1178-1183}
}
@article{Abbott00a,
author = {L. F. Abbott and Sacha B. Nelson},
title = {Synaptic Plasticity: Taming the Beast},
journal = {Nature Neuroscience},
year = {2000},
volume = {3},
pages = {1178--1183},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Abbott00b,
author = {Abbott, L. F. and Nelson, S. B.},
title = {{Synaptic plasticity: taming the beast}},
journal = {Nature Neuroscience},
year = {2000},
volume = {3},
pages = {1178--1183},
number = {Suppl.},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Abbott04,
author = {L. F. Abbott and Wade G. Regehr},
title = {Synaptic computation},
journal = {Nature},
year = {2004},
volume = {431},
pages = {796-803},
number = {7010},
month = oct,
issn = {0028-0836},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1038/nature03010}
}
@article{Abbott97,
author = {L. F. Abbott and J. A. Varela and K. Sen and S. B. Nelson},
title = {Synaptic depression and cortical gain control},
journal = {Science},
year = {1997},
volume = {275},
pages = {220-224}
}
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author = {L. F. Abbott and C. van Vreeswijk},
title = {Asynchronous states in a network of pulse-coupled oscillators},
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author = {M. Abeles},
title = {Firing rates and well-timed events},
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publisher = {Springer},
year = {1994},
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author = {M. Abeles and Y. Prut and H. Bergmann and E. Vaadia and A. Aertsen},
title = {Integration, Synchronicity, and Periodicity},
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publisher = {Elsevier Science Publishers},
year = {1993},
editor = {A. Aertsen},
pages = {149-181}
}
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author = {M. Abeles and E. Vaadia and H. Bergman and Y.Prut and I. Haalman
and H. Slovin},
title = {Dynamics of neuronal interactions in the frontal cortex of behaving
monkeys},
journal = {Concepts in Neuroscience},
year = {1993},
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pages = {131-158}
}
@article{Abraham03,
author = {W.C. Abraham},
title = {How long will long-term potentiation last?
},
journal = {Philosophical Transactions R. Soc. Lond B: Biological Sciences},
year = {2003},
volume = {358},
pages = {735 - 744 }
}
@article{Abraham02,
author = {W.C. Abraham and B. Logan and J.M. Greenwood and M. Dragunow},
title = {Induction and Experience-Dependent Consolidation of Stable Long-Term
Potentiation Lasting Months in the Hippocampus},
journal = {J. Neuroscience},
year = {2002},
volume = {22},
pages = {9626 - 9634}
}
@article{Abraham08,
author = {Wickliffe C Abraham},
title = {Metaplasticity: tuning synapses and networks for plasticity.},
journal = {Nat Rev Neurosci},
year = {2008},
volume = {9},
pages = {387},
number = {5},
month = {May},
abstract = {Synaptic plasticity is a key component of the learning machinery in
the brain. It is vital that such plasticity be tightly regulated
so that it occurs to the proper extent at the proper time. Activity-dependent
mechanisms that have been collectively termed metaplasticity have
evolved to help implement these essential computational constraints.
Various intercellular signalling molecules can trigger lasting changes
in the ability of synapses to express plasticity; their mechanisms
of action are reviewed here, along with a consideration of how metaplasticity
might affect learning and clinical conditions.},
doi = {10.1038/nrn2356},
keywords = {plasticity},
owner = {sprekeler},
pii = {nrn2356},
pmid = {18401345},
timestamp = {2008.05.08},
url = {http://dx.doi.org/10.1038/nrn2356}
}
@article{Achard06,
author = {Achard, Pablo and De Schutter, Erik},
title = {Complex parameter landscape for a complex neuron model.},
journal = {PLoS Comput Biol},
year = {2006},
volume = {2},
pages = {e94},
number = {7},
abstract = {The electrical activity of a neuron is strongly dependent on the ionic
channels present in its membrane. Modifying the maximal conductances
from these channels can have a dramatic impact on neuron behavior.
But the effect of such modifications can also be cancelled out by
compensatory mechanisms among different channels. We used an evolution
strategy with a fitness function based on phase-plane analysis to
obtain 20 very different computational models of the cerebellar Purkinje
cell. All these models produced very similar outputs to current injections,
including tiny details of the complex firing pattern. These models
were not completely isolated in the parameter space, but neither
did they belong to a large continuum of good models that would exist
if weak compensations between channels were sufficient. The parameter
landscape of good models can best be described as a set of loosely
connected hyperplanes. Our method is efficient in finding good models
in this complex landscape. Unraveling the landscape is an important
step towards the understanding of functional homeostasis of neurons.},
address = {Theoretical Neurobiology, University of Antwerp, Belgium.},
au = {Achard, P and De Schutter, E},
bdsk-url-1 = {http://dx.doi.org/10.1371/journal.pcbi.0020094},
da = {20060719},
date-added = {2008-03-30 22:22:38 +0200},
date-modified = {2008-03-30 22:23:38 +0200},
dcom = {20060908},
doi = {10.1371/journal.pcbi.0020094},
edat = {2006/07/20 09:00},
issn = {1553-7358 (Electronic)},
jid = {101238922},
jt = {PLoS computational biology},
language = {eng},
lr = {20061115},
mh = {Algorithms; Cell Line; Computational Biology; Humans; *Models, Neurological;
Neurons/*metabolism; Purkinje Cells/metabolism; Synapses/metabolism},
mhda = {2006/09/09 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2006/03/23 {$[$}received{$]$}; 2006/06/08 {$[$}accepted{$]$}},
pii = {06-PLCB-RA-0109R2},
pl = {United States},
pmid = {16848639},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {PLoS Comput Biol. 2006 Jul 21;2(7):e94. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Adams05,
author = {J.P. Adams and S.M. Dudek},
title = {Late-phase long-term potentiation: getting to the nucleus},
journal = {Nature Reviews Neuroscience},
year = {2005},
volume = {6},
pages = {737-743 }
}
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author = {Adelsen, E. H. and Bergen, J. R.},
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year = {1985},
volume = {2},
pages = {284--299},
number = {2},
keywords = {Vision, Vision-Models},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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title = {The basis of sensation},
publisher = {W.W. Norton, New York},
year = {1928},
author = {E. D. Adrian}
}
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title = {The impulses produced by sensory nerve endings.},
journal = {J. Physiol. (London)},
year = {1926},
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pages = {49--72}
}
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title = {Response synchronization in the visual cortex},
journal = {Current Opinion in Neurobiology},
year = {1993},
volume = {3},
pages = {586-594}
}
@incollection{Aertsen86,
author = {A. Aertsen and G. Gerstein and P. Johannesma},
title = {From neuron to assembly: Neuronal organization and stimulus representation.},
booktitle = {Brain Theory},
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year = {1986},
editor = {G. Palm and A. Aertsen},
pages = {7-24},
address = {Berlin Heidelberg New York}
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@article{Aguera03b,
author = {Aguera y Arcas, B. and A.L. Fairhall},
title = {What causes a neuron to spike?},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {1789-1803}
}
@article{Aguera03a,
author = {Aguera y Arcas, B. and A.L. Fairhall and W. Bialek},
title = {Computation in a single neuron: Hodgkin-Huxley revisited},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {1715-1749}
}
@article{Aguera03,
author = {Aguera y Arcas, Blaise and Fairhall, Adrienne L and Bialek, William},
title = {Computation in a single neuron: Hodgkin and Huxley revisited.},
journal = {Neural Comput},
year = {2003},
volume = {15},
pages = {1715--1749},
number = {8},
abstract = {A spiking neuron "computes" by transforming a complex dynamical input
into a train of action potentials, or spikes. The computation performed
by the neuron can be formulated as dimensional reduction, or feature
detection, followed by a nonlinear decision function over the low-dimensional
space. Generalizations of the reverse correlation technique with
white noise input provide a numerical strategy for extracting the
relevant low-dimensional features from experimental data, and information
theory can be used to evaluate the quality of the low-dimensional
approximation. We apply these methods to analyze the simplest biophysically
realistic model neuron, the Hodgkin-Huxley (HH) model, using this
system to illustrate the general methodological issues. We focus
on the features in the stimulus that trigger a spike, explicitly
eliminating the effects of interactions between spikes. One can approximate
this triggering "feature space" as a two-dimensional linear subspace
in the high-dimensional space of input histories, capturing in this
way a substantial fraction of the mutual information between inputs
and spike time. We find that an even better approximation, however,
is to describe the relevant subspace as two dimensional but curved;
in this way, we can capture 90% of the mutual information even at
high time resolution. Our analysis provides a new understanding of
the computational properties of the HH model. While it is common
to approximate neural behavior as "integrate and fire," the HH model
is not an integrator nor is it well described by a single threshold.},
address = {Rare Books Library, Princeton University, Princeton, NJ 08544, USA.
blaisea@princeton.edu},
au = {Aguera y Arcas, B and Fairhall, AL and Bialek, W},
bdsk-url-1 = {http://dx.doi.org/10.1162/08997660360675017},
da = {20030926},
date-added = {2008-03-27 14:10:56 +0100},
date-modified = {2008-03-27 14:11:00 +0100},
dcom = {20031023},
doi = {10.1162/08997660360675017},
edat = {2003/09/27 05:00},
issn = {0899-7667 (Print)},
jid = {9426182},
jt = {Neural computation},
language = {eng},
mh = {Action Potentials/*physiology; *Models, Neurological; Neurons/*physiology},
mhda = {2003/10/24 05:00},
own = {NLM},
owner = {sprekeler},
pl = {United States},
pmid = {14511510},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
sb = {IM},
so = {Neural Comput. 2003 Aug;15(8):1715-49. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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journal = {Trends in Cognitive Sciences},
year = {2004},
volume = {8},
pages = {457-464},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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title = {Brain, Behavior, Robotics},
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visual cortex: a laminar analysis.},
journal = {Journal of Physiology},
year = {1984},
volume = {348},
pages = {153--185},
keywords = {Vision, Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
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title = {{Functional connectivity between simple cells and complex cells in
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journal = {Nat Neurosci},
year = {1998},
volume = {1},
pages = {395--403},
number = {5},
keywords = {Vision, Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {D.G. Amaral},
title = {Emerging Principles of intrinsic hippocampal organization},
journal = {Current Opinion in Neurobiology},
year = {1993},
volume = {3},
pages = {225--229},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {S.~Amari},
title = {Neural theory of assocation and concept-formation},
journal = {Biol.~Cybern.},
year = {1977},
volume = {26},
pages = {175-185}
}
@article{Amari77b,
author = {S.~Amari},
title = {A mathematical foundation of statistical neurodynamics},
journal = {SIAM J. Applied Mathematics},
year = {1977},
volume = {33},
pages = {95-126}
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@article{Amari77c,
author = {S.~Amari},
title = {Dynamics of pattern formation in lateral-inhibition type neural fields},
journal = {Biol. Cybern.},
year = {1977},
volume = {27},
pages = {77-87}
}
@article{Amari74,
author = {S. Amari},
title = {A method of statistical neurodynamics},
journal = {Kybernetik},
year = {1974},
volume = {14},
pages = {201-215}
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author = {S. Amari},
title = {Characteristics of random nets of analog neuron-like elements},
journal = {IEEE transactions systems, man, cybernetics},
year = {1972},
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pages = {643-657}
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year = {1994},
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@book{Amit89,
title = {Modeling brain function.},
publisher = {Cambridge University Press},
year = {1989},
author = {D. J. Amit},
address = {Cambridge UK}
}
@article{Amit97,
author = {D. J. Amit and N. Brunel},
title = {Dynamics of a recurrent network of spiking neurons before and following
learning},
journal = {Network},
year = {1997},
volume = {8},
pages = {373-404}
}
@article{Amit97a,
author = {D. J. Amit and N. Brunel},
title = {A model of spontaneous activity and local delay activity during delay
periods in the cerebral cortex},
journal = {Cerebral Cortex},
year = {1997},
volume = {7},
pages = {237-252}
}
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author = {D. J. Amit and H. Gutfreund and H. Sompolinsky},
title = {Statistical mechanics of neural networks near saturation.},
journal = {Ann~Phys~(NY)},
year = {1987},
volume = {173},
pages = {30--67}
}
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author = {Amit, D J and H Gutfreund and H Sompolinsky},
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year = {1987},
volume = {35},
pages = {2293-2303}
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pages = {1007--1032}
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@article{Amit85b,
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networks},
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year = {1985},
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author = {D. J. Amit and M. V. Tsodyks},
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journal = {Network},
year = {1992},
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pages = {121-137}
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@article{Amit91,
author = {D. J. Amit and M. V. Tsodyks},
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spike rates. I: Substrate --- spikes, rates, and neuronal gain.},
journal = {Network},
year = {1991},
volume = {2},
pages = {259--273}
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author = {M. I. Anderson and K. J. Jeffery},
title = {{Heterogeneous modulation of place cell firing by changes in context}},
journal = {Journal of Neuroscience},
year = {2003},
volume = {23},
pages = {8827--8835},
number = {26},
month = oct,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Anzai07,
author = {Anzai, A. and Peng, X. and Van Essen, D.C.},
title = {{Neurons in monkey visual area V2 encode combinations of orientations}},
journal = {Nature Neuroscience},
year = {2007},
volume = {10},
pages = {1313--1321},
keywords = {Vision-Physiology, vision},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Apicella91,
author = {P. Apicella and T. Ljunberg and E. Scarnati and W. Schultz},
title = {Responses to reward in monkey dorsal and ventral striatum},
journal = {Exp. Brain Research},
year = {1991},
volume = {85},
pages = {491-500}
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@article{Apicella92,
author = {P. Apicella and E. Scarnati and T. Ljunberg and W. Schultz},
title = {Neuronal activity in monkey striatum related to the expectation of
predictable environmental events},
journal = {J. Neurophysiol.},
year = {1992},
volume = {68},
pages = {945-960}
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@article{Appleby06,
author = {Peter Appleby and Terry Elliot},
title = {Stable Competitive Dynamics Emerge from Multispike Interactions in
a Stochastic Model of Spike-Timing-Dependent Plasticity},
journal = {Neural Computation},
year = {2006},
volume = {18},
pages = {2414-2464},
number = {10},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Appleby05a,
author = {Peter Appleby and Terry Elliot},
title = {Synaptic and Temporal Ensemble Interpretation of Spike-Timing-Dependent
Plasticity},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {2316--2336},
number = {11},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Appleby05,
author = {P.A. Appleby and T. Elliott},
title = {Synaptic and temporal ensemble interpretation of spike-timing-dependent
plasticity},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {2316-2336}
}
@article{Araujo01,
author = {I. E. T. de Araujo and E. T. Rolls and S. M. Stringer},
title = {A view model which accounts for the spatial fields of hippocampal
primate spatial view cells and rat place cells},
journal = {Hippocampus},
year = {2001},
volume = {11},
pages = {699--706},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Arbib95,
title = {The handbook of brain theory and neural networks},
publisher = {MIT Press},
year = {1995},
author = {M. A. Arbib},
address = {Cambridge, MA}
}
@article{Arbuthnot96,
author = {G. W. Arbuthnit and J. R. Wickens},
title = {Dopamine cells are neurons too},
journal = {Trends in Neurosciences},
year = {1996},
volume = {19},
pages = {279}
}
@article{Arieli96,
author = {A. Arieli and A. STerkin and A. Grinvald and A. Aertsen},
title = {Dynamics of Ongoing Activity: Explanation of the Large Variability
in Evoked Cortical Responses },
journal = {Science},
year = {1996},
volume = {273},
pages = {1868-1871}
}
@incollection{Arleo01a,
author = {A. Arleo and W. Gerstner},
title = {{Hippocampal spatial model for state space representation in robotic
reinforcement learning}},
booktitle = {Proceedings of the fifth European Workshop on Reinforcement learning},
publisher = {CKI, Utrecht University},
year = {2001},
editor = {M. A. Wiering},
pages = {1-3}
}
@article{Arleo01,
author = {A. Arleo and W. Gerstner},
title = {{Spatial orientation in navigating agents: Modeling head-direction
cells}},
journal = {Neurocomputing},
year = {2001},
volume = {38-40},
pages = {1059--1065}
}
@article{Arleo00,
author = {A. Arleo and W. Gerstner},
title = {Spatial cognition and neuro-mimetic navigation: a model of hippocampal
place cell activity},
journal = {Biological Cybernetics},
year = {2000},
volume = {83},
pages = {287-299},
number = {3}
}
@incollection{Arleo99a,
author = {A. Arleo and W. Gerstner},
title = {A vision-driven model of hippocampal place cells and temporally asymmetric
\protect{LTP}-induction for action learning},
booktitle = {ICANN'99 Artificial Neural Networks},
publisher = {IEE Conference Publication},
year = {1999},
pages = {132-137}
}
@article{Arleo04,
author = {A. Arleo and F. Smeraldi and W. Gerstner},
title = {Cognitive navigation based on non-uniform Gabor space sampling, unsupervised
growing networks, and reinforcement learning},
journal = {IEEE Transactions on Neural Networks,},
year = {2004},
volume = {15},
pages = {639-652}
}
@article{Arleo04a,
author = {A. Arleo and M. Zugaro and C. D\'{e}jean and E. Burgui\`{e}re and
M. Khamassi and S. I. Wiener},
title = {Rat anterodorsal thalamic head direction neurons depend upon dynamic
visual signals to select anchoring landmark cues},
journal = {Europ Journal of Neuroscience},
year = {2004},
volume = {20},
pages = {530--536},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Arsiero07,
author = {Arsiero, Maura and Luscher, Hans-Rudolf and Lundstrom, Brian Nils
and Giugliano, Michele},
title = {The impact of input fluctuations on the frequency-current relationships
of layer 5 pyramidal neurons in the rat medial prefrontal cortex.},
journal = {J Neurosci},
year = {2007},
volume = {27},
pages = {3274--3284},
number = {12},
abstract = {The role of irregular cortical firing in neuronal computation is still
debated, and it is unclear how signals carried by fluctuating synaptic
potentials are decoded by downstream neurons. We examined in vitro
frequency versus current (f-I) relationships of layer 5 (L5) pyramidal
cells of the rat medial prefrontal cortex (mPFC) using fluctuating
stimuli. Studies in the somatosensory cortex show that L5 neurons
become insensitive to input fluctuations as input mean increases
and that their f-I response becomes linear. In contrast, our results
show that mPFC L5 pyramidal neurons retain an increased sensitivity
to input fluctuations, whereas their sensitivity to the input mean
diminishes to near zero. This implies that the discharge properties
of L5 mPFC neurons are well suited to encode input fluctuations rather
than input mean in their firing rates, with important consequences
for information processing and stability of persistent activity at
the network level.},
address = {Institute of Physiology, University of Bern, CH-3012 Bern, Switzerland.},
au = {Arsiero, M and Luscher, HR and Lundstrom, BN and Giugliano, M},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.4937-06.2007},
da = {20070322},
date-added = {2008-03-29 13:26:21 +0100},
date-modified = {2008-03-29 13:26:32 +0100},
dcom = {20070412},
doi = {10.1523/JNEUROSCI.4937-06.2007},
edat = {2007/03/23 09:00},
gr = {T32 07266/United States PHS},
issn = {1529-2401 (Electronic)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20071203},
mh = {Action Potentials/*physiology; Animals; Electric Stimulation/methods;
Nerve Net/physiology; Neurons/*physiology; Prefrontal Cortex/*physiology;
Rats; Rats, Wistar},
mhda = {2007/04/14 09:00},
own = {NLM},
owner = {sprekeler},
pii = {27/12/3274},
pl = {United States},
pmid = {17376988},
pst = {ppublish},
pt = {Comparative Study; Journal Article; Research Support, N.I.H., Extramural;
Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {J Neurosci. 2007 Mar 21;27(12):3274-84. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Artola90,
author = {A. Artola and S. Br\protect{\"o}cher and W. Singer},
title = {Different voltage dependent thresholds for inducing long-term depression
and long-term potentiation in slices of rat visual cortex},
journal = {Nature},
year = {1990},
volume = {347},
pages = {69-72}
}
@article{Artola93,
author = {Alain Artola and Wolf Singer},
title = {Long-Term depression of excitatory synaptic transmission and its
relationship to long-term potentiation},
journal = {Trends Neurosci.},
year = {1993},
volume = {16},
pages = {480--487},
number = {11}
}
@article{Astakhov98,
author = {V. Astakhov and M. Hasler and T. Kapitaniak and A. Shabunin and V.
Anishchenko},
title = {Effect of parameter mismatch on the mechanism of chaos synchronization
loss in coupled systems},
journal = {Physical Revue E},
year = {1998},
volume = {58},
pages = {5620-5628},
annote = {Hasler - paper cited in grant proposal}
}
@article{Atick90,
author = {J.J. Atick and A.N. Redlich},
title = {Towards a theory of early visual processing},
journal = {Neural Computation},
year = {1990},
volume = {4},
pages = {559-572}
}
@article{Atick92,
author = {Atick, Jospeh J.},
title = {Could information theory provide an ecological theory of sensory
processing?},
journal = {Network: Computation in Neural Systems},
year = {1992},
volume = {3},
pages = {213-251},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Attneave54,
author = {Fred Attneave},
title = {Some Informational Aspects of Visual Perception},
journal = {Psychological Review},
year = {1954},
volume = {61},
pages = {183--192},
number = {3},
keywords = {Vision-Models, Optimal-Coding},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Av-ron91,
author = {E. Av-Ron and H. Parnas and L.A. Segel},
title = {A minimal biophysical model for an excitable and oscillatory neuron},
journal = {Biol. Cybern.},
year = {1991},
volume = {65},
pages = {487-500}
}
@article{Aviel06,
author = {Y. Aviel and W. Gerstner},
title = {From spiking neurons to rate models: a cascade model as an approximation
to spiking neuron models with refractoriness },
journal = {Phys. Rev. E},
year = {2006},
volume = {73},
pages = {51908}
}
@article{Aviel06a,
author = {Aviel, Y. and Gerstner, W.},
title = {{From spiking neurons to rate models: A cascade model as an approximation
to spiking neuron models with refractoriness}},
journal = {Physical Review E},
year = {2006},
volume = {73},
pages = {51908},
number = {5},
keywords = {neuronal-processing},
owner = {sprekeler},
publisher = {APS},
timestamp = {2008.04.14}
}
@article{Azouz00,
author = {R. Azouz and C.M. Gray},
title = {Dynamic spike threshold reveals a mechanism for coincidence detection
in cortical neurons in vivo},
journal = {Proc. National Academy of Sciences USA},
year = {2000},
volume = {97},
pages = {8110-8115}
}
@article{Azouz03,
author = {Rony Azouz and Charles M. Gray},
title = {Adaptive Coincidence Detection and Dynamic Gain Control in Visual
Cortical Neurons In Vivo},
journal = {Neuron},
year = {2003},
volume = {37},
pages = {513--523}
}
@article{Azouz03a,
author = {Azouz, Rony and Gray, Charles M},
title = {Adaptive coincidence detection and dynamic gain control in visual
cortical neurons in vivo.},
journal = {Neuron},
year = {2003},
volume = {37},
pages = {513--523},
number = {3},
abstract = {Several theories have proposed a functional role for response synchronization
in sensory perception. Critics of these theories have argued that
selective synchronization is physiologically implausible when cortical
networks operate at high levels of activity. Using intracellular
recordings from visual cortex in vivo, in combination with numerical
simulations, we find dynamic changes in spike threshold that reduce
cellular sensitivity to slow depolarizations and concurrently increase
the relative sensitivity to rapid depolarizations. Consistent with
this, we find that spike activity and high-frequency fluctuations
in membrane potential are closely correlated and that both are more
tightly tuned for stimulus orientation than the mean membrane potential.
These findings suggest that under high-input conditions the spike-generating
mechanism adaptively enhances the sensitivity to synchronous inputs
while simultaneously decreasing the sensitivity to temporally uncorrelated
inputs.},
address = {Center for Computational Biology and Department of Cell Biology and
Neuroscience, Montana State University, Bozeman, MT 59717, USA.},
au = {Azouz, R and Gray, CM},
da = {20030210},
date-added = {2008-03-27 01:33:03 +0100},
date-modified = {2008-03-27 01:33:13 +0100},
dcom = {20030404},
edat = {2003/02/11 04:00},
issn = {0896-6273 (Print)},
jid = {8809320},
jt = {Neuron},
language = {eng},
lr = {20061115},
mh = {Action Potentials/physiology; Adaptation, Ocular/*physiology; Animals;
Cats; Evoked Potentials, Visual/physiology; Female; Male; Neurons/*physiology;
Orientation/physiology; Sensory Thresholds/physiology; Visual Cortex/*cytology/*physiology},
mhda = {2003/04/05 05:00},
own = {NLM},
owner = {sprekeler},
pii = {S0896627302011868},
pl = {United States},
pmid = {12575957},
pst = {ppublish},
pt = {Journal Article; Research Support, U.S. Gov't, P.H.S.},
pubm = {Print},
sb = {IM},
so = {Neuron. 2003 Feb 6;37(3):513-23. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Bach00,
author = {M. Bach and C. Schmitt and T. Quenzer and T. Meigen and M. Fahle},
title = {{Summation of texture segregation across orientation and spatial
frequency: electrophysiological and psychophysical findings}},
journal = {Vision Research},
year = {2000},
volume = {40},
pages = {3559--3566},
number = {26},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Baddeley98,
author = {R. Baddeley and L. F. Abbott and M. Booth and F. Sengpiel and T.
Freeman},
title = {Responses of neurons in primary and inferior temporal visual cortices
to natural scenes},
booktitle = {Proceedings of the Royal Society London},
year = {1998},
number = {264},
series = {B},
pages = {1775-1783},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Badel06,
author = {L. Badel and W. Gerstner and M.J.E. Richardson},
title = {Dependence of the Spike-Triggered Average Voltage on Membrane Response
Properties},
journal = {Neurocomputing},
year = {2007},
volume = {69},
pages = {1062-1065}
}
@article{Badel07,
author = {Badel, L and Lefort, S and Brette, R and Petersen, CC and Gerstner,
W and Richardson, MJ},
title = {Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron
voltage traces.},
journal = {J Neurophysiol},
year = {2007},
abstract = {Neuronal response properties are typically probed by intracellular
measurements of current-voltage (I-V) relationships during application
of current or voltage steps. Here we demonstrate the measurement
of a novel I-V curve measured while the neuron exhibits a fluctuating
voltage and emits spikes. This dynamic I-V curve requires only a
few tens of seconds of experimental time and so lends itself readily
to the rapid classification of cell type, quantification of heterogeneities
in cell populations and to the generation of reduced analytical models.
We apply this technique to layer-5 pyramidal cells and show that
their dynamic I-V curve comprises linear and exponential components,
providing experimental evidence for a recently-proposed theoretical
model. The approach also allows us to determine the change of neuronal
response properties after a spike, millisecond by millisecond, so
that post-spike refractoriness of pyramidal cells can be quantified.
Observations of I-V curves during and in absence of refractoriness
are cast into a model which is used to predict both the subthreshold
response and spiking activity of the neuron to novel stimuli. The
predictions of the resulting model are in excellent agreement with
experimental data and close to the intrinsic neuronal reproducibility
to repeated stimuli.},
address = {School of Computer and Communications Sciences and Brain-Mind Institute,
Ecole Polytechnique Federale de Lausanne, Switzerland.},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.01107.2007},
da = {20071206},
date-added = {2007-12-12 22:17:33 +0100},
date-modified = {2007-12-12 22:23:24 +0100},
dep = {20071205},
doi = {10.1152/jn.01107.2007},
edat = {2007/12/07 09:00},
issn = {0022-3077 (Print)},
jid = {0375404},
language = {ENG},
mhda = {2007/12/07 09:00},
own = {NLM},
owner = {sprekeler},
pii = {01107.2007},
pmid = {18057107},
pst = {aheadofprint},
pt = {JOURNAL ARTICLE},
pubm = {Print-Electronic},
so = {J Neurophysiol. 2007 Dec 5;. },
stat = {Publisher},
timestamp = {2008.04.14}
}
@article{Badel04,
author = {L. Badel and A. Tonnelier},
title = {Pulse propagation in discrete excitatory networks of integrate-and-fire
neurons},
journal = {Physical Review E},
year = {2004},
volume = {70},
pages = {11906}
}
@article{Badoual06,
author = {Mathilde Badoual and Quan Zou and Andrew P Davison and Michael Rudolph
and Thierry Bal and Yves Frégnac and Alain Destexhe},
title = {Biophysical and phenomenological models of multiple spike interactions
in spike-timing dependent plasticity.},
journal = {Int J Neural Syst},
year = {2006},
volume = {16},
pages = {79--97},
number = {2},
month = {Apr},
abstract = {Spike-timing dependent plasticity (STDP) is a form of associative
synaptic modification which depends on the respective timing of pre-
and post-synaptic spikes. The biophysical mechanisms underlying this
form of plasticity are currently not known. We present here a biophysical
model which captures the characteristics of STDP, such as its frequency
dependency, and the effects of spike pair or spike triplet interactions.
We also make links with other well-known plasticity rules. A simplified
phenomenological model is also derived, which should be useful for
fast numerical simulation and analytical investigation of the impact
of STDP at the network level.},
institution = {Integrative and Computational Neuroscience Unit (UNIC), CNRS, Gif-sur-Yvette,
France.},
keywords = {Action Potentials; Animals; Biophysics; Computer Simulation; Models,
Neurological; Neuronal Plasticity; Neurons; Synapses},
owner = {cmellier},
pii = {S0129065706000524},
pmid = {16688849},
timestamp = {2008.05.08}
}
@article{Bair96,
author = {W. Bair and C. Koch},
title = {Temporal precision of spike trains in extrastriate cortex of the
behaving macaque monekey},
journal = {Neural Computation},
year = {1996},
volume = {8},
pages = {1185-1202}
}
@article{Bair94,
author = {W. Bair and C. Koch and W. Newsome and K. Britten},
title = {Power spectrum analysis of \protect{MT} neurons in the behaving monkey},
journal = {J. Neurosci.},
year = {1994},
volume = {14},
pages = {2870-2892}
}
@article{Bair01,
author = {W. Bair and E. Zohary and W.T. Newsome},
title = {Correlated firing in macaque visual area MT: Time scales and relationship
to behavior},
journal = {J. Neuroscience},
year = {2001},
volume = {21},
pages = {1676-1697}
}
@article{Baird86,
author = {B. Baird},
title = {Nonlinear dynamics of pattern formation and pattern recognition in
the rabbit olfactory bulb.},
journal = {Physica D},
year = {1986},
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pages = {150--175}
}
@article{Baird01,
author = {J. C. Baird and J. S. Taube and D. V. Peterson},
title = {{Statistical and information properties of head direction cells}},
journal = {Perception \& psychophysics},
year = {2001},
volume = {63},
pages = {1026--1037},
number = {6},
month = aug,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {P. Bak and C. Tang},
title = {xxx},
journal = {J. Geophys. Res.},
year = {1976},
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}
@article{Baldissera74,
author = {F. Baldissera and B. Gustafsson},
title = {Firing behavior of a neuron model based on the afterhyperpolarisation
conductance time course and algebraic summation. Adaptation and steady
state firing},
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year = {1974},
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pages = {27-47}
}
@article{Ball87,
author = {K. Ball and R. Sekuler},
title = {Direction-specific improvement in motion discrimination},
journal = {Vision Research},
year = {1987},
volume = {27},
pages = {953-965},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Ball82,
author = {K. Ball and R. Sekuler},
title = {A specific and enduring improvement in visual motion discrimination},
journal = {Science},
year = {1982},
volume = {218},
pages = {697-698},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Baras07,
author = {Dorit Baras and Ron Meir},
title = {Reinforcement Learning, Spike-Time-Dependent Plasticity, and the
BCM Rule},
journal = {Neural Computation},
year = {2007},
volume = {19},
pages = {2245--2279},
number = {8},
address = {Cambridge, MA, USA},
doi = {http://dx.doi.org/10.1162/neco.2007.19.8.2245},
issn = {0899-7667},
owner = {sprekeler},
publisher = {MIT Press},
timestamp = {2008.04.14}
}
@article{Barber02,
author = {D. Barber and F. Agakov},
title = {Spiking Sequence Learning using maximum likelihood: {H}opfield networks},
journal = {Neural Computation},
year = {2002},
volume = {to appear},
pages = {xx}
}
@article{Barlow80,
author = {H.B. Barlow},
title = {The absolute efficiency of perceptual decisions},
journal = {Phil. Trans. R. Soc. Lond. Ser. B},
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pages = {71-82}
}
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author = {H.B. Barlow},
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}
@inbook{Barlow89a,
pages = {54--72},
title = {{Adaptation and decorrelation in the cortex}},
publisher = {Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA},
year = {1989},
author = {Barlow, H. and F{\"o}ldi{\'a}k, P.},
journal = {Addison-Wesley Computation And Neural Systems Series},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Barlow69,
author = {H.B. Barlow and W.R. Levick},
title = {3 factors limiting reliable detection of light by retinal ganglion
cells of cat},
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}
@incollection{Barlow61a,
author = {Horace B. Barlow},
title = {Possible Principles Underlying the Transformations of Sensory Messages},
booktitle = {Sensory Communication},
publisher = {MIT Press},
year = {1961},
editor = {W. A. Rosenblith},
pages = {183--192},
edition = {{Cambridge}},
keywords = {Optimal-Coding},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@incollection{Barlow61,
author = {H. B. Barlow},
title = {Possible Principles underlying the transformation of sensory messages},
booktitle = {Sensory Communication},
publisher = {MIT Press},
year = {1961},
editor = {W. A. Rosenbluth},
pages = {217-234}
}
@article{Barlow89,
author = {H. B. Barlow},
title = {Unsupervised learning},
journal = {Neural. Comp.},
year = {1989},
volume = {1},
pages = {295-311}
}
@article{Barlow63,
author = {H. B. Barlow},
title = {The information capacity of nervous transmission},
journal = {Kybernetik},
year = {1963},
volume = {2},
pages = {1}
}
@article{Barlow71,
author = {H. B. Barlow and W. R. Levick and M. Yoon},
title = {Responses to single quanta of light in retinal ganglion cells of
the cat},
journal = {Vision Res. Suppl.},
year = {1971},
volume = {3},
pages = {87-101}
}
@article{Barlow64,
author = {J.S. Barlow},
title = {{Inertial navigation as a basis for animal navigation}},
journal = {Journal of Theoretical Biology},
year = {1964},
volume = {6},
pages = {76--117}
}
@book{Barndorff-Nielsen89,
title = {{Asymptotic techniques for use in statistics}},
publisher = {Chapman and Hall New York},
year = {1989},
author = {Barndorff-Nielsen, O.E. and Cox, D.R.},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Barto83,
author = {A.G. Barto and R.S. Sutton and C.W. Anderson},
title = {Neuronlike adaptive elements that can solve difficult learning and
control problems},
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year = {1983},
volume = {13},
pages = {835-846}
}
@article{Bartsch01,
author = {Bartsch, AP and van Hemmen, JL},
title = {Combined {H}ebbian development of geniculocortical and lateral connectivity
in a model of primary visual cortex},
journal = {Biological Cybernetics},
year = {2001},
volume = {84},
pages = {41--55},
number = {1},
keywords = {vision, plasticity, vision-models},
owner = {sprekeler},
publisher = {Springer},
timestamp = {2008.04.14}
}
@article{Bastian92,
author = {J. Bastian},
title = {Pyramidal cell plasticity in weakly electric fish: a mechansim for
attenuating responses to reafferent electrosensory inputs},
journal = {J. Comp. Physiol. A},
year = {1992},
volume = {176},
pages = {63-73}
}
@article{Bauer93b,
author = {H. U. Bauer and K. Pawelzik},
title = {Alternating oscillatory and stochastic dynamics in a model for a
neuronal assembly.},
journal = {Physica D},
year = {1993},
volume = {69},
pages = {380--393}
}
@inproceedings{Bauer93a,
author = {H. U. Bauer and K. Pawelzik and T. Geisel},
title = {Emergence of transient oscillations in an ensemble of neurons.},
booktitle = {Proceedings of the ICANN'93},
year = {1993},
editor = {S. Gielen and B. Kappen},
pages = {136--141},
address = {London},
publisher = {Springer}
}
@article{Baxter01a,
author = {Baxter, J. and Bartlett, P.L.},
title = {{Infinite-horizon policy-gradient estimation}},
journal = {Journal of Artificial Intelligence Research},
year = {2001},
volume = {15},
pages = {319--350},
number = {4},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Baxter01,
author = {Baxter, J. and Bartlett, P. and Weaver, L. },
title = {Experiments with Infinite-Horizon, Policy- Gradient Estimation},
journal = {Journal of Artificial Intelligence Research},
year = {2001},
volume = {15},
pages = {351--381},
citeulike-article-id = {2374785},
keywords = {daanbib, gradients, policy},
priority = {2}
}
@book{Beale90,
title = {Neural Computation: An Introduction},
publisher = {IOP publishing},
year = {1990},
author = {R. Beale and T. Jackson},
address = {Bristol}
}
@article{Bear03,
author = {M.F. Bear},
title = {Bidirectional synaptic plasticity: from theory to reality},
journal = {Philosophical Transactions R. Soc. Lond B: Biological Sciences },
year = {2003},
volume = {358},
pages = {649 - 655}
}
@article{Bear94,
author = {M.F. Bear and R. C. Malenka},
title = {Synaptic plasticity: LTP and LTD},
journal = {Curr. Opin. Neurobil.},
year = {1994},
volume = {4},
pages = {389-399}
}
@article{Beck08,
author = {Heinz Beck and Yoel Yaari},
title = {Plasticity of intrinsic neuronal properties in CNS disorders.},
journal = {Nat Rev Neurosci},
year = {2008},
volume = {9},
pages = {357--369},
number = {5},
month = {May},
abstract = {The input-output relationship of neuronal networks depends both on
their synaptic connectivity and on the intrinsic properties of their
neuronal elements. In addition to altered synaptic properties, profound
changes in intrinsic neuronal properties are observed in many CNS
disorders. These changes reflect alterations in the functional properties
of dendritic and somatic voltage- and Ca2+-gated ion channels. The
molecular mechanisms underlying this intrinsic plasticity comprise
the highly specific transcriptional or post-transcriptional regulation
of ion-channel expression, trafficking and function. The studies
reviewed here show that intrinsic plasticity, in conjunction with
synaptic plasticity, can fundamentally alter the input-output properties
of neuronal networks in CNS disorders.},
doi = {10.1038/nrn2371},
keywords = {plasticity},
owner = {sprekeler},
pii = {nrn2371},
pmid = {18425090},
timestamp = {2008.05.08},
url = {http://dx.doi.org/10.1038/nrn2371}
}
@article{Becker05,
author = {Suzanna Becker},
title = {A computational principle for hippocampal learning and neurogenesis},
journal = {Hippocampus},
year = {2005},
volume = {15},
pages = {722--738},
keywords = {hippocampus, neurogenesis},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Becker96,
author = {S. Becker},
title = {Mutual Information Maximization: Models of Cortical Self-Organization},
journal = {Network: Computation in Neural Systems},
year = {1996},
pages = {7-31},
owner = {sprekeler},
timestamp = {2008.04.14},
volune = {7}
}
@article{Becker92,
author = {Becker, Suzanna and Hinton, Geoffrey E.},
title = {A Self-Organizing Neural Network that Discovers Surfaces in Random-Dot
Stereograms.},
journal = {Nature},
year = {1992},
volume = {355},
pages = {161--163},
number = {6356},
keywords = {slowness, Vision-Models},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://www.science.mcmaster.ca/Psychology/becker/papers/BH92.abs.html}
}
@article{Bednar00,
author = {Bednar, J.A. and Miikkulainen, R.},
title = {Tilt Aftereffects in a Self-Organizing Model of the Primary Visual
Cortex},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {1721--1740},
number = {7},
keywords = {Plasticity, Vision, Vision-Models},
owner = {sprekeler},
publisher = {MIT Press},
timestamp = {2008.04.14}
}
@article{Beggs00,
author = {J.M. Beggs},
title = {A statistical theory of long-term potentiation and depression},
journal = {Neural Computation},
year = {2000},
volume = {13},
pages = {87-111}
}
@article{Bell95,
author = {A.J. Bell and T.J. Sejnowski},
title = {An information maximization approach to blind separation and blind
deconvolution},
journal = {Neural Computation},
year = {1995},
volume = {7},
pages = {1129-1159}
}
@article{Bell97b,
author = {Anthony J. Bell and Terrence J. Sejnowski},
title = {The 'Independent Components' of Natural Scenes are Edge Filters},
journal = {Vision Research},
year = {1997},
volume = {37},
pages = {3327--3338},
keywords = {ICA, vision, Vision-Models, Optimal-Coding},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Bell95a,
author = {Anthony J. Bell and Terrence J. Sejnowski},
title = {An information maximisation approach to blind separation and blind
deconvolution},
journal = {Neural Computation},
year = {1995},
volume = {7},
pages = {1129--1159},
number = {6},
keywords = {ICA },
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Bell97a,
author = {C.C. Bell and D. Bodznick and J. Montgomery and J. Bastian},
title = {The generation and subtraction of sensory expectations within cerebellar-like
structures},
journal = {Brain. Beh. Evol.},
year = {1997},
volume = {50},
pages = {17-31},
note = {suppl. {I}}
}
@article{Bell97,
author = {C.C. Bell and V. Han and Y. Sugawara and K. Grant},
title = {Synaptic plasticity in a cerebellum-like structure depends on temporal
order},
journal = {Nature},
year = {1997},
volume = {387},
pages = {278-281}
}
@article{Bellman57b,
author = {R.E. Bellman},
title = {A Markov decision process},
journal = {J. Mathematical Mechanics},
year = {1957},
volume = {6},
pages = {679-684}
}
@book{Bellman57,
title = {Dynamic Programming},
publisher = {Princeton University Press},
year = {1957},
author = {R. E. Bellman},
address = {Princeton}
}
@incollection{Bell05a,
author = {Anthony J. {Bell} and Lucas C. {Parra}},
title = {Maximising Sensitivity in a Spiking Network},
booktitle = {Advances in Neural Information Processing Systems 17},
publisher = {MIT Press},
year = {2005},
editor = {Lawrence K. Saul and Yair Weiss and {L\'{e}on} Bottou},
pages = {121-128},
address = {Cambridge, MA},
original = {0121_533.PDF}
}
@article{Belouchrani97,
author = {Adel Belouchrani and Karim Abed-Meraim and Jean-Francois Cardoso
and Eric Moulines},
title = {A blind source separation technique using second order statistics},
journal = {IEEE Transactions on Signal Processing},
year = {1997},
volume = {45},
pages = {434--444},
keywords = {ICA },
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Belykh00,
author = {I. Belykh and V. Belykh and M. Hasler},
title = {Hierarchy and stability of partially synchronous oscillations of
diffusively coupled dynamical systems},
journal = {Physical Revue E},
year = {2000},
volume = {62},
pages = {6332-6345}
}
@article{Belykh05,
author = {I. Belykh and E. De Lange and M. Hasler},
title = {Synchronization of Bursting Neurons: What Matters in the Network
Topology},
journal = {Phys.~{R}ev.~{L}ett.},
year = {2005},
volume = {94},
pages = {8101},
number = {18}
}
@article{Belykh03,
author = {V. Belykh and I. Belykh and M. Hasler},
title = {Connection graph stability method for synchronized coupled chaotic
systems},
journal = {Physica D},
year = {2004},
volume = {195},
pages = {159-187}
}
@article{Belykh04,
author = {V. Belykh and I. Belykh and M. Hasler},
title = {Blinking model and synchronization in small-world networks with a
time-varying coupling},
journal = {Physica D},
year = {2004},
volume = {195},
pages = {188-206}
}
@article{Belykh01,
author = {V. Belykh and I. Belykh and M. Hasler and K. Nevidin},
title = {Cluster synchronization in three-dimensional lattices of diffusively
coupled oscillators},
journal = {Int. J. Bifurcation and Chaos},
year = {2003},
volume = {13},
pages = {755-779}
}
@article{Belykh00b,
author = {V.N. Belykh and I.V. Belykh and E. Mosekilde and M. Colding-Jorgensen},
title = {Homoclinic bifurcation of cell model with bursting oscillations},
journal = {European Physical Journal E},
year = {2000},
volume = {3},
pages = {205-219},
annote = {hasler citation}
}
@article{Belykh02,
author = {V. Belykh and I. Belykh and K. Nevidin and M. Hasler},
title = {Persistent clusters in lattices of coupled nonidentical chaotic systems},
journal = {Int. J. Bifurcation and Chaos},
year = {2003},
volume = {13},
pages = {165-178}
}
@article{Ben95,
author = {G. {Ben Arous} and A. Guionnet},
title = {Large deviations for Langevin spin glass dynamics},
journal = {Probability Theory and Related Fields},
year = {1995},
volume = {102},
pages = {455-509}
}
@article{BenYishai95,
author = {R. Ben-Yishai and R.L. Bar-Or and H. Sompolinsky},
title = {Theory of orientation tuning in visual cortex},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1995},
volume = {92},
pages = {3844-3848}
}
@article{Benda03,
author = {Benda, Jan and Herz, Andreas V. M.},
title = {A Universal Model for Spike-Frequency Adaptation},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {2523-2564},
number = {11},
abstract = {Spike-frequency adaptation is a prominent feature of neural dynamics.
Among other mechanisms, various ionic currents modulating spike generation
cause this type of neural adaptation. Prominent examples are voltage-gated
potassium currents (M-type currents), the interplay of calcium currents
and intracellular calcium dynamics with calcium-gated potassium channels
(AHP-type currents), and the slow recovery from inactivation of the
fast sodium current. While recent modeling studies have focused on
the effects of specific adaptation currents, we derive a universal
model for the firing-frequency dynamics of an adapting neuron that
is independent of the specific adaptation process and spike generator.
The model is completely defined by the neuron's onset f-I curve,
the steady-state f-I curve, and the time constant of adaptation.
For a specific neuron, these parameters can be easily determined
from electrophysiological measurements without any pharmacological
manipulations. At the same time, the simplicity of the model allows
one to analyze mathematically how adaptation influences signal processing
on the single-neuron level. In particular, we elucidate the specific
nature of high-pass filter properties caused by spike-frequency adaptation.
The model is limited to firing frequencies higher than the reciprocal
adaptation time constant and to moderate fluctuations of the adaptation
and the input current. As an extension of the model, we introduce
a framework for combining an arbitrary spike generator with a generalized
adaptation current.},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Benhamou90,
author = {S. Benhamou},
title = {An analysis of movements of the Wood mouse Apodemus sylvaticus in
its home range},
journal = {Behavioral Processes},
year = {1990},
volume = {22},
pages = {235--250},
keywords = {Hippocampus, Various Artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Berkes05b,
author = {P. Berkes},
title = {Handwritten digit recognition with Nonlinear Fisher Discriminant
Analysis},
journal = {Proceedings of ICANN 2005},
year = {2005},
volume = {2},
pages = {285--287},
number = {LNCS 3696},
owner = {sprekeler},
publisher = {Springer},
timestamp = {2008.04.14}
}
@article{Berkes05c,
author = {Berkes, Pietro},
title = {Pattern Recognition with Slow Feature Analysis},
journal = {Cognitive Sciences EPrint Archive (CogPrints)},
year = {2005},
volume = {4104},
keywords = {slowness, sfa},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Berkes05,
author = {Pietro Berkes and Laurenz Wiskott},
title = {Analysis of inhomogeneous quadratic forms for physiological and theoretical
studies},
booktitle = {Proc.\ Computational and Systems Neuroscience, COSYNE'05, Salk Lake
City},
year = {2005},
month = mar,
note = {(abstract)},
keywords = {SFA, slowness, Vision-Models, vision},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://itb.biologie.hu-berlin.de/~wiskott/Abstracts/BerkWisk2005b.html}
}
@article{Berkes05a,
author = {Pietro Berkes and Laurenz Wiskott},
title = {Slow feature analysis yields a rich repertoire of complex cells},
journal = {Journal of Vision},
year = {2005},
volume = {5(6)},
pages = {579-602},
keywords = {SFA, slowness, Vision-Models, vision},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@misc{Berkes07,
author = {Berkes, Pietro and Zito, Tiziano},
title = {Modular Modular Toolkit for Data Processing (version 2.1)},
howpublished = {http://mdp-toolkit.sourceforge.net},
year = {2007},
keywords = {Slowness},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://mdp-toolkit.sourceforge.net}
}
@article{Bernander91,
author = {{\"O}. Bernander and R. J. Douglas and K. A. C. Martin and C. Koch},
title = {Synaptic background activity influences spatiotemporal integration
in single pyramidal cells},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1991},
volume = {88},
pages = {11569-11573}
}
@article{Berns98,
author = {G. Berns and T. Sejnowski},
title = {A computational model of how the basal ganglia produce sequences},
journal = {J. Cog. Neuroscience},
year = {1998},
volume = {10},
pages = {108-121}
}
@article{Berridge07,
author = {Berridge, Kent},
title = {The debate over dopamine’s role in reward: the case for incentive
salience},
journal = {Psychopharmacology},
year = {2007},
volume = {191},
pages = {391--431},
number = {3},
month = apr,
abstract = {Abstract Introduction Debate continues over the precise
causal contribution made by mesolimbic dopamine systems to reward.
There are three competing explanatory categories: ‘liking’, learning,
and ‘wanting’. Does dopamine mostly mediate the hedonic impact
of reward (‘liking’)? Does it instead mediate learned predictions
of future reward, prediction error teaching signals and stamp in
associative links (learning)? Or does dopamine motivate the pursuit
of rewards by attributing incentive salience to reward-related stimuli
(‘wanting’)? Each hypothesis is evaluated here, and it is suggested
that the incentive salience or ‘wanting’ hypothesis of dopamine
function may be consistent with more evidence than either learning
or ‘liking’. In brief, recent evidence indicates that dopamine
is neither necessary nor sufficient to mediate changes in hedonic
‘liking’ for sensory pleasures. Other recent evidence indicates
that dopamine is not needed for new learning, and not sufficient
to directly mediate learning by causing teaching or prediction signals.
By contrast, growing evidence indicates that dopamine does contribute
causally to incentive salience. Dopamine appears necessary for normal
‘wanting’, and dopamine activation can be sufficient to enhance
cue-triggered incentive salience. Drugs of abuse that promote dopamine
signals short circuit and sensitize dynamic mesolimbic mechanisms
that evolved to attribute incentive salience to rewards. Such drugs
interact with incentive salience integrations of Pavlovian associative
information with physiological state signals. That interaction sets
the stage to cause compulsive ‘wanting’ in addiction, but also
provides opportunities for experiments to disentangle ‘wanting’,
‘liking’, and learning hypotheses. Results from studies that
exploited those opportunities are described here.},
owner = {fremaux},
timestamp = {2008.05.07},
url = {http://dx.doi.org/10.1007/s00213-006-0578-x}
}
@article{Berry98,
author = {M.J. Berry and M. Meister},
title = {Refractoriness and Neural Precision},
journal = {J. of Neuroscience},
year = {1998},
volume = {18},
pages = {2200-2211}
}
@article{Berry97,
author = {M. J. Berry and D. K. Warland and M. Meister},
title = {The structure and precision of retinal spike trains},
journal = {Proc. Nat. Ac. Sciences (USA)},
year = {1997},
volume = {94},
pages = {5411--5416},
annote = {neural code, time coding, precision, reliability, reproducibility,
retina, ganglion cells}
}
@article{Bertschinger04,
author = {N. Bertschinger and T. Natschl{\"a}ger},
title = {Real-time computation at the edge of chaos in recurrent neural networks},
journal = {Neural Computation},
year = {2004},
volume = {16},
pages = {1413-1436}
}
@book{Bertsekas87,
title = {Dynamic Programming: Deterministic and Stochastic Models},
publisher = {Prentice-Hall},
year = {1987},
author = {D.P. Bertsekas},
address = {Englewood cliffs, NJ}
}
@book{Bertsekas96,
title = {Neuro-Dynamic Programming},
publisher = {Athena Scientific},
year = {1996},
author = {D.P. Bertsekas and J.N. Tsitsiklis}
}
@article{Bethge01,
author = {M. Bethge and K. Pawelzik and R. Rothenstein and M. Tsodyks},
title = {Noise as a signal for neuronal populations},
journal = {Preprint - appeared in different form as Silberberg et al. 2004},
year = {2001},
volume = {xx},
pages = {xx}
}
@article{Bethge03,
author = {M. Bethge and D. Rotermund and K. Pawelzik},
title = {Optimal neural rate coding leads to bimodal firing rate distributions},
journal = {NETWORK-COMPUTATION IN NEURAL SYSTEMS },
year = {2003},
volume = {14},
pages = {303-319}
}
@article{Bethge02,
author = {M. Bethge and D. Rotermund and K. Pawelzik},
title = {Optimal short-term population coding: when {F}isher information fails},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {2317-2351}
}
@article{Bi02,
author = {G-Q. Bi},
title = {Spatiotemporal specificity of synaptic plasticity: cellular rules
and mechanisms},
journal = {Biological Cybernetics},
year = {2002},
volume = {319-332}
}
@article{Bi99,
author = {G.Q. Bi and M.M. Poo},
title = {Distributed synaptic modification in neural networks induced by patterned
stimulation},
journal = {Nature},
year = {1999},
volume = {401},
pages = {792-796}
}
@article{Bi98,
author = {G.Q. Bi and M.M. Poo},
title = {Synaptic modifications in cultured hippocampal neurons: dependence
on spike timing, synaptic strength, and postsynaptic cell type},
journal = {J. Neurosci.},
year = {1998},
volume = {18},
pages = {10464-10472}
}
@article{Bi98a,
author = {Guo{-}qiang Bi and Mu{-}ming Poo},
title = {Synaptic Modifications in Cultured Hippocampal Neurons: Dependence
on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type},
journal = {Journal of Neuroscience},
year = {1998},
volume = {18},
pages = {10464--10472},
number = {24},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Bi98b,
author = {Bi, G. and Poo, M.},
title = {{Synaptic Modifications in Cultured Hippocampal Neurons: Dependence
on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type}},
journal = {Journal of Neuroscience},
year = {1998},
volume = {18},
pages = {10464},
number = {24},
owner = {sprekeler},
publisher = {Soc Neuroscience},
timestamp = {2008.04.14}
}
@article{Bi01,
author = {{G.-q.} Bi and {M.-m.} Poo},
title = {Synaptic modification of correlated activity: Hebb's postulate revisited},
journal = {Ann. Rev. Neurosci.},
year = {2001},
volume = {24},
pages = {139-166}
}
@article{Bi02b,
author = {{G.-Q.} Bi and {H.-X.} Wang},
title = {Temporal asymmetry in spike timing-dependent synaptic plasticity},
journal = {Physiology and Behavior},
year = {2002},
volume = {77},
pages = {551-555}
}
@article{Bialek01,
author = {W. Bialek and I. Nemenman and N. Tishby},
title = {Predictability, Complexity and Learning},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {2409-2463},
number = {11},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Bialek92,
author = {William Bialek and Fred Rieke},
title = {Reliability and information transmission in spiking neurons},
journal = {Trends in Neurosciences},
year = {1992},
volume = {15},
pages = {428--433},
number = {11}
}
@article{Bialek91,
author = {W. Bialek and F. Rieke and R. R. de~Ruyter van Stevenick and D. Warland},
title = {Reading a neural code.},
journal = {Science},
year = {1991},
volume = {252},
pages = {1854--1857}
}
@article{Bienenstock82,
author = {E.L. Bienenstock and L.N. Cooper and P.W. Munroe },
title = {Theory of the development of neuron selectivity: orientation specificity
and binocular interaction in visual cortex},
journal = {Journal of Neuroscience},
year = {1982},
volume = {2},
pages = {32-48},
note = {reprinted in Anderson and Rosenfeld, 1990}
}
@article{Bienenstock95,
author = {E. Bienenstock and R. Doursat},
title = {The {\protect H}ebbian Development of synfire chains},
journal = {in preparation},
year = {1995}
}
@article{Bienenstock82a,
author = {Elie L. Bienenstock and Leon N. Cooper and Paul W. Munro},
title = {Theory for the Development of Neuron Selectivity: Orientation Specificity
and Binocular Interaction in Visual Cortex},
journal = {Journal of Neuroscience},
year = {1982},
volume = {2},
pages = {32--48},
number = {1},
keywords = {Plasticity, Vision-Models},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Rossum08,
author = {G. Billings and M. \protect{van Rossum} },
title = {Memory retention and Spike Timing dependent plasticity},
journal = {Preprint},
year = {2008},
volume = {xx},
pages = {xx}
}
@article{Billock97,
author = {V.A. Billock},
title = {Very short term visual memory via reverberation: A role for the cortico-thalamic
excitatory circuit in temporal filling-in during blinks and saccades?},
journal = {Vision Research},
year = {1997},
volume = {37},
pages = {949-953}
}
@inproceedings{Bindman91,
author = {L. Bindman and G. Christofi and K. Murphy and A. Nowicky},
title = {Long--term potentiation {(LTP)} and depression {(LTD)} in the neocortex
and hippocampus: an overview.},
booktitle = {Aspects of synaptic transmission.},
year = {1991},
editor = {T.W. Stone},
volume = {1},
pages = {3--25},
address = {London},
publisher = {Taylor Francis}
}
@book{Bishop95,
title = {Neural Networks for Pattern Recognition},
publisher = {Clarendon Press},
year = {1995},
author = {C. M. Bishop},
address = {Oxford}
}
@article{Blair95,
author = {H.T. Blair and P.E. Sharp},
title = {{Anticipatory head direction signals in anterior thalamus: Evidence
for a thalamocortical circuit that integrates angular head motion
to compute head direction}},
journal = {Journal of Neuroscience},
year = {1995},
volume = {15(9)},
pages = {6260--6270}
}
@book{Blakemore90,
title = {Vision: coding and efficiency},
publisher = {Cambridge University Press},
year = {1990},
author = {C. Blakemore},
address = {Cambridge}
}
@misc{Blakemore87,
author = {Blakemore, C. and Price, DJ},
title = {{The organization and post-natal development of area 18 of the cat's
visual cortex}},
year = {1987},
journal = {The Journal of Physiology},
keywords = {vision,vision-physiology},
number = {1},
owner = {sprekeler},
pages = {263--292},
publisher = {Physiological Soc},
timestamp = {2008.04.14},
volume = {384}
}
@phdthesis{Blaschke05,
author = {T. Blaschke},
title = {Independent Component Analysis and Slow Feature Analysis: Relations
and Combination},
school = {Humboldt-Universit\"{a}t zu Berlin},
year = {2005},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Blaschke06,
author = {Blaschke, Tobias and Berkes, Pietro and Wiskott, Laurenz},
title = {What is the relation between Slow Feature Analysis and Independent
Component Analysis?},
journal = {Neural Computation},
year = {2006},
volume = {18},
pages = {2495--2508},
number = {10},
keywords = {ICA, slowness},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Blaschke04,
author = {T. Blaschke and L. Wiskott},
title = {{CuBICA}: Independent Component Analysis by Simultaneous Third- and
Fourth-Order Cumulant Diagonalization},
journal = {IEEE Transactions on Signal Processing},
year = {2004},
volume = {52},
pages = {1250--1256},
number = {5},
month = may,
abstract = {CuBICA, an improved method for independent component analysis (ICA)
based on the diagonalization of cumulant tensors is proposed. It
is based on Comon's algorithm [Comon, 1994] but it takes
third- and fourth-order cumulant tensors into account simultaneously.
The underlying contrast function is also mathematically much simpler
and has a more intuitive interpretation. It is therefore easier to
optimize and approximate. A comparison with Comon's and three other
ICA-algorithms on different data sets demonstrates its performance.},
keywords = {ICA},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://itb.biologie.hu-berlin.de/~wiskott/Abstracts/BlasWisk2004a.html},
urlpaper = {http://itb.biologie.hu-berlin.de/~wiskott/Publications/BlasWisk2004a-CuBICA-IEEE-SP.pdf},
urlpaper2 = {http://itb.biologie.hu-berlin.de/~wiskott/Publications/BlasWisk2004a-CuBICA-IEEE-SP.ps.gz}
}
@article{Blaschke07,
author = {Blaschke, Tobias and Zito, Tiziano and Wiskott, Laurenz},
title = {Independent Slow Feature Analysis and Nonlinear Blind Source Separation},
journal = {Neural Computation},
year = {2007},
volume = {19},
pages = {994-1021},
number = {4},
keywords = {slowness,ICA},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Blasdel92,
author = {G. G. Blasdel},
title = {Orientations selectivity, preference, and continuity in monkey striate
cortex},
journal = {The Journal of Neuroscience},
year = {1992},
volume = {12},
pages = {3139-3161},
owner = {sprekeler},
timestamp = {2008.04.14}
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number = {3},
abstract = {We review different aspects of the simulation of spiking neural networks.
We start by reviewing the different types of simulation strategies
and algorithms that are currently implemented. We next review the
precision of those simulation strategies, in particular in cases
where plasticity depends on the exact timing of the spikes. We overview
different simulators and simulation environments presently available
(restricted to those freely available, open source and documented).
For each simulation tool, its advantages and pitfalls are reviewed,
with an aim to allow the reader to identify which simulator is appropriate
for a given task. Finally, we provide a series of benchmark simulations
of different types of networks of spiking neurons, including Hodgkin-Huxley
type, integrate-and-fire models, interacting with current-based or
conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different
simulators, and the codes are made available. The ultimate goal of
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appropriate integration strategy and simulation tool to use for a
given modeling problem related to spiking neural networks.},
address = {Ecole Normale Superieure, Paris, France.},
au = {Brette, R and Rudolph, M and Carnevale, T and Hines, M and Beeman,
D and Bower, JM and Diesmann, M and Morrison, A and Goodman, PH and
Harris FC, Jr and Zirpe, M and Natschlager, T and Pecevski, D and
Ermentrout, B and Djurfeldt, M and Lansner, A and Rochel, O and Vieville,
T and Muller, E and Davison, AP and El Boustani, S and Destexhe,
A},
bdsk-url-1 = {http://dx.doi.org/10.1007/s10827-007-0038-6},
da = {20071010},
date-added = {2008-03-28 21:38:23 +0100},
date-modified = {2008-03-28 21:38:28 +0100},
dcom = {20071226},
dep = {20070712},
doi = {10.1007/s10827-007-0038-6},
edat = {2007/07/17 09:00},
gr = {NS11613/NS/United States NINDS},
issn = {0929-5313 (Print)},
jid = {9439510},
jt = {Journal of computational neuroscience},
language = {eng},
mh = {Algorithms; Animals; Computer Simulation; Electrophysiology; Humans;
*Models, Neurological; Nerve Net/cytology/*physiology; Neurons/*physiology;
Software; Synapses/physiology},
mhda = {2007/12/27 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2006/11/29 {$[$}received{$]$}; 2007/04/12 {$[$}accepted{$]$}; 2007/04/02
{$[$}revised{$]$}; 2007/07/12 {$[$}aheadofprint{$]$}},
pl = {United States},
pmid = {17629781},
pst = {ppublish},
pt = {Journal Article; Research Support, N.I.H., Extramural; Research Support,
Non-U.S. Gov't; Review},
pubm = {Print-Electronic},
rf = {145},
sb = {IM},
so = {J Comput Neurosci. 2007 Dec;23(3):349-98. Epub 2007 Jul 12. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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triggering the action potential (AP) and returned to near zero values
at the moment of triggering. The fact that the current peak occurs
in advance of the AP may be partially explained by a phase delay
between the transmembrane current and potential. The failure of the
average current trajectory to return to control values immediately
following the peak argues for a positive role of the declining phase
in spike triggering. 7. Probability densities preceding spikes were
Gaussian, indicating that the average was also the most probable
value. Although the densities were broad, confirming that spikes
were preceded by a wide variety of current wave forms, their standard
deviations were reduced significantly with respect to controls, suggesting
preferred status of the average current trajectory in spike triggering.
8. The matrix of correlation coefficients between current pairs suggested
that spikes tended to be preceded by wave forms that in part kept
close to the average current trajectory and in part preserved its
shape. 9. The average first and second derivatives of spike-evoking
epochs revealed that current slope and acceleration, respectively,
were most crucial in the last 200 msec before spike triggering, and
that these dynamic stimulus components were more important for a
cell maintained under a depolarizing, rather than a hyperpolarizing
bias. 10...},
au = {Bryant, HL and Segundo, JP},
da = {19761223},
date-added = {2007-12-05 18:26:44 +0100},
date-modified = {2007-12-05 18:26:47 +0100},
dcom = {19761223},
edat = {1976/09/01},
issn = {0022-3751 (Print)},
jid = {0266262},
jt = {The Journal of physiology},
keywords = {*Action Potentials; Animals; Electric Stimulation; Electrophysiology;
Mathematics; *Membrane Potentials; Mollusca/*physiology; Neurons/*physiology;
Statistics as Topic},
language = {eng},
lr = {20071115},
mhda = {1976/09/01 00:01},
own = {NLM},
owner = {sprekeler},
pl = {ENGLAND},
pmid = {978519},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, U.S. Gov't, P.H.S.},
pubm = {Print},
sb = {IM},
so = {J Physiol. 1976 Sep;260(2):279-314. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Buchs02,
author = {N. Buchs and W. Senn},
title = {Spike-based synaptic plasticity and the emergence of diretion selective
simple cells: simulation results},
journal = {J. Computational Neuroscience},
year = {2002},
volume = {xx},
pages = {xx}
}
@article{Buck76,
author = {J. Buck and E. Buck},
title = {Synchronous fireflies},
journal = {Scientific American},
year = {1976},
volume = {234},
pages = {74-85}
}
@article{Bugmann97,
author = {G. Bugmann and C. Christodoulou and J. G. Taylor},
title = {Role of temporal integration and fluctuation detection in the highly
irregular firing of leaky integrator neuron model with partial reset},
journal = {Neural Computation},
year = {1997},
volume = {9},
pages = {985-1000}
}
@article{Buhmann89a,
author = {J. Buhmann},
title = {Oscillations and low firing rates in associative memory neural networks.},
journal = {Phys. Rev. A},
year = {1989},
volume = {40},
pages = {4145--4148}
}
@article{Buhmann89b,
author = {J Buhmann},
title = {Associative memory with high information content},
journal = {Phys.~Rev. A},
year = {1989},
volume = {39},
pages = {2689-2692}
}
@article{Buhmann87,
author = {J Buhmann and K Schulten},
title = {Noise-driven temporal association in neural networks},
journal = {Europhys.~Lett.},
year = {1987},
volume = {4},
pages = {1205-1209}
}
@article{Buhmann86,
author = {J. Buhmann and K. Schulten},
title = {Associative recognition and storage in a model network with physiological
neurons.},
journal = {Biol. Cybern.},
year = {1986},
volume = {54},
pages = {319--335}
}
@article{Bullier01,
author = {Bullier, J.},
title = {{Integrated model of visual processing}},
journal = {Brain Research Reviews},
year = {2001},
volume = {36},
pages = {96--107},
number = {2-3},
keywords = {vision, vision-physiology},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@article{Bulsara91,
author = {A. Bulsara and E. W. Jacobs and T. Zhou and F. Moss and L. Kiss},
title = {Stochastic Resonance in a Single Neuron Model: Theory and Analog
Simulation},
journal = {Journal of Theoretical Biology},
year = {1991},
volume = {152},
pages = {531--555}
}
@article{Bulsara96,
author = {A. R. Bulsara and T. C. Elston and C. R. Doering and S. B. Lowen
and K. Lindenberg},
title = {Cooperative behavior in periodically driven noisy integrate- fire
models of neuronal dynamics},
journal = {Physical Review E},
year = {1996},
volume = {53},
pages = {3958--3969}
}
@article{Buonomano98,
author = {D.V Buonomano and M.M. Merzenich},
title = {Cortical plasticity: From synapses to maps },
journal = {Annual Review of Neuroscience},
year = {1998},
volume = {21},
pages = {149-186 }
}
@article{Buonomano98a,
author = {D. V. Buonomano and M. M. Merzenich},
title = {Cortical plasticity: From synapses to maps},
journal = {Annual Reviews of Neuroscience},
year = {1998},
volume = {21},
pages = {149--186},
abstract = {It has been clear for almost two decades that cortical representations
in adult animals are not fixed entities, but rather, are dynamic
and are continuously modified by experience. The cortex can preferentially
allocate area to represent the particular peripheral input sources
that are proportionally most used. Alterations in cortical representations
appear to underlie learning tasks dependent on the use of the behaviorally
important peripheral inputs that they represent. The rules governing
this cortical representational plasticity following manipulations
of inputs, including learning, are increasingly well understood.
In parallel with developments in the field of cortical map plasticity,
studies of synaptic plasticity have characterized specific elementary
forms of plasticity, including associative long-term potentiation
and long-term depression of excitatory postsynaptic potentials. Investigators
have made many important strides toward understanding the molecular
underpinnings of these fundamental plasticity processes and toward
defining the learning rules that govern their induction. The fields
of cortical synaptic plasticity and cortical map plasticity have
been implicitly linked by the hypothesis that synaptic plasticity
underlies cortical map reorganization. Recent experimental and theoretical
work has provided increasingly stronger support for this hypothesis.
The goal of the current paper is to review the fields of both synaptic
and cortical map plasticity with an emphasis on the work that attempts
to unite both fields. A second objective is to highlight the gaps
in our understanding of synaptic and cellular mechanisms underlying
cortical representational plasticity.},
doi = {10.1146/annurev.neuro.21.1.149},
keywords = {Animals; Brain Mapping; Cerebral Cortex; Neuronal Plasticity; Synapses},
owner = {sprekeler},
pmid = {9530495},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1146/annurev.neuro.21.1.149}
}
@article{Buracas98a,
author = {G. T. Buracas and A. M. Zador and M. R. DeWeese and T. D. Albright},
title = {Efficient discrimination of temporal patterns by motion-sensitive
neurons in primate visual cortex.},
journal = {Neuron},
year = {1998},
volume = {20},
pages = {959--969},
number = {5},
month = {May},
abstract = {Although motion-sensitive neurons in macaque middle temporal (MT)
area are conventionally characterized using stimuli whose velocity
remains constant for 1-3 s, many ecologically relevant stimuli change
on a shorter time scale (30-300 ms). We compared neuronal responses
to conventional (constant-velocity) and time-varying stimuli in alert
primates. The responses to both stimulus ensembles were well described
as rate-modulated Poisson processes but with very high precision
(approximately 3 ms) modulation functions underlying the time-varying
responses. Information-theoretic analysis revealed that the responses
encoded only approximately 1 bit/s about constant-velocity stimuli
but up to 29 bits/s about the time-varying stimuli. Analysis of local
field potentials revealed that part of the residual response variability
arose from "noise" sources extrinsic to the neuron. Our results demonstrate
that extrastriate neurons in alert primates can encode the fine temporal
structure of visual stimuli.},
institution = {Howard Hughes Medical Institute and Sloan Center for Theoretical
Neurobiology, The Salk Institute for Biological Studies, La Jolla,
California 92037, USA.},
keywords = {Action Potentials; Algorithms; Analysis of Variance; Animals; Attention;
Discrimination Learning; Haplorhini; Information Theory; Motion Perception;
Neurons, Afferent; Nonlinear Dynamics; Photic Stimulation; Time Factors;
Time Perception; Visual Cortex},
owner = {gerstner},
pii = {S0896-6273(00)80477-8},
pmid = {9620700},
timestamp = {2008.06.04}
}
@incollection{Burgess07,
author = {N. Burgess},
title = {Computational Models of the Spatial and Mnemonic Functions of the
Hippocampus},
booktitle = {The hippocampus book},
publisher = {Oxford university press},
year = {2007},
pages = {715--749},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Burgess05,
author = {N. Burgess and F. Cacucci and C. Lever and J. O'keefe},
title = {{Characterizing multiple independent behavioral correlates of cell
firing in freely moving animals}},
journal = {Hippocampus},
year = {2005},
volume = {15},
pages = {149--153},
number = {2},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Burgess96,
author = {N. Burgess and J. O'Keefe},
title = {{Neuronal computations underlying the firing of place cells and their
role in navigation}},
journal = {Hippocampus},
year = {1996},
volume = {6},
pages = {749--762},
number = {6},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Burgess94a,
author = {N. Burgess and M. Recce and J. O'Keefe},
title = {A model of hippocampal function},
journal = {Neural Networks},
year = {1994},
volume = {6/7},
pages = {1065--1081},
number = {7},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Burgess94,
author = {N. Burgess and M. Recce and J. {O'K}eefe},
title = {A model of hippocampal function},
journal = {Neural Networks},
year = {1994},
volume = {7},
pages = {1065-1081}
}
@article{Burkitt06a,
author = {A. N. Burkitt},
title = {A review of the integrate-and-fire neuron model: I. Homogeneous synaptic
input },
journal = {Biol. Cybernetics},
year = {2006},
volume = {95},
pages = {1-19}
}
@article{Burkitt06b,
author = {A. N. Burkitt},
title = {A review of the integrate-and-fire neuron model: II. Inhomogeneous
synaptic input and network properties },
journal = {Biol. Cybernetics},
year = {2006},
volume = {95},
pages = {97-112}
}
@article{Burkitt01,
author = {A. N. Burkitt},
title = {Balanced Neurons: Analysis of leaky integrate-and-fire neurons with
reversal potential},
journal = {Biological Cybernetics},
year = {2001},
volume = {85},
pages = {247-255}
}
@article{Burkitt99,
author = {A. N. Burkitt and G. M. Clark},
title = {Analysis of integrate-and-fire neurons: synchronization of synaptic
input and spike output},
journal = {Neural Computation},
year = {1999},
volume = {11},
pages = {871-901}
}
@article{Burkitt04,
author = {A. N. Burkitt and M. H. Meffin and D.B. Grayden},
title = {Spike-timing-dependent plasticity: The relationship to rate-based
learning for models with weight dynamics determined by a stable fixed
point},
journal = {Neural Computation},
year = {2004},
volume = {16},
pages = {885-940}
}
@article{Burridge67,
author = {R. Burridge and L. Knopoff},
title = {xx},
journal = {Bull. Seism. Soc. Am.},
year = {1967},
volume = {57},
pages = {341}
}
@article{Burrone03,
author = {Burrone, J. and Murthy, V.N.},
title = {{Synaptic gain control and homeostasis}},
journal = {Curr Opin Neurobiol},
year = {2003},
volume = {13},
pages = {560--7},
number = {5},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Burrone02,
author = {Burrone, J. and O'Byrne, M. and Murthy, VN},
title = {{Multiple forms of synaptic plasticity triggered by selective suppression
of activity in individual neurons.}},
journal = {Nature},
year = {2002},
volume = {420},
pages = {414--8},
number = {6914},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Burwell03,
author = {R.D. Burwell and D.M. Hafeman},
title = {Positional firing properties of postrhinal cortex neurons},
journal = {Neuroscience},
year = {2003},
volume = {119},
pages = {577--588},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Buser92,
title = {Vision},
publisher = {MIT Press},
year = {1992},
author = {Pierre Buser and Michel Imbert},
address = {Cambridge}
}
@article{Bush91,
author = {P. C. Bush and R. J. Douglas},
title = {Synchronization of bursting action potential discharge in a model
network of neocortical neurons.},
journal = {Neural Computation},
year = {1991},
volume = {3},
pages = {19--30}
}
@article{Bush93,
author = {Bush, P C and Sejnowski, T J},
title = {Reduced compartmental models of neocortical pyramidal cells.},
journal = {J Neurosci Methods},
year = {1993},
volume = {46},
pages = {159--166},
number = {2},
abstract = {Model neurons composed of hundreds of compartments are currently used
for studying phenomena at the level of the single cell. Large network
simulations require a simplified model of a single neuron that retains
the electrotonic and synaptic integrative properties of the real
cell. We introduce a method for reducing the number of compartments
of neocortical pyramidal neuron models (from 400 to 8-9 compartments)
through a simple collapsing method based on conserving the axial
resistance rather than on the surface area of the dendritic tree.
The reduced models retain the general morphology of the pyramidal
cells on which they are based, allowing accurate positioning of synaptic
inputs and ionic conductances on individual model cells, as well
as construction of spatially accurate network models. The reduced
models run significantly faster than the full models, yet faithfully
reproduce their electrical responses.},
address = {Howard Hughes Medical Institute, La Jolla, CA.},
au = {Bush, PC and Sejnowski, TJ},
da = {19930518},
date-added = {2008-03-28 23:23:30 +0100},
date-modified = {2008-03-28 23:23:46 +0100},
dcom = {19930518},
edat = {1993/02/01},
issn = {0165-0270 (Print)},
jid = {7905558},
jt = {Journal of neuroscience methods},
language = {eng},
lr = {20031114},
mh = {Action Potentials/physiology; Animals; Cats; Cerebral Cortex/cytology/*physiology;
Dendrites/physiology; Electrophysiology; Horseradish Peroxidase;
Models, Neurological; Neurons/*physiology; Pyramidal Tracts/cytology/*physiology;
Synapses/physiology},
mhda = {1993/02/01 00:01},
own = {NLM},
owner = {sprekeler},
pii = {0165-0270(93)90151-G},
pl = {NETHERLANDS},
pmid = {8474259},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
rn = {EC 1.11.1.- (Horseradish Peroxidase)},
sb = {IM},
so = {J Neurosci Methods. 1993 Feb;46(2):159-66. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Butts07,
author = {Daniel A. Butts and Patrick O. Kanold and Carla J. Shatz},
title = {A burst-based {H}ebbian learning rule at retinogeniculate synapses
links retinal waves to activity-dependent refinement},
journal = {PLoS Biology},
year = {2007},
volume = {5},
pages = {e61 EP -},
number = {3},
month = mar,
keywords = {plasticity, Vision, Vision-Models},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1371\%2Fjournal.pbio.0050061}
}
@article{Buzs'aki04,
author = {G. Buzs{\'a}ki},
title = {Large-scale recording of neuronal ensembles.},
journal = {Nature Neuroscience},
year = {2004},
volume = {7},
pages = {446--451},
number = {5},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Byrne89,
title = {Neural Models of Plasticity},
publisher = {Academic Press},
year = {1989},
author = {J. H. Byrne and W. O. Berry},
address = {San Diego}
}
@article{Cacucci04,
author = {F. Cacucci and C. Lever and T.J. Wills and N. Burgess and J. O'Keefe},
title = {Theta-Modulated Place-by-Direction Cells in the Hippocampal Formation
in the Rat},
journal = {Journal of Neuroscience},
year = {2004},
volume = {24},
pages = {8265--8277},
number = {38},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Cai04,
author = {D. Cai and L. Tao and D.W. McLaughlin},
title = {An embedded network approach for scale-up of fluctuation-driven systems
with preservation of spike information},
journal = {Proc. National Academy Sciences (USA)},
year = {2004},
volume = {101},
pages = {14288-14293 }
}
@article{Cai04a,
author = {D. Cai and L. Tao and M. Shelley and D.W. McLaughlin},
title = {An effective kinetic representation of fluctuation-driven neuronal
networks with application to simple and complex cells in visual cortex},
journal = {Proc. National Academy Sciences (USA)},
year = {2004},
volume = {101},
pages = {7757-7762}
}
@article{Calton05,
author = {J. L. Calton and J. S. Taube},
title = {{Degradation of head direction cell activity during inverted locomotion}},
journal = {Journal of Neuroscience},
year = {2005},
volume = {25},
pages = {2420--2428},
number = {9},
month = mar,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Calvin68,
author = {W. Calvin and C.F. Stevens},
title = {Synaptic noise and other sources of randomness in motoneuron interspike
intervals},
journal = {J. Neurophysiology},
year = {1968},
volume = {31},
pages = {574-587}
}
@article{Cang05,
author = {Cang, J. and Renteria, R.C. and Kaneko, M. and Liu, X. and Copenhagen,
D.R. and Stryker, M.P.},
title = {Development of Precise Maps in Visual Cortex Requires Patterned Spontaneous
Activity in the Retina},
journal = {Neuron},
year = {2005},
volume = {48},
pages = {797--809},
number = {5},
keywords = {Vision, plasticity},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@article{Capocelli71,
author = {R. M. Capocelli and L. M. Ricciardi},
title = {Diffusion approximation and first passage time problem for a neuron
model},
journal = {Kybernetik},
year = {1971},
volume = {8},
pages = {214-223}
}
@article{Carandini05,
author = {Matteo Carandini and Jonathan B. Demb and Valerio Mante and David
J. Tolhurst and Yang Dan and Bruno A. Olshausen and Jack L. Gallant
and Nicole C. Rust},
title = {Do We Know What The Early Visual System Does?},
journal = {Journal of Neuroscience},
year = {2005},
volume = {25},
pages = {10577--10597},
number = {46},
keywords = {vision, Vision-Models},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Carandini05a,
author = {M. Carandini and J. B. Demb and V. Mante and D. J. Tolhurst and Y.
Dan and B. A. Olshausen and J. L. Gallant and N. C. Rust},
title = {Do we know what the early visual system does?},
journal = {The Journal of Neuroscience},
year = {2005},
volume = {25},
pages = {10577-97},
number = {46},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Carandini99,
author = {Carandini, M. and Heeger, D.J. and Movshon, J.A.},
title = {{Linearity and gain control in V1 simple cells}},
journal = {Cerebral cortex},
year = {1999},
volume = {13},
pages = {401--443},
keywords = {vision, vision-models},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Carandini97,
author = {M. Carandini and D. L. Ringach},
title = {Predictions of a recurrent model of orientation selectivity},
journal = {Vision Research},
year = {1997},
volume = {37},
pages = {3061-3071},
number = {21},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Carpenter97,
author = {G. Carpenter},
title = {Distributed learning, recognition and prediction by \protect{ART}
and \protect{ARTMAP} neural networks },
journal = {Neural Networs},
year = {1997},
volume = {10},
pages = {1473-1494}
}
@article{Carpenter90,
author = {G. Carpenter and S. Grossberg},
title = {\protect{ART} 3: Hierarchical search using chemical transmitters
in self-organizing pattern recognition},
journal = {Neural Networks},
year = {1990},
volume = {3},
pages = {129-152}
}
@article{Carpenter87,
author = {G. Carpenter and S. Grossberg},
title = {ART 2: Self-organization of stable category recognition codes for
analog input patterns},
journal = {Applied Optics},
year = {1987},
volume = {26},
pages = {4919-4930}
}
@inproceedings{Carr95,
author = {C. E. Carr},
title = {The {D}evelopment of {N}ucleus {L}aminaris in the {B}arn {O}wl},
booktitle = {Advances in Hearing Research},
year = {1995},
editor = {G. A. Manley and G. M. Klump and C. K{\"o}ppl and H. Fastl and H.
Oeckinghaus},
pages = {24-30},
address = {Singapure},
publisher = {World Scientific}
}
@article{Carr93,
author = {Catherine E. Carr},
title = {Processing of Temporal Information in the Brain},
journal = {Annual Rev. Neurosci.},
year = {1993},
volume = {16},
pages = {223--43},
comment = {Sehr didaktisch aufgebauter Review-Artikel, der eine gute Uebersicht
ueber das Gebiet gibt.},
keywords = {phase, time, delay, sound localization, owls, bats, electric fish.}
}
@article{Carr91,
author = {C. E. Carr and R. E. Boudreau},
title = {Central Projections of Auditory Nerve Fibers in the Barn Owl},
journal = {J. Comp. Neurol.},
year = {1991},
volume = {314},
pages = {306--318}
}
@article{Carr90,
author = {C. E. Carr and M. Konishi},
title = {A circuit for detection of interaural time differences in the brain
stem of the barn owl},
journal = {J. Neurosci.},
year = {1990},
volume = {10},
pages = {3227-3246}
}
@article{Carr88,
author = {Catherine E. Carr and Masakazu Konishi},
title = {Axonal delay lines for measurement in the owl's brainstem},
journal = {Proc. Natl. Acad. Sci USA},
year = {1988},
volume = {85},
pages = {8311--8315}
}
@article{Cash98a,
author = {Cash, S and Yuste, R},
title = {Input summation by cultured pyramidal neurons is linear and position-independent.},
journal = {J Neurosci},
year = {1998},
volume = {18},
pages = {10--15},
number = {1},
abstract = {The role of dendritic morphology in integration and processing of
neuronal inputs is still unknown. Models based on passive cable theory
suggest that dendrites serve to isolate synapses from one another.
Because of decreases in driving force or resistance, two inputs onto
the same dendrite would diminish their joint effect, resulting in
sublinear summation. When on different dendrites, however, inputs
would not interact and therefore would sum linearly. These predictions
have not been rigorously tested experimentally. In addition, recent
results indicate that dendrites have voltage-sensitive conductances
and are not passive cables. To investigate input integration, we
characterized the effects of dendritic morphology on the summation
of subthreshold excitatory inputs on cultured hippocampal neurons
with pyramidal morphologies. We used microiontophoresis of glutamate
to systematically position inputs throughout the dendritic tree and
tested the summation of two inputs by measuring their individual
and joint effects. We find that summation was surprisingly linear
regardless of input position. For small inputs, this linearity arose
because no significant shunts or changes in driving force occurred
and no voltage-dependent channels were opened. Larger inputs also
added linearly, but this linearity was caused by balanced action
of NMDA and IA potassium conductances. Therefore, active conductances
can maintain, paradoxically, a linear input arithmetic. Furthermore,
dendritic morphology does not interfere with this linearity, which
may be essential for particular neuronal computations.},
address = {Department of Biological Sciences, Columbia University, New York,
New York 10027, USA.},
au = {Cash, S and Yuste, R},
da = {19980116},
date-added = {2008-03-28 23:31:06 +0100},
date-modified = {2008-03-28 23:31:09 +0100},
dcom = {19980116},
edat = {1998/01/24},
issn = {0270-6474 (Print)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20061115},
mh = {2-Amino-5-phosphonovalerate/pharmacology; Action Potentials/drug effects/physiology;
Animals; Animals, Newborn; Cells, Cultured; Dendrites/chemistry/*physiology;
Electric Conductivity; Excitatory Amino Acid Antagonists/pharmacology;
Excitatory Postsynaptic Potentials/physiology; Hippocampus/cytology;
Iontophoresis; Linear Models; Nickel/pharmacology; Potassium Channels/physiology;
Pyramidal Cells/*cytology/*physiology/ultrastructure; Rats; Rats,
Sprague-Dawley; Receptors, N-Methyl-D-Aspartate/physiology; Tetraethylammonium/pharmacology;
Tetrodotoxin/pharmacology},
mhda = {1998/01/24 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {9412481},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
rn = {0 (Excitatory Amino Acid Antagonists); 0 (Potassium Channels); 0 (Receptors,
N-Methyl-D-Aspartate); 4368-28-9 (Tetrodotoxin); 66-40-0 (Tetraethylammonium);
7440-02-0 (Nickel); 76726-92-6 (2-Amino-5-phosphonovalerate); 7718-54-9
(nickel chloride)},
sb = {IM},
so = {J Neurosci. 1998 Jan 1;18(1):10-5. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Cash98,
author = {S. Cash and R. Yuste},
title = {Input summation by cultured pyramidal neurons is linear and position-independent},
journal = {J. Neuroscience},
year = {1988},
volume = {18},
pages = {10-15}
}
@article{Castellani01,
author = {G.C. Castellani and E.M. Quinlan and L.N. Shouval and H.Z. Cooper},
title = {A biophysical model of bidirectional synaptic plasticity: dependence
on AMPA and NMDA receptors},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2001},
volume = {98},
pages = {12772-12777}
}
@article{Cessac94,
author = {B. Cessac and B. Doyon and M. Quoy and M. Samuleides},
title = {Mean-field equations, bifurcation map and route to chaos in discrete
time neural networks},
journal = {Phyisca D},
year = {1994},
volume = {74},
pages = {24-44}
}
@article{Chacron04,
author = {M. J. Chacron and B. Lindner and A. Longtin},
title = {Noise shaping by interval correlations increases information transfer},
journal = {Phys. Rev. Letters},
year = {2004},
volume = {92},
pages = {80601}
}
@book{Chance00,
title = {Modeling Cortical Dynamics and the Responses of Neurons in the Primary
Visual Cortex.},
publisher = {Brandeis University},
year = {2000},
author = {F.S. Chance},
series = {PhD dissertaion}
}
@article{Chance01a,
author = {F. S. Chance and S. {du Lac} and L. F. Abbott },
title = {An integrate-and-fire model of spike-rate dynamics},
journal = {Society of Neuroscience Abstracts},
year = {2001},
volume = {27},
pages = {821.44}
}
@article{Chance99,
author = {Chance, F S and Nelson, S B and Abbott, L F},
title = {{{C}omplex cells as cortically amplified simple cells}},
journal = {Nat Neurosci},
year = {1999},
volume = {2},
pages = {277--282},
number = {3},
month = {Mar},
keywords = {Vision, Vision-Models},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Chateau03,
author = {H. Chateau and T. Fukai},
title = {A stochastic model to predict the consequences of arbitrary forms
of spike-timing dependent plasticity},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {597-620}
}
@article{Chavarriaga03,
author = {R. Chavarriaga and E. Sauser and W. Gerstner},
title = {Modelling directional firing properties of place cells},
journal = {CNS Meeting 2003},
year = {2003},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Chavarriaga05,
author = {Ricardo Chavarriaga and Thomas Str{\"o}sslin and Denis Sheynikhovich and Wulfram Gerstner},
title = {Competition between cue response and place response: A model of rat navigation behaviour},
journal = {Connection Science},
year = {2005},
volume = {17},
pages = {167-183}
}
@article{Chavarriaga05a,
author = {Ricardo Chavarriaga and Thomas Str\"osslin and Denis Sheynikhovich
and Wulfram Gerstner},
title = { A computational model of parallel navigation systems in rodents},
journal = {Neuroinformatics},
year = {2005},
volume = {3},
pages = {223-241}
}
@article{Chechik03,
author = {G. Chechik},
title = {Spike-Timing-Dependent Plasticity and Relevant Mututal Information
Maximization},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {1481-1510}
}
@techreport{Chechik03b,
author = {G. Chechik and A. Globerson},
title = {Information Bottleneck and Linear Projections of Gaussian Processes.},
institution = {Hebrew University },
year = {2003},
number = {4},
month = {May},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Chechik03a,
author = {G. Chechik and A. Globerson and N. Tishby and Y. Weiss},
title = {Information Bottleneck for {G}aussian Variables},
booktitle = {Advances in Neural Information Processing Systems 15 },
year = {2003},
editor = {Sebastian Thrun and Lawrence Saul and Bernhard {Sch\"{o}lkopf}},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Chechik05,
author = {G. Chechik and A. Globerson and N. Tishby and Y. Weiss},
title = {Information Bottleneck for {G}aussian Variables},
journal = {The Journal of Machine Learning Research},
year = {2005},
volume = {6},
pages = {165-188},
keywords = {Optimal Coding},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Chen94a,
author = {L.L. Chen and L.H. Lin and C.A. Barnes and B.L. McNaughton},
title = {Head-direction cells in the rat posterior cortex. {II.} {C}ontributions
of visual and ideothetic information to the directional firing},
journal = {Experimental Brain Research},
year = {1994},
volume = {101},
pages = {24--34},
number = {1},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Chen94,
author = {L.L. Chen and L.H. Lin and E.J. Green and C.A. Barnes and B.L. McNaughton},
title = {Head-direction cells in the rat posterior cortex. {I.} {A}natomical
distribution and behavioral modulation},
journal = {Experimental Brain Research},
year = {1994},
volume = {101},
pages = {8--23},
number = {1},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Chen07,
author = {Xiaodong Chen and Feng Han and Mu-Ming Poo and Yang Dan},
title = {Excitatory and suppressive receptive field subunits in awake monkey
primary visual cortex (V1).},
journal = {Proceedings of the National Academy of Sciences},
year = {2007},
volume = {104},
pages = {19120--19125},
number = {48},
month = {Nov},
abstract = {An essential step in understanding visual processing is to characterize
the neuronal receptive fields (RFs) at each stage of the visual pathway.
However, RF characterization beyond simple cells in the primary visual
cortex (V1) remains a major challenge. Recent application of spike-triggered
covariance (STC) analysis has greatly facilitated characterization
of complex cell RFs in anesthetized animals. Here we apply STC to
RF characterization in awake monkey V1. We found up to nine subunits
for each cell, including one or two dominant excitatory subunits
as described by the standard model, along with additional excitatory
and suppressive subunits with weaker contributions. Compared with
the dominant subunits, the nondominant excitatory subunits prefer
similar orientations and spatial frequencies but have larger spatial
envelopes. They contribute to response invariance to small changes
in stimulus orientation, position, and spatial frequency. In contrast,
the suppressive subunits are tuned to orientations 45 degrees -90
degrees different from the excitatory subunits, which may underlie
cross-orientation suppression. Together, the excitatory and suppressive
subunits form a compact description of RFs in awake monkey V1, allowing
prediction of the responses to arbitrary visual stimuli.},
doi = {10.1073/pnas.0706938104},
keywords = {Vision},
owner = {sprekeler},
pii = {0706938104},
pmid = {18006658},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1073/pnas.0706938104}
}
@article{Chichilnisky01,
author = {E. J. Chichilnisky},
title = {A simple white noise analysis of neuronal light responses},
journal = {Network},
year = {2001},
volume = {12},
number = {199-213}
}
@article{Choi88,
author = {M Y Choi},
title = {Dynamic model of neural networks},
journal = {Phys.~Rev.~Lett.},
year = {1988},
volume = { 61},
pages = {2809-2812}
}
@article{Chow98,
author = {C. C. Chow},
title = {Phase-locking in weakly heterogeneous neuronal networks},
journal = {Physica D},
year = {1998},
volume = {118},
pages = {343-370}
}
@article{Chow00,
author = {C. C. Chow and N. Kopell},
title = {Dynamics of spiking neurons with electrical couplings},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {1643-1678}
}
@article{Chow96,
author = {C. C. Chow and J. White},
title = {Spontaneous action potential fluctuations due to channel fluctuations},
journal = {Bioph. J.},
year = {1996},
volume = {71},
pages = {3013-3021}
}
@book{Churchland92,
title = {The computational brain},
publisher = {MIT Press},
year = {1992},
author = {P. S. Chruchland and T. J. Sejnowski},
address = {Cambridge}
}
@article{Chubb02,
author = {C. Chubb and J. Talevich},
title = {{Attentional control of texture orientation judgments}},
journal = {Vision Research},
year = {2002},
volume = {42},
pages = {311--330},
number = {3},
month = feb,
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Cichocki93,
title = {Neural Networks for Optimization and Signal Processing},
publisher = {John Wiley},
year = {1993},
author = {A. Cichocki and R. Unbehauen},
address = {Chichester}
}
@article{Clay73,
author = {Clay and Goel},
title = {Diffusion model for firing of a neuron with varying threshold},
journal = {J. theor. Biol},
volume = {39},
pages = {633-644},
key = {1973}
}
@article{Clifford02,
author = {C. W. G. Clifford},
title = {Perceptual adaptation: motion parallels orientation},
journal = {TRENDS in Cognitive Sciences},
year = {2002},
volume = {6},
pages = {136-142},
number = {3},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Clopath07,
author = {C. Clopath and R. Jolivet and A. Rauch and H.-R. Luescher and W.
Gerstner},
title = { Predicting Neuronal Activity with Simple Models of the Threshold
Type: Adaptive Exponential Integrate-and-Fire Model with Two Compartments},
journal = {Neurocomputing},
year = {2007},
volume = {xx},
pages = {xx},
note = {http://www.sciencedirect.com/ ONLINE since Oct. 2006}
}
@book{Cohen88,
title = {Neural Control of Rhythmic Movement in Vertebrates},
publisher = {John Wiley},
year = {1988},
author = {A. H. Cohen and S. Rossignol and S. Grillner},
address = {New York}
}
@article{Cohen83,
author = {M. A. Cohen and S. Grossberg},
title = {Absolute stability of global pattern formation and parallel memory
storage by competitive neural networks},
journal = {IEEE trans. on systems, man, and cybernetics},
year = {1983},
volume = {13},
pages = {815-823}
}
@article{Colburn90,
author = {H. Steven Colburn and Yan--an Han and Carrin Passaro Culotta},
title = {Coincidence model of {MSO} responses},
journal = {Hearing research},
year = {1990},
volume = {49},
pages = {335--3346}
}
@article{Collett92,
author = {T.S. Collett and E. Dillman and A. Giger and R. Wehner},
title = {{Visual landmarks and route following in desert ants}},
journal = {Journal of Comparative Physiology},
year = {1992},
volume = {170},
pages = {435--442}
}
@article{Collingridge94,
author = {Graham L. Collingridge},
title = {A question of reliability},
journal = {Nature},
year = {1994},
volume = {371},
pages = {652--653}
}
@article{Collingridge83,
author = {G. L. Collingridge and S. J. Kehl and H. McLennan},
title = {Excitatory amino acids in synaptic transmission in the Schaffer collateral-commissural
pathway of the rat hippocampus},
journal = {J. Physiol.},
year = {1983},
volume = {334},
pages = {33-46}
}
@article{Collins96,
author = {J.J. Collins and C.C. Chow and A.C. Capela and T.T. Imhoff},
title = {Aperiodic stochastic resonance},
journal = {Physical Review E},
year = {1996},
volume = {54},
pages = {5575-5584}
}
@article{Connor77,
author = {J.A. Connor and D. Walter and R. Mc{K}own},
title = {Neural repetitive firing - modifications of the Hodgkin-Huxley axon
suggested by experimental results from crustacean axons},
journal = {Biophysical Journal},
year = {1977},
volume = {18},
pages = {81-102}
}
@article{Connors90,
author = {B. W. Connors and M. J. Gutnick},
title = {Intrinsic firing patterns of diverse cortical neurons},
journal = {Trends in Neurosci.},
year = {1990},
volume = {13},
pages = {99-104}
}
@article{Connors90a,
author = {Connors, B W and Gutnick, M J},
title = {Intrinsic firing patterns of diverse neocortical neurons.},
journal = {Trends Neurosci},
year = {1990},
volume = {13},
pages = {99--104},
number = {3},
abstract = {Neurons of the neocortex differ dramatically in the patterns of action
potentials they generate in response to current steps. Regular-spiking
cells adapt strongly during maintained stimuli, whereas fast-spiking
cells can sustain very high firing frequencies with little or no
adaptation. Intrinsically bursting cells generate clusters of spikes
(bursts), either singly or repetitively. These physiological distinctions
have morphological correlates. RS and IB cells can be either pyramidal
neurons or spiny stellate cells, and thus constitute the excitatory
cells of the cortex. FS cells are smooth or sparsely spiny non-pyramidal
cells, and are likely to be GABAergic inhibitory interneurons. The
different firing properties of neurons in neocortex contribute significantly
to its network behavior.},
address = {Division of Biology and Medicine, Brown University, Providence, RI
02912.},
au = {Connors, BW and Gutnick, MJ},
cin = {Trends Neurosci. 1990 Sep;13(9):365-6. PMID: 1699324},
da = {19900529},
date-added = {2008-03-29 18:14:06 +0100},
date-modified = {2008-03-29 18:14:14 +0100},
dcom = {19900529},
edat = {1990/03/01},
issn = {0166-2236 (Print)},
jid = {7808616},
jt = {Trends in neurosciences},
language = {eng},
lr = {20061115},
mh = {Animals; Cerebral Cortex/*physiology; Evoked Potentials; Neural Inhibition},
mhda = {1990/03/01 00:01},
own = {NLM},
owner = {sprekeler},
pii = {0166-2236(90)90185-D},
pl = {ENGLAND},
pmid = {1691879},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.; Review},
pubm = {Print},
rf = {28},
sb = {IM},
so = {Trends Neurosci. 1990 Mar;13(3):99-104. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Contreras99,
author = {J. Contreras-Vidal and W. Schultz},
title = {A predictive reinforcement model of dopamine neurons for learning
approach behavior},
journal = {J. Computational Neuroscience},
year = {1999},
volume = {6},
pages = {191-214}
}
@article{Cook03,
author = {D.L. Cook and P.C. Schwindt and L.A. Grande and W.J. Spain},
title = {Synaptic depression in the localization of sound},
journal = {Nature},
year = {2003},
volume = {421},
pages = {66-70}
}
@article{Cook07,
author = {Cook, Erik P and Guest, Jennifer A and Liang, Yong and Masse, Nicolas
Y and Colbert, Costa M},
title = {Dendrite-to-soma input/output function of continuous time-varying
signals in hippocampal CA1 pyramidal neurons.},
journal = {J Neurophysiol},
year = {2007},
volume = {98},
pages = {2943--2955},
number = {5},
abstract = {We examined how hippocamal CA1 neurons process complex time-varying
inputs that dendrites are likely to receive in vivo. We propose a
functional model of the dendrite-to-soma input/output relationship
that combines temporal integration and static-gain control mechanisms.
Using simultaneous dual whole cell recordings, we injected 50 s of
subthreshold and suprathreshold zero-mean white-noise current into
the primary dendritic trunk along the proximal 2/3 of stratum radiatum
and measured the membrane potential at the soma. Applying a nonlinear
system-identification analysis, we found that a cascade of a linear
filter followed by an adapting static-gain term fully accounted for
the nonspiking input/output relationship between the dendrite and
soma. The estimated filters contained a prominent band-pass region
in the 1- to 10-Hz frequency range that remained constant as a function
of stimulus variance. The gain of the dendrite-to-soma input/output
relationship, in contrast, varied as a function of stimulus variance.
When the contribution of the voltage-dependent current I(h) was eliminated,
the estimated filters lost their band-pass properties and the gain
regulation was substantially altered. Our findings suggest that the
dendrite-to-soma input/output relationship for proximal apical inputs
to CA1 pyramidal neurons is well described as a band-pass filter
in the theta frequency range followed by a gain-control nonlinearity
that dynamically adapts to the statistics of the input signal.},
address = {Department of Physiology, McGill University, 3655 Sir William Osler,
Montreal, QC H3G 1Y6, Canada. erik.cook@mcgill.ca},
au = {Cook, EP and Guest, JA and Liang, Y and Masse, NY and Colbert, CM},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00414.2007},
da = {20071116},
date-added = {2008-03-28 17:03:38 +0100},
date-modified = {2008-03-28 17:03:39 +0100},
dcom = {20080225},
dep = {20070919},
doi = {10.1152/jn.00414.2007},
edat = {2007/09/21 09:00},
issn = {0022-3077 (Print)},
jid = {0375404},
jt = {Journal of neurophysiology},
language = {eng},
mh = {Action Potentials/physiology/radiation effects; Animals; Axons/*physiology;
Dendrites/*physiology; Dose-Response Relationship, Radiation; Electric
Stimulation/methods; Hippocampus/*cytology; Male; Models, Neurological;
Patch-Clamp Techniques; Pyramidal Cells/*cytology; Rats; Rats, Sprague-Dawley;
Time Factors},
mhda = {2008/02/26 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2007/09/19 {$[$}aheadofprint{$]$}},
pii = {00414.2007},
pl = {United States},
pmid = {17881486},
pst = {ppublish},
pt = {In Vitro; Journal Article},
pubm = {Print-Electronic},
sb = {IM},
so = {J Neurophysiol. 2007 Nov;98(5):2943-55. Epub 2007 Sep 19. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Coombes01,
author = {S. Coombes and M. R. Owen and G. D. Smith},
title = {Mode locking in a periodically forced integrate-and-fire-or-burst
neuron model.},
journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
year = {2001},
volume = {64},
pages = {041914},
number = {4 Pt 1},
month = {Oct},
abstract = {The minimal "integrate-and-fire-or-burst" (IFB) neuron model reproduces
the salient features of experimentally observed thalamocortical relay
neuron response properties, including the temporal tuning of both
tonic spiking (i.e., conventional action potentials) and post-inhibitory
rebound bursting mediated by the low-threshold Ca2+ current, I(T).
In previous work focusing on experimental and IFB model responses
to sinusoidal current injection, large regions of stimulus parameter
space were observed for which the response was entrained to periodic
applied current, resulting in repetitive burst, tonic, or mixed (i.e.,
burst followed by tonic) responses. Here we present an exact analysis
of such mode-locking in the integrate-and-fire-or-burst model under
the influence of arbitrary periodic forcing that includes sinusoidally
driven responses as one case. In this analysis, the instabilities
of mode-locked states are identified as both smooth bifurcations
of an associated firing time map and nonsmooth bifurcations of the
underlying discontinuous flow. The explicit construction of borders
in parameter space that define the instabilities of mode-locked zones
is used to build up the Arnol'd tongue structure for the model. The
zones for mode-locking are shown to be in excellent agreement with
numerical simulations and are used to explore the observed stimulus
dependence of burst versus tonic response of the IFB neuron model.},
institution = {Department of Mathematical Sciences, Loughborough University, Leicestershire
LE11 3TU, United Kingdom.},
keywords = {Action Potentials; Animals; Calcium; Humans; Models, Neurological;
Models, Statistical; Models, Theoretical; Neurons; Oscillometry;
Synaptic Transmission; Time Factors},
owner = {gerstner},
pmid = {11690059},
timestamp = {2008.05.28}
}
@book{Cooper04,
title = {Theory of cortical plasticity.},
publisher = {World Scientific},
year = {2004},
author = {L.N. Cooper and N. Intrator and B.S. Blais and H. Z. Shouval},
address = {Singapore}
}
@article{Cordo96,
author = {P. Cordo and J.T. Inglis and S. Verschueren and J.J. Collins andD.
M. Merfeld and S. Rosenblum},
title = {Noise in human muscle spindels},
journal = {Nature},
year = {1996},
volume = {383},
pages = {769-770}
}
@article{Corral95,
author = {A. Corral and C. P. Perez and A. Diaz-Guilera and A. Arenas},
title = {Self-organized criticality and synchronization in a lattice model
of integrate-and-fire oscillators},
journal = {Phys. Rev. Lett.},
year = {1995},
volume = {74},
pages = {118-121}
}
@article{Correia08,
author = {Susana S Correia and Silvia Bassani and Tyler C Brown and Marie-France
Lisé and Donald S Backos and Alaa El-Husseini and Maria Passafaro
and José A Esteban},
title = {Motor protein-dependent transport of AMPA receptors into spines during
long-term potentiation.},
journal = {Nature Neuroscience},
year = {2008},
volume = {11},
pages = {457--466},
number = {4},
month = {Apr},
abstract = {The regulated trafficking of neurotransmitter receptors at synapses
is critical for synaptic function and plasticity. However, the molecular
machinery that controls active transport of receptors into synapses
is largely unknown. We found that, in rat hippocampus, the insertion
of AMPA receptors (AMPARs) into spines during synaptic plasticity
requires a specific motor protein, which we identified as myosin
Va. We found that myosin Va associates with AMPARs through its cargo
binding domain. This interaction was enhanced by active, GTP-bound
Rab11, which is also transported by the motor protein. Myosin Va
mediated the CaMKII-triggered translocation of GluR1 receptors from
the dendritic shaft into spines, but it was not required for constitutive
GluR2 trafficking. Accordingly, myosin Va was specifically required
for long-term potentiation, but not for basal synaptic transmission.
In summary, we identified the specific motor protein and organelle
acceptor that catalyze the directional transport of AMPARs into spines
during activity-dependent synaptic plasticity.},
doi = {10.1038/nn2063},
keywords = {plasticity},
owner = {sprekeler},
pii = {nn2063},
pmid = {18311135},
timestamp = {2008.05.08},
url = {http://dx.doi.org/10.1038/nn2063}
}
@book{Courant89,
title = {{Methods of mathematical physics Part I}},
publisher = {Wiley},
year = {1989},
author = {Courant, R. and Hilbert, D.},
keywords = {various-artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Cover91,
title = {Elements of Information Theory},
publisher = {Wiley},
year = {1991},
author = {T.M. Cover and J.A. Thomas},
address = {New York}
}
@article{Covey91,
author = {Ellen Covey and John H. Casseday},
title = {The Monaural Nuclei of the Lateral Lemniscus in an Echolocating Bat:
Parallel Pathways for Analyzing Temporal Features of Sound},
journal = {The Journal of Neuroscience},
year = {1991},
volume = {11},
pages = {3456--3470},
number = {11}
}
@incollection{Cowan68,
author = {J.D. Cowan},
title = {Statistical mechanics of nervous nets},
booktitle = {Proceedings of the 1967 NATO Conference on Neural Networks},
publisher = {Springer, Berlin},
year = {1968},
editor = {E. R. Caianiello},
pages = {xx}
}
@book{NIPS94,
title = {Advances in Neural Information Processing Systems},
publisher = {Morgan Kaufmann Publishers},
year = {1994},
author = {J. Cowan},
volume = {6},
address = {San Mateo}
}
@incollection{Cowan71,
author = {J. D. Cowan},
title = {A statistical mechancis of nervous activity},
booktitle = {Proceedings of 2nd American Mathematical Society Symposium on Mathematical
Questions in Biology},
publisher = {Amer. Math. Society},
year = {1971},
editor = {M. Gerstenhaber},
pages = {1-57}
}
@article{Cox05,
author = {Cox, D.D. and Meier, P. and Oertelt, N. and DiCarlo, J.J.},
title = {{ "Breaking" position-invariant object recognition}},
journal = {Nature Neuroscience},
year = {2005},
volume = {8},
pages = {1145--1147},
number = {9},
keywords = {slowness, vision, vision-models, vision-physiology},
owner = {sprekeler},
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feedback through inhibition of thalamic relay cells.},
journal = {J Neurophysiol},
year = {1998},
volume = {79},
pages = {999--1016},
number = {2},
abstract = {Early studies have shown that spindle oscillations are generated in
the thalamus and are synchronized over wide cortical territories.
More recent experiments have shown that this large-scale synchrony
depends on the integrity of corticothalamic feedback. Previously
proposed mechanisms emphasized exclusively intrathalamic mechanisms
to generate the synchrony of these oscillations. In the present paper,
we propose a cellular mechanism in which the synchrony is dependent
of a mutual interaction between cortex and thalamus. This cellular
mechanism is tested by computational models consisting of pyramidal
cells, interneurons, thalamic reticular (RE) and thalamocortical
(TC) relay cells, on the basis of voltage-clamp data on intrinsic
currents and synaptic receptors present in the circuitry. The model
suggests that corticothalamic feedback must operate on the thalamus
mainly through excitation of GABAergic RE neurons, therefore recruiting
relay cells essentially through inhibition and rebound. We provide
experimental evidence for such dominant inhibition in the lateral
posterior nucleus. In these conditions, the model shows that cortical
discharges optimally evoked thalamic oscillations. This feature is
essential to the present cellular mechanism and is also consistently
observed experimentally. The model further shows that, with this
type of corticothalamic feedback, cortical discharges recruited large
areas of the thalamus because of the divergent cortex-to-RE and RE-to-TC
axonal projections. Consequently, the thalamocortical network generated
patterns of oscillations and synchrony similar to in vivo recordings.
The model also emphasizes the important role of the modulation of
the Ih current by calcium in TC cells. This property conferred a
relative refractoriness to the entire network, a feature also observed
experimentally, as we show here. Further, the same property accounted
for various spatiotemporal features of oscillations, such as systematic
propagation after low-intensity cortical stimulation, local oscillations,
and more generally, a high variability in the patterns of spontaneous
oscillations, similar to in vivo recordings. We propose that the
large-scale synchrony of spindle oscillations in vivo is the result
of thalamocortical interactions in which the corticothalamic feedback
acts predominantly through the RE nucleus. Several predictions are
suggested to test the validity of this model.},
address = {Laboratoire de Neurophysiologie, Faculte de Medecine, Universite
Laval, Quebec G1K 7P4, Canada.},
au = {Destexhe, A and Contreras, D and Steriade, M},
da = {19980414},
date-added = {2008-03-17 18:01:21 +0100},
date-modified = {2008-03-17 18:01:27 +0100},
dcom = {19980414},
edat = {1998/04/18},
issn = {0022-3077 (Print)},
jid = {0375404},
jt = {Journal of neurophysiology},
language = {eng},
lr = {20061115},
mh = {Animals; Cats; Cerebral Cortex/*physiology; Evoked Potentials/physiology;
Excitatory Postsynaptic Potentials/physiology; Feedback; Interneurons/physiology;
Ion Channel Gating; Membrane Potentials; *Models, Neurological; Pyramidal
Cells/physiology; Receptors, AMPA/physiology; Receptors, GABA/physiology;
Receptors, Neurotransmitter/physiology; Synaptic Transmission; Thalamic
Nuclei/cytology/*physiology},
mhda = {1998/04/18 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {9463458},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
rn = {0 (Receptors, AMPA); 0 (Receptors, GABA); 0 (Receptors, Neurotransmitter)},
sb = {IM},
so = {J Neurophysiol. 1998 Feb;79(2):999-1016. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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pages = {739-751}
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address = {NEC Research Institute, 4 Independence Way, New Jersey 08540, USA.
adrienne@research.nj.nec.com},
au = {Fairhall, AL and Lewen, GD and Bialek, W and de Ruyter Van Steveninck,
RR},
bdsk-url-1 = {http://dx.doi.org/10.1038/35090500},
cin = {Nature. 2001 Aug 23;412(6849):776-7. PMID: 11518947},
da = {20010823},
date-added = {2008-03-27 14:10:56 +0100},
date-modified = {2008-03-27 14:11:03 +0100},
dcom = {20010913},
doi = {10.1038/35090500},
edat = {2001/08/24 10:00},
issn = {0028-0836 (Print)},
jid = {0410462},
jt = {Nature},
language = {eng},
lr = {20031114},
mh = {*Adaptation, Physiological; Animals; Data Interpretation, Statistical;
Diptera; Reaction Time; Synaptic Transmission; Visual Pathways/*physiology},
mhda = {2001/09/14 10:01},
own = {NLM},
owner = {sprekeler},
pii = {35090500},
pl = {England},
pmid = {11518957},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
sb = {IM},
so = {Nature. 2001 Aug 23;412(6849):787-92. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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keywords = {reinforcement-learning, plasticity},
owner = {fremaux},
timestamp = {2008.04.18},
url = {http://jn.physiology.org/cgi/content/abstract/98/6/3648}
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title = {How spike generation mechanisms determine the neuronal response to
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increases almost linearly with the firing rate. For higher frequencies,
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depends on the spike initiation mechanism. For conductance-based
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firing rate and a spike "slope factor," which determines the sharpness
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address = {Centre National de la Recherche Scientifique Unite Mixte de Recherche
8119, Neurophysique et Physiologie du Systeme Moteur, Unite de Formation
et de Recherche Biomedicale, Universite Paris 5 Rene Descartes, 75270
Paris Cedex 06, France.},
au = {Fourcaud-Trocme, N and Hansel, D and van Vreeswijk, C and Brunel,
N},
da = {20031219},
date-added = {2007-12-05 18:25:04 +0100},
date-modified = {2007-12-05 18:25:11 +0100},
dcom = {20040116},
edat = {2003/12/20 05:00},
issn = {1529-2401 (Electronic)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
keywords = {Neuronal-Processing},
language = {eng},
mhda = {2004/01/17 05:00},
own = {NLM},
owner = {sprekeler},
pii = {23/37/11628},
pl = {United States},
pmid = {14684865},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
rn = {0 (Sodium Channels)},
sb = {IM},
so = {J Neurosci. 2003 Dec 17;23(37):11628-40. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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timestamp = {2008.04.14}
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author = {Mathias Franzius},
title = {Slowness and Sparseness for Unsupervised Learning of Spatial and
Object Codes from Naturalistic Data},
school = {Humboldt-Universit{\"a}t zu Berlin, Mathematisch-Naturwissenschaftliche
Fakult{\"a}t {I}, Universit{\"a}tsbibliothek},
year = {2008},
owner = {sprekeler},
timestamp = {2008.04.14}
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author = {M. Franzius},
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school = {Lehrstuhl f{\"u}r Grafische Systeme},
year = {2003},
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address = {Brandenburgische Technische Universit{\"a}t Cottbus},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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year = {2007},
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based on unsupervised learning on quasi-natural visual stimuli. The
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which were recently shown to reproduce many properties of complex
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grid-like representation of position and orientation, which is transcoded
into a localized place-field, head-direction, or view representation,
by sparse coding. The type of cells that develops depends solely
on the relevant input statistics, i.e., the movement pattern of the
simulated animal. The numerical simulations are complemented by a
mathematical analysis that allows us to accurately predict the output
of the top SFA layer.},
doi = {10.1371/journal.pcbi.0030166},
keywords = {hippocampus, slowness},
owner = {sprekeler},
pii = {07-PLCB-RA-0240},
pmid = {17784780},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1371/journal.pcbi.0030166}
}
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author = {Mathias Franzius and Henning Sprekeler and Laurenz Wiskott},
title = {Slowness leads to place cells},
booktitle = {Proceedings CNS 2006},
year = {2006},
keywords = {slowness, hippocampus,},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {M.\ Franzius and H.\ Sprekeler and L.\ Wiskott},
title = {Slowness leads to place cells},
booktitle = {Proc.\ Berlin Neuroscience Forum 2006, Bad Liebenwalde, June 8--10},
year = {2006},
pages = {42},
address = {Berlin},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://itb.biologie.hu-berlin.de/~wiskott/Abstracts/FranSpreWisk2006a.html}
}
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pages = {297--299},
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keywords = {hippocampus},
owner = {sprekeler},
pmid = {17195112},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1007/s10827-006-0013-7}
}
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author = {Mathias Franzius and Niko Wilbert and Laurenz Wiskott},
title = {Unsupervised Learning of Invariant 3{D}-Object Representations with
Slow Feature Analysis},
booktitle = {Proc. 3rd Bernstein Symposium for Computational Neuroscience, Göttingen,
September 24--27},
year = {2007},
pages = {105},
publisher = {Bernstein Center for Computational Neuroscience (BCCN) Göttingen},
keywords = {slowness},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://itb.biologie.hu-berlin.de/~wiskott/Abstracts/FranWilbWisk2007.html}
}
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@inproceedings{Friedman01,
author = {N. Friedman and O. Mosenzon and N. Slonim and N. Tishby},
title = {Multivariate Information Bottleneck},
booktitle = {Proceedings of Uncertainty in AI},
year = {2001},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Froemke02,
author = {R. Froemke and Y. Dan},
title = {Spike-timing dependent plasticity induced by natural spike trains},
journal = {Nature},
year = {2002},
volume = {416},
pages = {433-438}
}
@article{Froemke02a,
author = {Froemke, RC and Dan, Y.},
title = {{Spike-timing-dependent synaptic modification induced by natural
spike trains.}},
journal = {Nature},
year = {2002},
volume = {416},
pages = {433--8},
number = {6879},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Froemke07a,
author = {Froemke, RC and Merzenich, MM and Schreiner, CE},
title = {{A synaptic memory trace for cortical receptive field plasticity.}},
journal = {Nature},
year = {2007},
volume = {450},
pages = {425--9},
number = {7168},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Froemke07,
author = {Robert C. Froemke and Michael M. Merzenich and Christoph E. Schreiner},
title = {A synaptic memory trace for cortical receptive field plasticity},
journal = {Nature},
year = {2007},
volume = {450},
pages = {425-429}
}
@article{Froemke05,
author = {R. C. Froemke and M.-M. Poo and Y. Dan},
title = {Spike-timing-dependent synaptic plasticity depends on dendritic location},
journal = {Nature},
year = {2005},
volume = {434},
pages = {221-225}
}
@article{Froemke05a,
author = {Robert C. Froemke and Mu{-}ming Poo and Yang Dan},
title = {Spike-timing-dependent plasticity depends on dendritic location},
journal = {Nature},
year = {2005},
volume = {434},
pages = {221--225},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Froemke06,
author = {R. C. Froemke and I.A. Tsay and M. Raad and J.D. Long and Y. Dan},
title = {Contribution of individual spikes in burst-induced long-term synaptic
modification},
journal = {J. Neurophysiology},
year = {2006},
volume = {95},
pages = {1620-1629}
}
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author = {B.J. Frost and H. Mouritsen},
title = {The neural mechanisms of long distance animal navigation},
journal = {Current Opinions in Neurobiololy},
year = {2006},
volume = {16},
pages = {481--8},
number = {4},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hermens08,
author = {Frouke Hermens and Gediminas Luksys and Wulfram Gerstner
and Michael H. Herzog},
title = {Modeling Spatial and Temporal Aspects of Visual Backward Masking},
journal = {Psych. Rev.},
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volume = {115},
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}
@article{Frohlich08,
author = {Fr{\"o}hlich, Flavio and Bazhenov, Maxim and Sejnowski, Terrence
J.},
title = {Pathological Effect of Homeostatic Synaptic Scaling on Network Dynamics
in Diseases of the Cortex},
journal = {Journal of Neuroscience},
year = {2008},
volume = {28},
pages = {1709-1720},
number = {7},
abstract = {Slow periodic EEG discharges are common in CNS disorders. The pathophysiology
of this aberrant rhythmic activity is poorly understood. We used
a computational model of a neocortical network with a dynamic homeostatic
scaling rule to show that loss of input (partial deafferentation)
can trigger network reorganization that results in pathological periodic
discharges. The decrease in average firing rate in the network by
deafferentation was compensated by homeostatic synaptic scaling of
recurrent excitation among pyramidal cells. Synaptic scaling succeeded
in recovering the network target firing rate for all degrees of deafferentation
(fraction of deafferented cells), but there was a critical degree
of deafferentation for pathological network reorganization. For deafferentation
degrees below this value, homeostatic upregulation of recurrent excitation
had minimal effect on the macroscopic network dynamics. For deafferentation
above this threshold, however, a slow periodic oscillation appeared,
patterns of activity were less sparse, and bursting occurred in individual
neurons. Also, comparison of spike-triggered afferent and recurrent
excitatory conductances revealed that information transmission was
strongly impaired. These results suggest that homeostatic plasticity
can lead to secondary functional impairment in case of cortical disorders
associated with cell loss.},
doi = {10.1523/JNEUROSCI.4263-07.2008},
eprint = {http://www.jneurosci.org/cgi/reprint/28/7/1709.pdf},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.jneurosci.org/cgi/content/abstract/28/7/1709}
}
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author = {Y.-X. Fu and K. Djupsund and H. Gao and B. Hayden and K. Shen and
Y. Dan},
title = {Temporal specificity in the cortical plasticity of visual space representation},
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year = {2002},
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pages = {1999-2003}
}
@mastersthesis{Fuentes93,
author = {U. Fuentes},
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im Cortex},
school = {Technische Universit\protect{\"a}t M\protect{\"u}nchen},
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D.S. Touretzky},
title = {Influence of Path Integration Versus Environmental Orientation on
Place Cell Remapping Between Visually Identical Environments},
journal = {Journal of Neurophysiology},
year = {2005},
volume = {94},
pages = {2603--2613},
number = {4},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Fuhs98,
author = {M. C. Fuhs and A. D. Redish and D. S. Touretzky},
title = {A Visually Driven Hippocampal Place Cell Model},
journal = {Proceedings of the Sixth Annual Conference on Computational Neuroscience
: Trends in Research, 1998},
year = {1998},
pages = {101--106},
editor = {J. Bower},
keywords = {Hippocampus},
owner = {sprekeler},
publisher = {Plenum Publishing},
timestamp = {2008.04.14}
}
@article{Fuhs06,
author = {Mark C Fuhs and David S Touretzky},
title = {A spin glass model of path integration in rat medial entorhinal cortex.},
journal = {J Neurosci},
year = {2006},
volume = {26},
pages = {4266--4276},
number = {16},
month = {Apr},
abstract = {Electrophysiological recording studies in the dorsocaudal region of
medial entorhinal cortex (dMEC) of the rat reveal cells whose spatial
firing fields show a remarkably regular hexagonal grid pattern (Fyhn
et al., 2004; Hafting et al., 2005). We describe a symmetric, locally
connected neural network, or spin glass model, that spontaneously
produces a hexagonal grid of activity bumps on a two-dimensional
sheet of units. The spatial firing fields of the simulated cells
closely resemble those of dMEC cells. A collection of grids with
different scales and/or orientations forms a basis set for encoding
position. Simulations show that the animal's location can easily
be determined from the population activity pattern. Introducing an
asymmetry in the model allows the activity bumps to be shifted in
any direction, at a rate proportional to velocity, to achieve path
integration. Furthermore, information about the structure of the
environment can be superimposed on the spatial position signal by
modulation of the bump activity levels without significantly interfering
with the hexagonal periodicity of firing fields. Our results support
the conjecture of Hafting et al. (2005) that an attractor network
in dMEC may be the source of path integration information afferent
to hippocampus.},
doi = {10.1523/JNEUROSCI.4353-05.2006},
keywords = {Entorhinal Cortex},
owner = {tomm},
pii = {26/16/4266},
pmid = {16624947},
timestamp = {2008.05.14},
url = {http://dx.doi.org/10.1523/JNEUROSCI.4353-05.2006}
}
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patterns},
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year = {2004},
volume = {14},
pages = {573-578},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {S. Fusi},
title = {Hebbian spike-driven synaptic plasticity for learning patterns of
mean firing rates},
journal = {Biol. Cybern.},
year = {2002},
volume = {87},
pages = {459-470}
}
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author = {Fusi, S. and Abbott, LF},
title = {{Limits on the memory storage capacity of bounded synapses}},
journal = {Nature Neuroscience},
year = {2007},
volume = {10},
pages = {485--493},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {S. Fusi and M. Annunziato and D. Badoni and A. Salamon and D.J.Amit},
title = {Spike-driven synaptic plasticity: theory, simulation, VLSI implementation},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {2227-2258}
}
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author = {S. Fusi and P.J. Drew and L.F. Abbott},
title = {Cascade models of synaptically stored memories},
journal = {Neuron},
year = {2005},
volume = {45},
pages = {599-611}
}
@article{Fusi05a,
author = {Fusi, S. and Drew, P.J. and Abbott, LF},
title = {{Cascade Models of Synaptically Stored Memories}},
journal = {Neuron},
year = {2005},
volume = {45},
pages = {599--611},
number = {4},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
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author = {S. Fusi and M. Mattia},
title = {Collective behavior of networks with linear (VLSI) Integrate and
Fire Neurons},
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year = {1999},
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}
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author = {M. Fyhn and T. Hafting and A. Treves and M.B. Moser and E.I. Moser},
title = {Hippocampal remapping and grid realignment in entorhinal cortex},
journal = {Nature},
year = {2007},
volume = {446},
pages = {190--4},
number = {7132},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {Marianne Fyhn and Sturla Molden and Menno P. Witter and Edvard I.
Moser and May-Britt Moser},
title = {{Spatial representation in the entorhinal cortex}},
journal = {Science},
year = {2004},
volume = {305},
pages = {1258--1264},
number = {5688},
month = aug,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {P. F{\"o}ldi{\`a}k},
title = {Learning invariance from transformation sequences},
journal = {Neural Computation},
year = {1991},
volume = {3},
pages = {194--200},
comment = {The trace-rule.},
keywords = {slowness, invariance learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Foldiak89,
author = {Peter F{\"o}ldi{\`a}k},
title = {Adaptive Network for Optimal Linear Feature Extraction},
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural
Networks},
year = {1989},
pages = {401-405},
address = {New York},
publisher = {IEEE Press},
category = {model},
keywords = {PCA, lateral inhibition,Various-Artists},
owner = {sprekeler},
remarks = {introduces a network made up of n linear units learning according
to Oja's rule with lateral connections learning anti-hebbian, which
evolves to find weights such that the rows of the transformation
matrix span the subspace of the first n principal components of the
input},
timestamp = {2008.04.14},
voume = {1}
}
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author = {G.T. Buracas and A.M. Zador and M.R. De\protect{Weese} and T. D.
Albright},
title = {Efficient discrimination of temporal patterns by motion-sensitive
neurons in primate visual cortex},
journal = {Neuron},
year = {1998},
volume = {20},
pages = {959-969}
}
@incollection{Gabbiani98,
author = {F. Gabbiani and C. Koch},
title = {Principles of spike train analysis},
booktitle = {Methods in Neuronal Modeling},
publisher = {MIT press},
year = {1998},
editor = {C. Koch and I. Segev},
chapter = {9},
pages = {312-360},
edition = {2nd}
}
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author = {F. Gabbiani and C. Koch},
title = {Coding of time-varying signals in spike trains of integrate-and-fire
neurons with random threshold},
journal = {Neural Computation},
year = {1996},
volume = {8},
pages = {44-66}
}
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author = {A. Gabrielov},
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number = {4},
month = oct,
owner = {sprekeler},
timestamp = {2008.04.14}
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author = {E. Gamble and C. Koch},
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synaptic input},
journal = {Science},
year = {1987},
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pages = {1311-1315}
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title = {Stochastic resonance},
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pages = {223--287}
}
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keywords = {various-artists},
owner = {sprekeler},
timestamp = {2008.04.14}
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title = {Statistical Mechanics of Neural Networks},
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address = {Berlin}
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@article{Gasparini04,
author = {Gasparini, Sonia and Migliore, Michele and Magee, Jeffrey C},
title = {On the initiation and propagation of dendritic spikes in CA1 pyramidal
neurons.},
journal = {J Neurosci},
year = {2004},
volume = {24},
pages = {11046--11056},
number = {49},
abstract = {Under certain conditions, regenerative voltage spikes can be initiated
locally in the dendrites of CA1 pyramidal neurons. These are interesting
events that could potentially provide neurons with additional computational
abilities. Using whole-cell dendritic recordings from the distal
apical trunk and proximal tuft regions and realistic computer modeling,
we have determined that highly synchronized and moderately clustered
inputs are required for dendritic spike initiation: approximately
50 synaptic inputs spread over 100 mum of the apical trunk/tuft need
to be activated within 3 msec. Dendritic spikes are characterized
by a more depolarized voltage threshold than at the soma [-48 +/-
1 mV (n = 30) vs -56 +/- 1 mV (n = 7), respectively] and are mainly
generated and shaped by dendritic Na+ and K+ currents. The relative
contribution of AMPA and NMDA currents is also important in determining
the actual spatiotemporal requirements for dendritic spike initiation.
Once initiated, dendritic spikes can easily reach the soma, but their
propagation is only moderately strong, so that it can be modulated
by physiologically relevant factors such as changes in the V(m) and
the ionic composition of the extracellular solution. With effective
spike propagation, an extremely short-latency neuronal output is
produced for greatly reduced input levels. Therefore, dendritic spikes
function as efficient detectors of specific input patterns, ensuring
that the neuronal response to high levels of input synchrony is a
precisely timed action potential output.},
address = {Neuroscience Center, Louisiana State University Health Science Center,
New Orleans, Louisiana 70112, USA. sgaspa1@lsuhsc.edu},
au = {Gasparini, S and Migliore, M and Magee, JC},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.2520-04.2004},
da = {20041213},
date-added = {2008-03-28 23:32:52 +0100},
date-modified = {2008-03-28 23:32:55 +0100},
dcom = {20050707},
doi = {10.1523/JNEUROSCI.2520-04.2004},
edat = {2004/12/14 09:00},
gr = {NS39458/NS/United States NINDS},
issn = {1529-2401 (Electronic)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20071114},
mh = {Action Potentials/*physiology; Animals; Dendrites/*physiology; Electrodes;
Excitatory Postsynaptic Potentials/physiology; Ion Channels/physiology;
Models, Neurological; Pyramidal Cells/*physiology/ultrastructure;
Rats; Rats, Sprague-Dawley; Receptors, AMPA/physiology; Receptors,
N-Methyl-D-Aspartate/physiology},
mhda = {2005/07/08 09:00},
own = {NLM},
owner = {sprekeler},
pii = {24/49/11046},
pl = {United States},
pmid = {15590921},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, N.I.H., Extramural; Research
Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.;
Research Support, U.S. Gov't, P.H.S.},
pubm = {Print},
rn = {0 (Ion Channels); 0 (Receptors, AMPA); 0 (Receptors, N-Methyl-D-Aspartate)},
sb = {IM},
so = {J Neurosci. 2004 Dec 8;24(49):11046-56. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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author = {R. Gaudoin and W. Gerstner and J. L. van Hemmen},
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Neurobiology Conference 1995},
publisher = {Georg Thieme Verlag},
year = {1995},
editor = {N. Elsner and R. Menzel},
volume = {2},
pages = {898}
}
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stages of hippocampal systems},
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year = {2002},
volume = {86},
pages = {15-28}
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author = {V. V. Gavrilov and S. I. Wiener and A. Berthoz},
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rats passively displaced on a mobile robot}},
journal = {Hippocampus},
year = {1998},
volume = {8},
pages = {475--490},
number = {5},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
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pages = {2758 - 2771}
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pages = {849--859},
number = {9},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {P. Georges-Francois and E. T. Rolls and R. G. Robertson},
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not head direction or eye position or place}},
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year = {1999},
volume = {9},
pages = {197--212},
number = {3},
month = apr,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {W Gerstner},
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year = {1991},
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author = {W. Gerstner},
title = {Spiking Neurons},
booktitle = {Pulsed Neural Networks},
publisher = {MIT-Press},
year = {1998},
editor = {W. Maass and C. M. Bishop},
chapter = {1},
pages = {3-53}
}
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author = {W. Gerstner},
title = {Populations of spiking neurons},
booktitle = {Pulsed Neural Networks},
publisher = {MIT-Press},
year = {1998},
editor = {W. Maass and C. M. Bishop},
chapter = {10},
pages = {261-295}
}
@incollection{Gerstner00d,
author = {W. Gerstner},
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publisher = {Elsevier},
year = {2001},
editor = {Frank Moss and Stan Gielen},
volume = {4},
chapter = {12},
pages = {469-516},
address = {Amsterdam}
}
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author = {W. Gerstner},
title = {Coding properties of spiking neurons: reverse- and cross-correlations},
journal = {Neural Networks},
year = {2001},
volume = {14},
pages = {599-610}
}
@article{Gerstner01a,
author = {Gerstner, W.},
title = {{Coding properties of spiking neurons: reverse and cross-correlations}},
journal = {Neural Networks},
year = {2001},
volume = {14},
pages = {599--610},
number = {6-7},
keywords = {neuronal-processing},
owner = {sprekeler},
publisher = {Elsevier Ltd},
timestamp = {2008.04.14}
}
@article{Gerstner00,
author = {W. Gerstner},
title = {Population dynamics of spiking neurons: fast transients, asynchronous
states and locking},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {43-89}
}
@article{Gerstner98a,
author = {W. Gerstner},
title = {Population dynamics for spiking neurons: fast transients, asynchronous
states and locking},
journal = { Neural Computation, to appear},
year = {2000},
volume = {12},
pages = {43-89}
}
@article{Gerstner99a,
author = {W. Gerstner},
title = {Population dynamics of spiking neurons: fast transients, asynchronous
states and locking},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {43-89}
}
@incollection{Gerstner99c,
author = {W. Gerstner},
title = {Rapid signal transmission by a population of spiking neurons},
booktitle = {ICANN'99 Artificial Neural Networks},
publisher = {IEE Conference Publication},
year = {1999},
volume = {470},
pages = {7-12}
}
@article{Gerstner96d,
author = {W. Gerstner},
title = {Rapid phase locking in systems of pulse-coupled oscillators with
delays},
journal = {Phys. Rev. Lett.},
year = {1996},
volume = {76},
pages = {1755-1758}
}
@article{Gerstner95a,
author = {W. Gerstner},
title = {Time Structure of the Activity in Neural Network Models},
journal = {Phys. Rev. E},
year = {1995},
volume = {51},
pages = {738-758},
number = {1}
}
@article{Gerstner96c,
author = {W. Gerstner},
title = {A framework for spiking model neurons: The spike response method},
journal = {preprint, TU-Muenchen},
year = {1995},
volume = {xx},
pages = {xx}
}
@book{Gerstner93c,
title = {Kodierung und Signal\protect{\"u}bertragung in Neuronalen Systemen:
Assoziative Netzwerke mit stochastisch feuernden Neuronen},
publisher = {Harri--Deutsch Verlag},
year = {1993},
author = {W. Gerstner},
volume = {15},
series = {Reihe Physik},
address = {Frankfurt/Main},
note = {Dissertation Nov. 1992, TU M\protect{\"u}nchen}
}
@article{Gerstner97,
author = {W. Gerstner and L. F. Abbott},
title = {Learning navigational maps through potentiation and modulation of
hippocampal place cells},
journal = {Journal of Comput. Neurosci.},
year = {1997},
volume = {4},
pages = {79-94}
}
@incollection{Gerstner94b,
author = {W. Gerstner and J. L. van~Hemmen},
title = {How to describe neural activity -- spikes, rates, or assemblies?},
booktitle = {Advances in Neural Information Processing Systems 6},
publisher = {Morgan Kaufmann Publishers, San Francisco, CA},
year = {1994},
editor = {J. D. Cowan and G. Tesauro and J. Alspector},
pages = {463-470}
}
@inproceedings{Gerstner94a,
author = {W. Gerstner and J. L. van~Hemmen},
title = {Coding and information processing in neural networks.},
booktitle = {Models of neural networks II},
year = {1994},
editor = {E. Domany and J. L. van Hemmen and K. Schulten},
pages = {1-93},
address = {New York},
publisher = {Springer-Verlag},
anote = {chap. 1}
}
@incollection{Gerstner93e,
author = {W. Gerstner and J. L. van~Hemmen},
title = {Spikes or Rates? -- Stationary, oscillatory, and spatio-temporal
states in an associative network of spiking neurons},
booktitle = {ICANN'93, Proceedings of the International Conference on Artificial
Neural Networks, Amsterdam, 13-16 September 1993},
publisher = {Springer-Verlag},
year = {1993},
editor = {S. Gielen and B. Kappen},
pages = {633-638},
address = {London}
}
@article{Gerstner93d,
author = {W. Gerstner and J. L. van~Hemmen},
title = {Coherence and incoherence in a globally coupled ensemble of pulse
emitting units},
journal = {Phys. Rev. Lett.},
year = {1993},
volume = {71},
pages = {312--315},
number = {3}
}
@article{Gerstner92a,
author = {W. Gerstner and J. L. van Hemmen},
title = {Associative memory in a network of `spiking' neurons.},
journal = {Network},
year = {1992},
volume = {3},
pages = {139--164}
}
@article{Gerstner92b,
author = {W. Gerstner and J. L. van Hemmen},
title = {Universality in neural networks: The importance of the mean firing
rate.},
journal = {Biol. Cybern.},
year = {1992},
volume = {67},
pages = {195--205}
}
@article{Gerstner96b,
author = {W. Gerstner and J. L. van Hemmen and J. D. Cowan},
title = {What matters in neuronal locking},
journal = {Neural Comput.},
year = {1996},
volume = {8},
pages = {1653-1676}
}
@incollection{Gerstner97b,
author = {W. Gerstner and R. Kempter and J.L. van Hemmen and H. Wagner},
title = {A developmental learning rule for coincidence tuning in the barn
owl auditory system},
booktitle = {Computational Neuroscience: trends in research 1997},
publisher = {Plenum Press, New York},
year = {1997},
editor = {J. Bower},
pages = {665-669}
}
@article{Gerstner96,
author = {Gerstner, W. and Kempter, R. and van Hemmen, J.L. and Wagner, H.},
title = {{A neuronal learning rule for sub-millisecond temporal coding}},
journal = {Nature},
year = {1996},
volume = {383},
pages = {76--78},
number = {6595},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@incollection{Gerstner98d,
author = {W. Gerstner and R. Kempter and J. L. van Hemmen},
title = {Hebbian learning of Pulse timing in the Barn Owl auditory system},
booktitle = {Pulsed Neural Networks},
publisher = {MIT-Press},
year = {1998},
editor = {W. Maass and C. M. Bishop},
chapter = {14},
pages = {353-377}
}
@article{Gerstner96a,
author = {W. Gerstner and R. Kempter and J. Leo van Hemmen and H. Wagner},
title = {A neuronal learning rule for sub-millisecond temporal coding},
journal = {Nature},
year = {1996},
volume = {383},
pages = {76-78}
}
@book{Gerstner02a,
title = {{Spiking neuron models}},
publisher = {Cambridge University Press New York},
year = {2002},
author = {Gerstner, W. and Kistler, W.M.},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Gerstner02c,
author = {Gerstner, W. and Kistler, W.M.},
title = {{Mathematical formulations of Hebbian learning}},
journal = {Biological Cybernetics},
year = {2002},
volume = {87},
pages = {404--415},
number = {5},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Springer},
timestamp = {2008.04.14}
}
@book{Gerstner02,
title = {Spiking Neuron Models},
publisher = {Cambridge University Press},
year = {2002},
author = {W. Gerstner and W. K. Kistler},
address = {Cambridge UK}
}
@article{Gerstner02b,
author = {W. Gerstner and W. K. Kistler},
title = {Mathematical Formulations of Hebbian Learning},
journal = {Biological Cybernetics},
year = {2002},
volume = {87},
pages = {404-415}
}
@book{Gerstner02d,
title = {Spiking neuron models : single neurons, populations, plasticity },
publisher = {Cambridge University Press},
year = {2002},
author = {Gerstner, Wulfram and Kistler, Werner M.},
address = {Cambridge, U.K. },
bdsk-url-1 = {http://www.loc.gov/catdir/samples/cam031/2002067657.html},
call-number = {QP363},
date-added = {2008-03-26 15:57:05 +0100},
date-modified = {2008-03-26 15:57:17 +0100},
dewey-call-number = {573.8/536},
genre = {Neurons},
isbn = {0521813840 (hardback)},
library-id = {2002067657},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.loc.gov/catdir/samples/cam031/2002067657.html}
}
@article{Gerstner97c,
author = {W. Gerstner and A.K. Kreiter and H. Markram and A.V.M. Herz},
title = {Neural codes: firing rates and beyond},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1997},
volume = {94},
pages = {12740-12741}
}
@article{Gerstner93a,
author = {W. Gerstner and R. Ritz and J. L. van~Hemmen},
title = {A biologically motivated and analytically soluble model of collective
oscillations in the cortex: {I}. Theory of weak locking.},
journal = {Biol. Cybern.},
year = {1993},
volume = {68},
pages = {363--374}
}
@article{Gerstner93b,
author = {W. Gerstner and R. Ritz and J. L. van~Hemmen},
title = {Why spikes? {H}ebbian learning and retrieval of time--resolved excitation
patterns.},
journal = {Biol. Cybern.},
year = {1993},
volume = {69},
pages = {503--515}
}
@incollection{Gerstner95d,
author = {W. Gerstner and A. Schiegg and R. Ritz and J. L. van Hemmen},
title = {Long term potentiation in dendritic spines: a model study},
booktitle = {Goettingen Neurobiology Report 1995; Proceedings of the 23rd Goettingen
Neurobiology Conference 1995},
publisher = {Georg Thieme Verlag},
year = {1995},
editor = {N. Elsner and R. Menzel},
volume = {1},
pages = {121}
}
@article{Gestri78,
author = {G. Gestri},
title = {Dynamics of a Model for the variability of the interspike intervals
in a retinal neuron},
journal = {Biological Cybernetics},
year = {1978},
volume = {31},
pages = {97-98}
}
@book{Geszti90,
title = {Physical Models of Neural Networks},
publisher = {World Scientific},
year = {1990},
author = {Tam\'{a}s Geszti},
address = {Singapore}
}
@article{Ghose04,
author = {G. M. Ghose},
title = {Learning in mammalian sensory cortex},
journal = {Current Opinion in Neurobiology},
year = {2004},
volume = {14},
pages = {513-518},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Ghose97,
author = {G. M. Ghose and J. H. R. Maunsell},
title = {Perceptual learning can selectively alter neural responses in primate
V1},
journal = {Society for Neuroscience Abstracts},
year = {1997},
volume = {23},
pages = {1544},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Ghose02,
author = {G. M. Ghose and T. Yang and J. H. R. Maunsell},
title = {Physiological correlates of perceptual learning in monkey V1 and
V2},
journal = {The Journal of Neurophysiology},
year = {2002},
volume = {87},
pages = {1867-1888},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Gibson63,
author = {E. J. Gibson},
title = {Perceptual learning},
journal = {Annual Reviews in Psychology},
year = {1963},
volume = {14},
pages = {29-56},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Gibson05,
author = {J.R. Gibson and M. Beierlein and B.W. Connors},
title = {Functional Properties of Electrical Synapses Between Inhibitory Interneurons
of Neocortical Layer 4},
journal = {J.~{N}europhysiol.},
year = {2005},
volume = {93},
pages = {467--480}
}
@article{Gilbert98,
author = {C. D. Gilbert},
title = {Adult cortical dynamics},
journal = {Physiological Reviews},
year = {1998},
volume = {78},
pages = {467-485},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Gilbert01,
author = {C. D. Gilbert and M. Sigman and R. E. Crist},
title = {The neural basis of perceptual learning},
journal = {Neuron},
year = {2001},
volume = {31},
pages = {681-697},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Gillies00,
author = {Gillies, Andrew and Arbuthnott, Gordon},
title = {Computational models of the basal ganglia},
journal = {Movement Disorders},
year = {2000},
volume = {15},
pages = {762--770},
number = {5},
owner = {fremaux},
timestamp = {2008.05.07},
url = {http://dx.doi.org/10.1002/1531-8257(200009)15:5<762::AID-MDS1002>3.0.CO;2-2}
}
@article{Gillner98,
author = {S. Gillner and H. Mallot},
title = {Navigation and acquisition of spatial knowledge in a virtual maze},
journal = {J. Cognitive Neurosciencie},
year = {1998},
volume = {10},
pages = {445-463}
}
@incollection{Giorno92,
author = {V. Giorno and A. G. Nobile and L. M.Ricciardi},
title = {Instantaneous return processes and neuronal firings.},
booktitle = {Cybernetics and Systems Research, Vol 1.},
publisher = {World Scientific Press},
year = {1992},
editor = {R. Trappl},
pages = {829-236}
}
@article{Glass79,
author = {L. Glass and M.C. Mackey},
title = {A simple model for phase locking in biological oscillators},
journal = {J. Mathematical Biol.},
year = {1979},
volume = {7},
pages = {339-352}
}
@article{Gluck03,
author = {M.A. Gluck and M. Meeter and C.E. Myers},
title = {Computational models of the hippocampal region: linking incremental
learning and episodic memory},
journal = {TRENDS in Cognitive Sciences},
year = {2003},
volume = {7},
number = {6},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Gluss67,
author = {B. Gluss},
title = {A model of neuron firing with exponential decay of potential resulting
in diffusion equations for the probability density},
journal = {Bull. Math. Biophysics},
year = {1967},
volume = {29},
pages = {233-243},
comment = {Building on work Gers:1964(41), he adds exponential decay between
PSPs (i.e. leakiness) and performs the diffusion limit for many,
tiny PSPs. Derives expressions for ISID using integral equation ansatz.},
topic = {Stochastic process, integrate-and-fire, ISI, FPTD, integral equation,
diffusion approximation}
}
@article{Gold99,
author = {J. Gold and P. J. Bennett and A. B. Sekuler},
title = {Signal but not noise changes with perceptual learning},
journal = {Nature},
year = {1999},
volume = {402},
pages = {176-178},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Gold94,
author = {J. I. Gold and M. F. Bear},
title = {A model of dendritic spike {$Ca^{2+}$} concentration exploring possible
basis for sliding synaptic modification threshold},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1994},
volume = {91},
pages = {3941-3945}
}
@article{Goldberg64,
author = {J.M. Goldberg and H.O. Adrian and F.D. Smith},
title = {Response of neurons of the superior olivary complex of cat to acoustic
stimuli of long duration},
journal = {J. Neurophysiology},
year = {1964},
volume = {27},
pages = {706-749},
optannote = {experimental interval distribution/hazard function/renewal}
}
@article{Goldberg02,
author = {J. Goldberg and K. Holthoff and R. Yuste},
title = {A problem with Hebb and local spikes},
journal = {Trends in Neurosciences},
year = {2002},
volume = {25},
pages = {433-435}
}
@article{Goldberg69,
author = {Jay M. Goldberg and Paul B. Brown},
title = {Response of Binaural Neurons of Dog {S}uperior {O}livary {C}omplex
to Dichotic Tonal Stimuli: Some Physiological Mechanisms of Sound
Localization},
journal = {J. Neurophysiol.},
year = {1969},
volume = {32},
pages = {613--636}
}
@article{Golding02,
author = {Golding, Nace L and Staff, Nathan P and Spruston, Nelson},
title = {Dendritic spikes as a mechanism for cooperative long-term potentiation.},
journal = {Nature},
year = {2002},
volume = {418},
pages = {326--331},
number = {6895},
abstract = {Strengthening of synaptic connections following coincident pre- and
postsynaptic activity was proposed by Hebb as a cellular mechanism
for learning. Contemporary models assume that multiple synapses must
act cooperatively to induce the postsynaptic activity required for
hebbian synaptic plasticity. One mechanism for the implementation
of this cooperation is action potential firing, which begins in the
axon, but which can influence synaptic potentiation following active
backpropagation into dendrites. Backpropagation is limited, however,
and action potentials often fail to invade the most distal dendrites.
Here we show that long-term potentiation of synapses on the distal
dendrites of hippocampal CA1 pyramidal neurons does require cooperative
synaptic inputs, but does not require axonal action potential firing
and backpropagation. Rather, locally generated and spatially restricted
regenerative potentials (dendritic spikes) contribute to the postsynaptic
depolarization and calcium entry necessary to trigger potentiation
of distal synapses. We find that this mechanism can also function
at proximal synapses, suggesting that dendritic spikes participate
generally in a form of synaptic potentiation that does not require
postsynaptic action potential firing in the axon.},
address = {Department of Neurobiology and Physiology, Institute for Neuroscience,
Northwestern University, Evanston, IL 60208-3520, USA.},
au = {Golding, NL and Staff, NP and Spruston, N},
bdsk-url-1 = {http://dx.doi.org/10.1038/nature00854},
da = {20020718},
date-added = {2008-03-28 23:33:54 +0100},
date-modified = {2008-03-28 23:33:56 +0100},
dcom = {20020809},
doi = {10.1038/nature00854},
edat = {2002/07/19 10:00},
issn = {0028-0836 (Print)},
jid = {0410462},
jt = {Nature},
language = {eng},
lr = {20061115},
mh = {*Action Potentials; Animals; Axons/physiology; Calcium/metabolism;
Calcium Signaling; Dendrites/*physiology; Excitatory Postsynaptic
Potentials/physiology; *Long-Term Potentiation; Pyramidal Cells/*cytology/*physiology;
Rats; Rats, Wistar; Synapses/physiology; Theta Rhythm},
mhda = {2002/08/10 10:01},
own = {NLM},
owner = {sprekeler},
pii = {nature00854},
pl = {England},
pmid = {12124625},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
rn = {7440-70-2 (Calcium)},
sb = {IM},
so = {Nature. 2002 Jul 18;418(6895):326-31. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Goldman01,
author = {Goldman, M S and Golowasch, J and Marder, E and Abbott, L F},
title = {Global structure, robustness, and modulation of neuronal models.},
journal = {J Neurosci},
year = {2001},
volume = {21},
pages = {5229--5238},
number = {14},
abstract = {The electrical characteristics of many neurons are remarkably robust
in the face of changing internal and external conditions. At the
same time, neurons can be highly sensitive to neuromodulators. We
find correlates of this dual robustness and sensitivity in a global
analysis of the structure of a conductance-based model neuron. We
vary the maximal conductance parameters of the model neuron and,
for each set of parameters tested, characterize the activity pattern
generated by the cell as silent, tonically firing, or bursting. Within
the parameter space of the five maximal conductances of the model,
we find directions, representing concerted changes in multiple conductances,
along which the basic pattern of neural activity does not change.
In other directions, relatively small concurrent changes in a few
conductances can induce transitions between these activity patterns.
The global structure of the conductance-space maps implies that neuromodulators
that alter a sensitive set of conductances will have powerful, and
possibly state-dependent, effects. Other modulators that may have
no direct impact on the activity of the neuron may nevertheless change
the effects of such direct modulators via this state dependence.
Some of the results and predictions arising from the model studies
are replicated and verified in recordings of stomatogastric ganglion
neurons using the dynamic clamp.},
address = {Volen Center and Department of Biology, Brandeis University, Waltham,
Massachusetts 02454, USA. mark_g@mit.edu},
au = {Goldman, MS and Golowasch, J and Marder, E and Abbott, LF},
da = {20010704},
date-added = {2007-12-11 22:16:34 +0100},
date-modified = {2007-12-11 22:16:42 +0100},
dcom = {20010726},
edat = {2001/07/05 10:00},
gr = {MH46742/MH/United States NIMH},
issn = {1529-2401 (Electronic)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20071114},
mhda = {2001/07/28 10:01},
own = {NLM},
owner = {sprekeler},
pii = {21/14/5229},
pl = {United States},
pmid = {11438598},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.},
pubm = {Print},
rn = {0 (Neurotransmitter Agents)},
sb = {IM},
so = {J Neurosci. 2001 Jul 15;21(14):5229-38. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Goldman03,
author = {Goldman, Mark S and Levine, Joseph H and Major, Guy and Tank, David
W and Seung, H S},
title = {Robust persistent neural activity in a model integrator with multiple
hysteretic dendrites per neuron.},
journal = {Cereb Cortex},
year = {2003},
volume = {13},
pages = {1185--1195},
number = {11},
abstract = {Short-term memory is often correlated with persistent changes in neuronal
firing rates in response to transient inputs. We model the persistent
maintenance of an analog eye position signal by an oculomotor neural
integrator receiving transient eye movement commands. Previous models
of this network rely on precisely tuned positive feedback with <1%
tolerance to mistuning, or use neurons that exhibit large discontinuities
in firing rate with small changes in eye position. We show analytically
how using neurons with multiple bistable dendritic compartments can
enhance the robustness of eye fixations to mistuning while reproducing
the approximately linear and continuous relationship between neuronal
firing rates and eye position, and the dependence of neuron pair
firing rate relationships on the direction of the previous saccade.
The response of the model to continuously varying inputs makes testable
predictions for the performance of the vestibuloocular reflex. Our
results suggest that dendritic bistability could stabilize the persistent
neural activity observed in working memory systems.},
address = {Howard Hughes Medical Institute, Brain and Cognitive Sciences Department,
MIT, Cambridge, MA 02139, USA. mgoldma1@wellesley.edu},
au = {Goldman, MS and Levine, JH and Major, G and Tank, DW and Seung, HS},
da = {20031024},
date-added = {2008-03-28 23:36:27 +0100},
date-modified = {2008-03-28 23:36:33 +0100},
dcom = {20031212},
edat = {2003/10/25 05:00},
gr = {EY06558/EY/United States NEI; RR00166/RR/United States NCRR},
issn = {1047-3211 (Print)},
jid = {9110718},
jt = {Cerebral cortex (New York, N.Y. : 1991)},
language = {eng},
lr = {20071114},
mh = {Action Potentials/*physiology; Dendrites/*physiology; Eye Movements/physiology;
*Neural Networks (Computer); Neurons/physiology},
mhda = {2003/12/13 05:00},
own = {NLM},
owner = {sprekeler},
pl = {United States},
pmid = {14576210},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.},
pubm = {Print},
sb = {IM},
so = {Cereb Cortex. 2003 Nov;13(11):1185-95. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Goles81,
author = {E. Goles and J. Olivos},
title = {Comportement p\'{e}riodique des fonctions \`{a} seuil binaires et
applications.},
journal = {Discr. Appl. Math.},
year = {1981},
volume = {3},
pages = {93--105}
}
@inproceedings{Goles86,
author = {E. Goles and Y. Vichniac},
title = {Lyapunov functions for parallel neural networks.},
booktitle = {Neural networks for computing},
year = {1986},
editor = {J. S. Denker},
pages = {165--181},
address = {New York},
publisher = {American Institute of Physics}
}
@article{Golob97,
author = {E. J. Golob and J. S. Taube},
title = {{Head direction cells and episodic spatial information in rats without
a hippocampus}},
journal = {Proceedings of the National Academy of Sciences of the United States
of America},
year = {1997},
volume = {94},
pages = {7645--7650},
number = {14},
month = jul,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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excitatory and inhibitory populations},
journal = {Phys.~{R}ev.~{E}},
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pages = {061911}
}
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publisher = {Elsevier Science},
year = {2001},
editor = {F. Moss and S. Gielen},
author = {D. Golomb and D. Hansel and G. Mato},
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Modeling}
}
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year = {2006},
publisher = {Springer}
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keywords = {Lie groups, vision, Vision-model, invariance learning},
owner = {sprekeler},
timestamp = {2008.04.14}
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author = {Gordon, Urit and Polsky, Alon and Schiller, Jackie},
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year = {2006},
volume = {26},
pages = {12717--12726},
number = {49},
month = dec,
abstract = {Synaptic plasticity rules widely determine how cortical networks develop
and store information. Using confocal imaging and dual site focal
synaptic stimulation, we show that basal dendrites, which receive
the majority of synapses innervating neocortical pyramidal neurons,
contain two compartments with respect to plasticity rules. Synapses
innervating the proximal basal tree are easily modified when paired
with the global activity of the neuron. In contrast, synapses innervating
the distal basal tree fail to change in response to global suprathreshold
activity or local dendritic spikes. These synapses can undergo long-term
potentiation under unusual conditions when local NMDA spikes, which
evoke large calcium transients, are paired with a "gating molecule,"
BDNF. Moreover, these synapses use a new temporal plasticity rule,
which is an order of magnitude longer than spike timing dependent
plasticity and prefers reversed presynaptic/postsynaptic activation
order. The newly described plasticity compartmentalization of basal
dendrites expands the networks plasticity rules and may support different
learning and developmental functions.},
comment = {10.1523/JNEUROSCI.3502-06.2006},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.jneurosci.org/cgi/content/abstract/26/49/12717}
}
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for space: interaction between path integration and environmental
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journal = {Journal of Neuroscience},
year = {1996},
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pages = {8027--8040},
number = {24},
month = dec,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Gothard96a,
author = {K. M. Gothard and W. E. Skaggs and K. M. Moore and B. L. McNaughton},
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frames in a landmark-based navigation task}},
journal = {Journal of Neuroscience},
year = {1996},
volume = {16},
pages = {823--835},
number = {2},
month = jan,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Graupner07,
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pages = {11--38},
number = {1--2}
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year = {1989},
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owner = {sprekeler},
timestamp = {2008.04.14}
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title = {The {A}daptive {B}rain {I}},
publisher = {Elsevier},
year = {1987},
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note = {reprinted in Anderson and Rosenfeld, 1990}
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note = {reprinted in Anderson and Rosenfeld, 1990}
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Modeling the interaction of visual and path integration cues},
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year = {2001},
volume = {11},
pages = {216--239},
number = {3},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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title = {Nonlinear oscillations, dynamical systems, and bifurcations of vector
fields},
publisher = {Springer Verlag, New York},
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@article{Gulledge03,
author = {Gulledge, Allan T and Stuart, Greg J},
title = {Action potential initiation and propagation in layer 5 pyramidal
neurons of the rat prefrontal cortex: absence of dopamine modulation.},
journal = {J Neurosci},
year = {2003},
volume = {23},
pages = {11363--11372},
number = {36},
abstract = {Somatic and dendritic whole-cell recording was used to examine action
potential (AP) initiation and propagation in layer 5 pyramidal neurons
of the rat prelimbic prefrontal cortex. APs generated by somatic
current injection, or via antidromic stimulation, were reliably recorded
at apical dendritic locations as far as 480 microm from the soma.
Although the backpropagation of single APs into the apical dendrite
was robust, frequency-dependent attenuation was observed during AP
trains delivered at 10-100 Hz. APs were usually initiated close to
the soma (presumably in the axon); however, strong depolarizing input
to the apical dendrite could generate dendritic spikes that preceded
somatic APs. AP backpropagation was dependent solely on activation
of dendritic voltage-gated sodium channels and did not require activation
of dendritic calcium channels. Despite not playing a role in AP backpropagation,
calcium-imaging experiments demonstrated that dendritic calcium channels
are activated by backpropagating APs, leading to transient increases
in intracellular calcium. In addition, calcium imaging revealed that
AP backpropagation into the distal apical tuft was frequency dependent.
Finally, we tested whether dopamine, a prominent neuromodulator associated
with prefrontal activity, could alter AP initiation or backpropagation.
Bath-applied dopamine (10 or 100 microm) did not effect AP backpropagation,
frequency-dependent depression, local dendritic spike initiation,
or AP-induced calcium signaling. These data indicate that AP backpropagation
in prefrontal layer 5 pyramidal neurons is robust but frequency dependent
in the distal tuft, requires dendritic sodium rather than calcium
channel activation, and, unlike other aspects of neuronal excitability,
insensitive to modulation by dopamine.},
address = {Division of Neuroscience, John Curtin School of Medical Research,
Australian National University, Canberra, ACT 0200, Australia.},
au = {Gulledge, AT and Stuart, GJ},
da = {20031215},
date-added = {2008-03-28 17:06:03 +0100},
date-modified = {2008-03-28 17:06:12 +0100},
dcom = {20040116},
edat = {2003/12/16 05:00},
issn = {1529-2401 (Electronic)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20061115},
mh = {*Action Potentials; Animals; Calcium/metabolism; Calcium Channels/physiology;
Cells, Cultured; Dendrites/physiology; Dopamine/*pharmacology; Kinetics;
Patch-Clamp Techniques; Prefrontal Cortex/cytology/*physiology; Pyramidal
Cells/drug effects/metabolism/*physiology; Rats; Rats, Wistar; Sodium
Channels/physiology},
mhda = {2004/01/17 05:00},
own = {NLM},
owner = {sprekeler},
pii = {23/36/11363},
pl = {United States},
pmid = {14673000},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, Non-P.H.S.},
pubm = {Print},
rn = {0 (Calcium Channels); 0 (Sodium Channels); 51-61-6 (Dopamine); 7440-70-2
(Calcium)},
sb = {IM},
so = {J Neurosci. 2003 Dec 10;23(36):11363-72. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Gupta00,
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synapses in the neocortex},
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year = {2000},
volume = {287},
pages = {273-278}
}
@article{Gustafsson87,
author = {B. Gustafsson and H. Wigstrom and W. C. Abraham and Y.-Y. Huang},
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}
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journal = {Science},
year = {2002},
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pages = {1556-1559}
}
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year = {2006},
volume = {9},
pages = {420--428},
number = {3},
month = mar,
comment = {10.1038/nn1643},
issn = {1097-6256},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1038/nn1643}
}
@article{Gutkin98,
author = {B. Gutkin and G. B. Ermentrout},
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year = {1998},
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pages = {1047-1065}
}
@article{Gutkin05,
author = {Boris S. Gutkin and G. Bard Ermentrout and Alex D. Reyes},
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year = {2005},
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}
@book{Guyon93,
title = {Advances in Pattern Recognition Systems using Neural Networks},
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year = {1993},
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address = {Singapore}
}
@article{Guyonneau05,
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title = {Neurons Tune to the Earliest Spikes Through STDP},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {859--879},
number = {4},
keywords = {plasticity},
owner = {sprekeler},
publisher = {MIT Press},
timestamp = {2008.04.14}
}
@article{Guetig03,
author = {R. G{\"u}tig and R. Aharonov and S. Rotter and H. Sompolinsky},
title = {Learning input correlations through non-linear temporally asymmetric
Hebbian plasticity},
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year = {2003},
volume = {23},
pages = {3697-3714}
}
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{Hebbian} Plasticity},
journal = {Journal of Neuroscience},
year = {2003},
volume = {23},
pages = {3697--3714},
number = {9},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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year = {1993},
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pages = {636-642},
annote = {Hasler - paper cited in grant proposal}
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year = {1996},
volume = {166},
pages = {363-390},
note = {Russian Journal, but article in English},
annote = {Hasler citation}
}
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year = {1998},
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pages = {555-565}
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@article{Hafting05,
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year = {2005},
volume = {436},
pages = {801--806},
number = {7052},
month = aug,
dateother = {print},
day = {11},
issn = {0028-0836},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1038/nature03721}
}
@article{Hahnloser03,
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title = {Emergence of Neural Integration in the Head-Direction System by visual
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journal = {Neuroscience},
year = {2003},
volume = {120},
pages = {877--891},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Haken88,
title = {Neural and Synergetic Computers},
publisher = {Springer},
year = {1988},
author = {H. Haken},
address = {Berlin}
}
@book{Haken83,
title = {Synergetics, An Introduction},
publisher = {Springer},
year = {1983},
author = {Hermann Haken},
series = {Springer Series in Synergetics},
keywords = {Various-Artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Hale91,
title = {Dynamics and Bifurcations},
publisher = {Springer},
year = {1991},
author = {Jack K. Hale and H{"u}sgen Ko\c{c}ac},
number = {3},
series = {Text in Applied Mathematics},
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bibliothek = {A.12, 122},
isbn = {ISBN 3-540-97141-6}
}
@book{Hall03,
title = {Lie Groups, Lie Algebras, and Representations},
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year = {2003},
author = {Brian C. Hall},
keywords = {Lie groups, Various-Artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Han07,
author = {Han, X. and Boyden, E.S.},
title = {{Multiple-color optical activation, silencing, and desynchronization
of neural activity, with single-spike temporal resolution}},
journal = {PLoS ONE},
year = {2007},
volume = {2},
pages = {e299},
number = {3},
keywords = {various-artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {Yanan Han and H. Steven Colburn },
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year = {1993},
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pages = {115--130}
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journal = {Journal of Neuroscience},
year = {2001},
volume = {21},
pages = {4490--4497},
number = {12},
month = jun,
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hansel03,
author = {D. Hansel and G. Mato},
title = {Asynchronous states and the emergence of synchrony in large networks
of interacting excitatory and inhibitory},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {1-56}
}
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year = {2001},
volume = {86},
pages = {4175-4178}
}
@article{Hansel95,
author = {D. Hansel and G. Mato and C. Meunier},
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journal = {Neural Comput.},
year = {1995},
volume = {7},
pages = {307-337}
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@article{Hansel92,
author = {D. Hansel and H. Sompolinski},
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year = {1992},
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pages = {718--721}
}
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author = {D. Hansel and H. Sompolinsky},
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journal = {J. Comput. Neurosci.},
year = {1996},
volume = {3},
pages = {7-34}
}
@book{NIPS93,
title = {Advances in Neural Information Processing Systems},
publisher = {Morgan Kaufmann Publishers},
year = {1993},
author = {S. J. Hanson},
volume = {5},
address = {San Mateo}
}
@article{Hargreaves05,
author = {E. Hargreaves and G. Rao and I. Lee and J. Knierim},
title = {Major Dissociation Between Medial and Lateral Entorhinal Input to
Dorsal Hippocampus},
journal = {Science},
year = {2005},
volume = {308},
pages = {1792--1794},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Harmeling03,
author = {Harmeling, Stefan and Ziehe, Andreas and Kawanabe, Motoaki and M{\"u}ller,
Klaus-Robert},
title = {Kernel-Based Nonlinear Blind Source Separation},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {1089--1124},
keywords = {ICA},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Harris08,
author = {Harris, K.D.},
title = {{Stability of the fittest: Organizing learning through retroaxonal
signals}},
journal = {Trends in Neurosciences},
year = {2008},
pages = {130--136},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@article{Harris08a,
author = {Harris, Kenneth D.},
title = {Stability of the fittest: organizing learning through retroaxonal
signals},
journal = {Trends in Neurosciences},
year = {2008},
volume = {31},
pages = {130--136},
number = {3},
month = mar,
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.sciencedirect.com/science/article/B6T0V-4RS3TBR-1/2/6872c4361a0d59592c233546a6bad2e9}
}
@article{Hartley05,
author = {T. Hartley and N. Burgess},
title = {Complementary Memory systems: competition, cooperation and compensation},
journal = {Trends n Neurosciences},
year = {2005},
volume = {28},
pages = {169--170},
number = {4},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hartline40,
author = {H. K. Hartline},
title = {Rhe receptive fields of optic nerve fibers},
journal = {Am. J. Physiol.},
year = {1940},
volume = {130},
pages = {690-699}
}
@article{Hartmann95,
author = {G. Hartmann and R. Wehner},
title = {The ant's path integration system: a neural architecture},
journal = {Biol. Cybern.},
year = {1995},
volume = {73},
pages = {483-497}
}
@article{Harvey07,
author = {Christopher D Harvey and Karel Svoboda},
title = {Locally dynamic synaptic learning rules in pyramidal neuron dendrites.},
journal = {Nature},
year = {2007},
volume = {450},
pages = {1195--1200},
number = {7173},
month = {Dec},
abstract = {Long-term potentiation (LTP) of synaptic transmission underlies aspects
of learning and memory. LTP is input-specific at the level of individual
synapses, but neural network models predict interactions between
plasticity at nearby synapses. Here we show in mouse hippocampal
pyramidal cells that LTP at individual synapses reduces the threshold
for potentiation at neighbouring synapses. After input-specific LTP
induction by two-photon glutamate uncaging or by synaptic stimulation,
subthreshold stimuli, which by themselves were too weak to trigger
LTP, caused robust LTP and spine enlargement at neighbouring spines.
Furthermore, LTP induction broadened the presynaptic-postsynaptic
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keywords = {Plasticity},
owner = {sprekeler},
pii = {nature06416},
pmid = {18097401},
timestamp = {2008.04.23},
url = {http://dx.doi.org/10.1038/nature06416}
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address = {Wolfson Institute for Biomedical Research and Department of Physiology,
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da = {20030709},
date-added = {2008-03-30 22:46:40 +0200},
date-modified = {2008-03-30 22:46:43 +0200},
dcom = {20030902},
edat = {2003/07/10 05:00},
issn = {0959-4388 (Print)},
jid = {9111376},
jt = {Current opinion in neurobiology},
language = {eng},
lr = {20061115},
mh = {Action Potentials/*physiology; Animals; Brain/physiology; Dendrites/*physiology;
Humans; Neurons/physiology; Synapses/physiology},
mhda = {2003/09/03 05:00},
own = {NLM},
owner = {sprekeler},
pii = {S0959438803000758},
pl = {England},
pmid = {12850223},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Review},
pubm = {Print},
rf = {123},
sb = {IM},
so = {Curr Opin Neurobiol. 2003 Jun;13(3):372-83. },
stat = {MEDLINE},
timestamp = {2008.04.14}
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dcom = {20061018},
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edat = {2006/10/07 09:00},
issn = {1095-9203 (Electronic)},
jid = {0404511},
jt = {Science (New York, N.Y.)},
keywords = {Neuronal-Processing},
language = {eng},
lr = {20070319},
mhda = {2006/10/19 09:00},
own = {NLM},
owner = {sprekeler},
pii = {314/5796/80},
pl = {United States},
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pt = {Journal Article; Review},
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journal = {Proc. Natl. Acad. Sci. USA},
year = {1984},
volume = {81},
pages = {3088--3092}
}
@article{Hopfield82,
author = {J. J. Hopfield},
title = {Neural networks and physical systems with emergent collective computational
abilities},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1982},
volume = {79},
pages = {2554--2558}
}
@article{Hopfield04,
author = {J. J. Hopfield and C. D. Brody},
title = {Learning rules and network repair in spike-timing-based computation
networks},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2004},
volume = {101},
pages = {337-342}
}
@article{Hopfield95,
author = {J. J. Hopfield and A. V. M. Herz},
title = {Rapid local synchronization of action potentials: towards computation
with coupled integrate-and-fire networks},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1995},
volume = {92},
pages = {6655}
}
@article{Hopp83,
author = {S.L. Hopp and W. Timberlake},
title = {Odor cue determinants of urine marking in male rats (Rattus norvegicus).},
journal = {Behavioral Neural Biology},
year = {1983},
volume = {37},
pages = {162--72},
number = {1},
keywords = {Various-Artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Hoppensteadt97,
title = {Weakly connected neural networks},
publisher = {Springer},
year = {1997},
author = {Frank C. Hoppensteadt and Eugene M. Izhikevich}
}
@article{Hori05,
author = {E. Hori and Y. Nishio and K. Kazui and K. Umeno and E. Tabuchi and
K. Sasaki and S. Endo and T. Ono and H. Nishijo},
title = {Place-related neural responses in the monkey hippocampal formation
in a virtual space},
journal = {Hippocampus},
year = {2005},
volume = {15},
pages = {991--996},
number = {8},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@incollection{Horn99,
author = {D. Horn and N. Levu and I. Meilijison and E. Ruppin},
title = {Distributed synchrony of spiking neurons in a Hebbian Cell assembly},
booktitle = {Advances in Neural Information Processing 12},
publisher = {MIT-Press},
year = {2000},
volume = {12},
pages = {to appear}
}
@inproceedings{Horn00,
author = {Horn, D. and Levy, N. and Meilijson, I. and Ruppin, E.},
title = {Distributed Synchrony of Spiking Neurons in a {H}ebbian Cell Assembly},
booktitle = {Advances in Neural Information Processing Systems 12},
year = {2000},
editor = {S. A. Solla and T. K. Leen and K.-R. M{\"u}ller},
pages = {129--135},
publisher = {MIT Press},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Horn98,
author = {D. Horn and N. Levy and E. Ruppin},
title = {Memory maintenance via neuronal regulation},
journal = {Neural Computation},
year = {1998},
volume = {10},
pages = {1-18}
}
@article{Horn98a,
author = {D. Horn and S, Levanda},
title = {Fast temporal encoding and decoding with spiking neurons},
journal = {Neural Comput.},
year = {1998},
volume = {10},
pages = {1705-1720}
}
@article{Horn91b,
author = {D. Horn and D. Sagi},
title = {Parallel activation of memories in an oscillatory neural network.},
journal = {Neural Computation},
year = {1991},
volume = {3},
pages = {31-43}
}
@article{Horn91a,
author = {D. Horn and D. Sagi and M. Usher},
title = {Segmentation, binding, and illusory conjunctions.},
journal = {Neural Computation},
year = {1991},
volume = {3},
pages = {510--525}
}
@inproceedings{Horn92,
author = {D. Horn and M. Usher},
title = {Oscillatory model of short term memory.},
booktitle = {Advances in Neural Information Processing Systems},
year = {1992},
editor = {J. E. Moody and S. J. Hanson and R. P. Lippmann},
volume = {4},
pages = {125--132},
address = {San Mateo CA},
publisher = {Morgan Kaufmann}
}
@article{Horn89,
author = {D. Horn and M. Usher},
title = {Neural networks with dynamical thresholds.},
journal = {Phys. Rev. A},
year = {1989},
volume = {40},
pages = {1036--1040}
}
@book{Horn85,
title = {Matrix analysis},
publisher = {Cambridge University Press},
year = {1985},
author = {R. A. Horn and C. R. Johnson},
address = {Cambridge, UK}
}
@article{Horton05,
author = {Horton, J.C. and Adams, D.L.},
title = {{The cortical column: a structure without a function}},
journal = {Philosophical Transactions: Biological Sciences},
year = {2005},
volume = {360},
pages = {837--862},
number = {1456},
keywords = {various-artists,vision},
owner = {sprekeler},
publisher = {The Royal Society},
timestamp = {2008.04.14}
}
@article{Hosoya05,
author = {Hosoya, Toshihiko and Baccus, Stephen A. and Meister, Markus},
title = {Dynamic predictive coding by the retina},
journal = {Nature},
year = {2005},
volume = {436},
pages = {71--77},
number = {7047},
month = jul,
comment = {10.1038/nature03689},
issn = {0028-0836},
keywords = {Optimal-Coding, vision, vision-physiology},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1038/nature03689}
}
@incollection{Houk95,
author = {J. Houk and J. Adams and A. Barto},
title = {A model of how the basal ganglia generate and use neural signals
that predict reinforcement},
booktitle = {Models on Information Processing in the Basal Ganglia},
publisher = {MIT Press},
year = {1995},
editor = {J. C. Houk and J. L. Davis and D. G. Beiser},
pages = {249-270},
address = {Cambridge}
}
@article{Hubel68,
author = {D. Hubel and T. Wiesel},
title = {Receptive Fields and Functional Architecture of Monkey Striate Cortex},
journal = {Journal of Physiology},
year = {1968},
volume = {195},
pages = {215--243},
keywords = {vision, Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hubel65,
author = {D. Hubel and T. Wiesel},
title = {Binocular interaction in striate cortex of kittens reared with artificial
squint},
journal = {The Journal of Neurophysiology},
year = {1965},
volume = {28},
pages = {1041-1059},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hubel63,
author = {Hubel, D.H. and Wiesel, T.N.},
title = {RECEPTIVE FIELDS OF CELLS IN STRIATE CORTEX OF VERY YOUNG, VISUALLY
INEXPERIENCED KITTENS},
journal = {Journal of Neurophysiology},
year = {1963},
volume = {26},
pages = {994--1002},
number = {6},
keywords = {Vision, Vision-Physiology},
owner = {sprekeler},
publisher = {Am Physiological Soc},
timestamp = {2008.04.14}
}
@article{Hubel62a,
author = {Hubel, D.H. and Wiesel, T.N.},
title = {{Receptive fields, binocular interaction and functional architecture
in the cat's visual cortex}},
journal = {Journal of Physiology},
year = {1962},
volume = {160},
pages = {106--154},
number = {1},
keywords = {vision, Vision-Physiology},
owner = {sprekeler},
publisher = {London},
timestamp = {2008.04.14}
}
@article{Hubel62b,
author = {D. Hubel and T. Wiesel},
title = {Receptive fields, binocular interaction, and functional architecture
in the cat's visual cortex},
journal = {The Journal of Physiology (London)},
year = {1962},
volume = {160},
pages = {106-154},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Hubel88,
title = {Eye, brain, and vision},
publisher = {W. H. Freeman},
year = {1988},
author = {David H. Hubel},
address = {New York}
}
@article{Hubel77,
author = {D. H. Hubel and T. N. Wiesel},
title = {Functional architecture of macaque monkey visual cortex},
journal = {Proc.~R.~Soc.~B},
year = {1977},
volume = {198},
pages = {1-59}
}
@article{Hubel62,
author = {D. H. Hubel and T. N. Wiesel},
title = {Receptive fields, binocular interaction and functional architecture
in the cat's visual cortex.},
journal = {J. Physiol. (London)},
year = {1962},
volume = {160},
pages = {106-154}
}
@article{Hubel59,
author = {D. H. Hubel and T. N. Wiesel},
title = {Receptive fields of single neurons in the cat's striate cortex},
journal = {J. Physiol.},
year = {1959},
volume = {148},
pages = {574-591}
}
@incollection{Hughes77,
author = {A. Hughes},
title = {The Visual System in Vertebrates},
booktitle = {The Handbook of Sensory Physiology},
publisher = {Springer},
year = {1977},
editor = {F. Crescitelli},
volume = {Vol VI1/5},
pages = {615--756},
address = {Berlin},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hughes78,
author = {A. Hughes},
title = {{A schematic eye for the rat}},
journal = {Vision Research},
year = {1978},
volume = {19},
pages = {569--588},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Humphreys94,
title = {Introduction to Lie algebras and representation theory},
publisher = {Springer},
year = {1994},
author = {James E. Humphreys},
keywords = {Lie groups},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Humphries07,
author = {Humphries, Mark D and Gurney, Kevin},
title = {Solution methods for a new class of simple model neurons.},
journal = {Neural Comput},
year = {2007},
volume = {19},
pages = {3216--3225},
number = {12},
abstract = {Izhikevich (2003) proposed a new canonical neuron model of spike generation.
The model was surprisingly simple yet able to accurately replicate
the firing patterns of different types of cortical cell. Here, we
derive a solution method that allows efficient simulation of the
model.},
address = {Adaptive Behaviour Research Group, Department of Psychology, University
of Sheffield, Sheffield S10 2TP, UK. m.d.humphries@sheffield.ac.uk},
au = {Humphries, MD and Gurney, K},
bdsk-url-1 = {http://dx.doi.org/10.1162/neco.2007.19.12.3216},
da = {20071031},
date-added = {2008-03-29 19:37:47 +0100},
date-modified = {2008-03-29 19:37:52 +0100},
dcom = {20071221},
doi = {10.1162/neco.2007.19.12.3216},
edat = {2007/11/01 09:00},
issn = {0899-7667 (Print)},
jid = {9426182},
jt = {Neural computation},
language = {eng},
mh = {Action Potentials/*physiology; Algorithms; Animals; Cell Membrane/physiology;
Cerebral Cortex/*physiology; Humans; Models, Neurological; Nerve
Net/physiology; Neural Networks (Computer); Neural Pathways/*physiology;
Neurons/*physiology; Synaptic Transmission/physiology},
mhda = {2007/12/22 09:00},
own = {NLM},
owner = {sprekeler},
pl = {United States},
pmid = {17970650},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {Neural Comput. 2007 Dec;19(12):3216-25. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Hung05,
author = {C.P. Hung and G. Kreiman and T. Poggio and J.J. {DiCarlo}},
title = {Fast Readout of Object Identity from Macaque Inferior Temporal Cortex},
journal = {Science},
year = {2005},
volume = {310},
pages = {863 - 866}
}
@book{Hutter04,
title = {Universal Artificial Intelligence: Sequential Decisions based on
Algorithmic Probability},
publisher = {Springer},
year = {2004},
author = {M. Hutter},
address = {Berlin},
note = {(On J. Schmidhuber's SNF grant 20-61847)}
}
@article{Huys06,
author = {Huys, Quentin J M and Ahrens, Misha B and Paninski, Liam},
title = {Efficient estimation of detailed single-neuron models.},
journal = {J Neurophysiol},
year = {2006},
volume = {96},
pages = {872--890},
number = {2},
abstract = {Biophysically accurate multicompartmental models of individual neurons
have significantly advanced our understanding of the input-output
function of single cells. These models depend on a large number of
parameters that are difficult to estimate. In practice, they are
often hand-tuned to match measured physiological behaviors, thus
raising questions of identifiability and interpretability. We propose
a statistical approach to the automatic estimation of various biologically
relevant parameters, including 1) the distribution of channel densities,
2) the spatiotemporal pattern of synaptic input, and 3) axial resistances
across extended dendrites. Recent experimental advances, notably
in voltage-sensitive imaging, motivate us to assume access to: i)
the spatiotemporal voltage signal in the dendrite and ii) an approximate
description of the channel kinetics of interest. We show here that,
given i and ii, parameters 1-3 can be inferred simultaneously by
nonnegative linear regression; that this optimization problem possesses
a unique solution and is guaranteed to converge despite the large
number of parameters and their complex nonlinear interaction; and
that standard optimization algorithms efficiently reach this optimum
with modest computational and data requirements. We demonstrate that
the method leads to accurate estimations on a wide variety of challenging
model data sets that include up to about 10(4) parameters (roughly
two orders of magnitude more than previously feasible) and describe
how the method gives insights into the functional interaction of
groups of channels.},
address = {Gatsby Computational Neuroscience Unit, University College London,
UK. qhuys.ahrens@gatsby.ucl.ac.uk},
au = {Huys, QJ and Ahrens, MB and Paninski, L},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00079.2006},
da = {20060712},
date-added = {2008-03-30 22:16:28 +0200},
date-modified = {2008-03-30 22:19:13 +0200},
dcom = {20061108},
dep = {20060419},
doi = {10.1152/jn.00079.2006},
edat = {2006/04/21 09:00},
issn = {0022-3077 (Print)},
jid = {0375404},
jt = {Journal of neurophysiology},
language = {eng},
lr = {20061115},
mh = {Algorithms; Biophysics; Cell Membrane/physiology; Data Interpretation,
Statistical; Dendrites/physiology; Electrophysiology; Ion Channel
Gating/physiology; Ion Channels; Kinetics; Ligands; Likelihood Functions;
*Models, Neurological; Monte Carlo Method; Neurons/*physiology; Patch-Clamp
Techniques; Receptors, N-Methyl-D-Aspartate/physiology; Synapses/physiology},
mhda = {2006/11/10 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2006/04/19 {$[$}aheadofprint{$]$}; 2006/04/26 {$[$}aheadofprint{$]$}},
pii = {00079.2006},
pl = {United States},
pmid = {16624998},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print-Electronic},
rn = {0 (Ion Channels); 0 (Ligands); 0 (Receptors, N-Methyl-D-Aspartate)},
sb = {IM},
so = {J Neurophysiol. 2006 Aug;96(2):872-90. Epub 2006 Apr 19. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@book{Hyvarinen01,
title = {Independent Component Analysis},
publisher = {Wiley},
year = {2001},
author = {A. Hyv\protect{\"a}rinen and J. Karhunen and E. Oja },
citeulike-article-id = {2377255},
keywords = {nn},
priority = {2}
}
@article{Hyvarinen00,
author = {A. Hyv\protect{\"a}rinen and E. Oja},
title = {Independent Component Analysis: algorithms and applications},
journal = {Neural Networks},
year = {2000},
volume = {13},
pages = {411--430},
number = {4--5}
}
@article{Hyvarinen99a,
author = {A. Hyv{\"a}rinen},
title = {Fast and robust fixed-point algorithms for independent component
analysis},
journal = {IEEE Transactions on Neural Networks},
year = {1999},
volume = {10},
pages = {626--634},
keywords = {ICA},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hyvarinen99b,
author = {Aapo Hyv{\"a}rinen},
title = {Survey on Independent Component Analysis},
journal = {Neural Computing Surveys},
year = {1999},
volume = {2},
pages = {94-128},
keywords = {ICA},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Hyvarinen01a,
title = {{Independent Component Analysis}},
publisher = {Wiley, New York},
year = {2001},
author = {Hyv{\"a}rinen, A. and Karhunen, J. and Oja, E.},
keywords = {ICA},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hyvarinen99,
author = {Hyv{\"a}rinen, A. and Pajunen, P.},
title = {Nonlinear independent component analysis: Existence and uniqueness
results},
journal = {Neural Networks},
year = {1999},
volume = {12},
pages = {429--439},
number = {3},
keywords = {ICA, optimal-coding},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@inproceedings{Haefliger97,
author = {Philipp H{\"a}fliger and Misha Mahowald and Lloyd Watts},
title = {A Spike Based Learning Neuron in Analog VLSI},
booktitle = {Advances in Neural Information Processing Systems},
year = {1997},
editor = {Michael C. Mozer and Michael I. Jordan and Thomas Petsche},
volume = {9},
pages = {692-698},
publisher = {The {MIT} Press}
}
@article{Hausser01,
author = {Michael H{\"a}usser and Guy Major and Greg J. Stuart},
title = {Differential Shunting of EPSPs by Action Potentials},
journal = {Science},
year = {2001},
volume = {291},
pages = {138--141},
keywords = {Neuronal-Processing},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hausser03,
author = {Michael H{\"a}usser and Bartlett Mel},
title = {Dendrites: Bug or Feature?},
journal = {Current Opinion in Neurobiology},
year = {2003},
volume = {13},
pages = {372--282},
keywords = {Neuronal-Processing},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Holscher05,
author = {C. H{\"o}lscher and A. Schnee and H. Dahmen and L. Setia and H.A.
Mallot},
title = {Rats are able to navigate in virtual environments},
journal = {Journal of Experimental Biology},
year = {2005},
volume = {208},
pages = {561--569},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Ibata08,
author = {Keiji Ibata and Qian Sun and Gina G Turrigiano},
title = {Rapid synaptic scaling induced by changes in postsynaptic firing.},
journal = {Neuron},
year = {2008},
volume = {57},
pages = {819--826},
number = {6},
month = {Mar},
abstract = {Homeostatic synaptic scaling adjusts a neuron's excitatory synaptic
strengths up or down to compensate for perturbations in activity.
Little is known about the molecular pathway(s) involved, nor is it
clear which aspect of "activity"-local synaptic signaling, postsynaptic
firing, or large-scale changes in network activity-is required to
induce synaptic scaling. Here, we selectively block either postsynaptic
firing in individual neurons or a fraction of presynaptic inputs,
while optically monitoring changes in synaptic strength. We find
that synaptic scaling is rapidly induced by block of postsynaptic
firing, but not by local synaptic blockade, and is mediated through
a drop in somatic calcium influx, reduced activation of CaMKIV, and
an increase in transcription. Cortical neurons thus homeostatically
adjust synaptic strengths in response to changes in their own firing
rate, a mechanism with the computational advantage of efficiently
normalizing synaptic strengths without interfering with synapse-specific
mechanisms of information storage.},
doi = {10.1016/j.neuron.2008.02.031},
keywords = {plasticity},
owner = {sprekeler},
pii = {S0896-6273(08)00213-4},
pmid = {18367083},
timestamp = {2008.04.15},
url = {http://dx.doi.org/10.1016/j.neuron.2008.02.031}
}
@article{Idiart93,
author = {M. A. P. Idiart and L. F. Abbott},
title = {Propagation of excitation in neural network models},
journal = {Network},
year = {1993},
volume = {4},
pages = {285-294}
}
@article{Ikegaya04,
author = {Y. Ikegaya and G. Aaron and D. Aranov and I. Lampl and D. Ferster
and R. Yuste},
title = { Synfire chains and cortical songs: Temporal modules of cortical
activity },
journal = {Science},
year = {2004},
volume = {304},
pages = {559-564}
}
@article{Intrator92,
author = {N. Intrator and L.N. Cooper},
title = {Objective function formulation of the bcm theory of visual cortical
plasticity - statistical connections, stability conditions},
journal = {Neural Networks},
year = {1992},
volume = {5},
pages = {3-17}
}
@article{Isaac95,
author = {J. T.R. Isaac and R. A. Nicoll and R. C. Malenka},
title = {Evidence for silent synapses: Implications for the expression of
LTP},
journal = {Neuron},
year = {1995},
volume = {15},
pages = {427-434}
}
@article{Ito95,
author = {Ito, M. and Tamura, H. and Fujita, I. and Tanaka, K.},
title = {Size and position invariance of neuronal responses in monkey inferotemporal
cortex},
journal = {Journal of Neurophysiology},
year = {1995},
volume = {73},
pages = {218-226},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Izhikevich07,
author = {E.M. Izhikevich},
title = {Solving the Distal Reward Problem through Linkage of STDP and Dopamine
Signaling},
journal = {Cerebral Cortex},
year = {2007},
volume = {17},
pages = {2443-2452}
}
@article{Izhikevich07a,
author = {Izhikevich, E.M.},
title = {{Solving the Distal Reward Problem through Linkage of STDP and Dopamine
Signaling}},
journal = {Cerebral Cortex},
year = {2007},
volume = {17},
pages = {2443--2452},
number = {10},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Oxford Univ Press},
timestamp = {2008.04.14}
}
@article{Izhikevich05,
author = {Izhikevich, E.M.},
title = {{Polychronization: Computation with Spikes}},
journal = {Neural Computation},
year = {2005},
volume = {18},
pages = {245--282},
number = {2},
owner = {sprekeler},
publisher = {MIT Press},
timestamp = {2008.04.14}
}
@article{Izhikevich04,
author = {E.M. Izhikevich},
title = {Which Model to Use for Cortical Spiking Neurons?},
journal = {IEEE Transactions on Neural Networks},
year = {2004},
volume = {15},
pages = {1063-1070}
}
@article{Izhikevich03,
author = {E.M. Izhikevich},
title = {Simple model of spiking neurons},
journal = {IEEE Transactions on Neural Networks},
year = {2003},
volume = {14},
pages = {1569-1572}
}
@article{Izhikevich-add01,
author = {E.M. Izhikevich},
title = {Synchronization of elliptic bursters},
journal = {SIAM Review},
year = {2001},
volume = {43},
pages = {315-344}
}
@article{Izhikevich01,
author = {E.M. Izhikevich},
title = {Resonate-and-fire neurons},
journal = {Neural Networks},
year = {2001},
volume = {14},
pages = {883-894}
}
@article{Izhikevich00,
author = {E. Izhikevich},
title = {Neural excitability, spiking, and bursting},
journal = {Int. J. of Bif. and Chaos},
year = {2000},
volume = {10},
pages = {1171-1266}
}
@article{Izhikevich99,
author = {E. Izhikevich},
title = {Class 1 neural excitability, conventional synapses, weakly connected
networks, and mathematical foundations of pulse-coupled models},
journal = {IEEE Transactions on Neural Networks},
year = {1999},
volume = {10},
pages = {499-507}
}
@article{Izhikevich03a,
author = {E.M. Izhikevich and N.S. Desai},
title = {Relating STDP to BCM},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {1511-1523}
}
@book{Izhikevich07b,
title = {Dynamical systems in neuroscience : the geometry of excitability
and bursting },
publisher = {MIT Press},
year = {2007},
author = {Izhikevich, Eugene M},
address = {Cambridge, Mass. },
call-number = {QP355.2},
date-added = {2007-12-12 22:03:04 +0100},
date-modified = {2007-12-12 22:03:07 +0100},
dewey-call-number = {612.8},
genre = {Neurology},
isbn = {0262090430 (alk. paper)},
library-id = {2006040349},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Izhikevich03b,
author = {Izhikevich, E M},
title = {Simple model of spiking neurons.},
journal = {IEEE Trans Neural Netw},
year = {2003},
volume = {14},
pages = {1569--1572},
number = {6},
abstract = {A model is presented that reproduces spiking and bursting behavior
of known types of cortical neurons. The model combines the biologically
plausibility of Hodgkin-Huxley-type dynamics and the computational
efficiency of integrate-and-fire neurons. Using this model, one can
simulate tens of thousands of spiking cortical neurons in real time
(1 ms resolution) using a desktop PC.},
address = {The Neurosciences Inst., San Diego, CA, USA.},
au = {Izhikevich, EM},
bdsk-url-1 = {http://dx.doi.org/10.1109/TNN.2003.820440},
da = {20080204},
date-added = {2008-03-14 12:54:20 +0100},
date-modified = {2008-03-14 12:54:30 +0100},
doi = {10.1109/TNN.2003.820440},
edat = {2008/02/05 09:00},
issn = {1045-9227 (Print)},
jid = {101211035},
jt = {IEEE transactions on neural networks / a publication of the IEEE Neural
Networks Council},
language = {eng},
mhda = {2008/02/05 09:00},
own = {NLM},
owner = {sprekeler},
pl = {United States},
pmid = {18244602},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
so = {IEEE Trans Neural Netw. 2003;14(6):1569-72. },
stat = {In-Data-Review},
timestamp = {2008.04.14}
}
@article{Izhikevich08,
author = {Izhikevich, Eugene M. and Edelman, Gerald M.},
title = {{Large-scale model of mammalian thalamocortical systems}},
journal = {Proceedings of the National Academy of Sciences},
year = {2008},
pages = {0712231105},
abstract = {The understanding of the structural and dynamic complexity of mammalian
brains is greatly facilitated by computer simulations. We present
here a detailed large-scale thalamocortical model based on experimental
measures in several mammalian species. The model spans three anatomical
scales. (i) It is based on global (white-matter) thalamocortical
anatomy obtained by means of diffusion tensor imaging (DTI) of a
human brain. (ii) It includes multiple thalamic nuclei and six-layered
cortical microcircuitry based on in vitro labeling and three-dimensional
reconstruction of single neurons of cat visual cortex. (iii) It has
22 basic types of neurons with appropriate laminar distribution of
their branching dendritic trees. The model simulates one million
multicompartmental spiking neurons calibrated to reproduce known
types of responses recorded in vitro in rats. It has almost half
a billion synapses with appropriate receptor kinetics, short-term
plasticity, and long-term dendriticspike-timing-dependent synaptic
plasticity (dendritic STDP). The model exhibits behavioral regimes
of normal brain activity that were not explicitly built-in but emerged
spontaneously as the result of interactions among anatomical and
dynamic processes. We describe spontaneous activity, sensitivity
to changes in individual neurons, emergence of waves and rhythms,
and functional connectivity on different scales.},
doi = {10.1073/pnas.0712231105},
eprint = {http://www.pnas.org/cgi/reprint/0712231105v1.pdf},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.pnas.org/cgi/content/abstract/0712231105v1}
}
@article{Okeefe93,
author = {J.{O'K}eefe and M. Recce},
title = {Phase relationship between hippocampal place units and the hippocampal
theta rhythm},
journal = {Hippocampus},
year = {1993},
volume = {3},
pages = {317-330}
}
@article{Jaakkola94,
author = {Jaakkola, T. and Jordan, M. I. and Singh, S. P.},
title = {On the Convergence of Stochastic Iterative Dynamic Programming Algorithms},
journal = {Neural Computation},
year = {1994},
volume = {6},
pages = {1185--1201},
number = {6},
month = {November},
owner = {sprekeler},
publisher = {MIT Press},
timestamp = {2008.04.14}
}
@book{Jack75,
title = {Electric current flow in excitable cells.},
publisher = {Clarendon Press},
year = {1975},
author = {J. J. B. Jack and D. Noble and R. W. Tsien},
address = {Oxford}
}
@book{Jackson62,
title = {Classical Electrodynamics},
publisher = {Wiley},
year = {1962},
author = {J.D. Jackson}
}
@article{JACOBS01,
author = {G. H. JACOBS and J. A. FENWICK and G. A. WILLIAMS},
title = {{CONE}-{BASED} {VISION} {OF} {RATS} {FOR} {ULTRAVIOLET} {AND} {VISIBLE}
{LIGHTS}},
journal = {Journal of Experimental Biology},
year = {2001},
volume = {204},
pages = {2439--2446},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Jaeger04,
author = {H. Jaeger and H. Haas},
title = {Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy
in Wireless Communication},
journal = {Science},
year = {2004},
volume = {304},
pages = {78--80}
}
@book{James90,
title = {Psychology (Briefer Course), ch. 16},
publisher = {Holt},
year = {1890},
author = {William James},
address = {New York},
annote = {reprinted in Anderson and Rosenfeld}
}
@article{Jazayeri06,
author = {M. Jazayeri and J. A. Movshon},
title = {Optimal representation of sensory information by neural populations},
journal = {Nature Neuroscience},
year = {2006},
volume = {9},
pages = {690-696},
number = {5},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Jeffery07,
author = {Kathryn J Jeffery},
title = {Self-localization and the entorhinal-hippocampal system.},
journal = {Curr Opin Neurobiol},
year = {2007},
volume = {17},
pages = {684--691},
number = {6},
month = {Dec},
abstract = {Self-localization requires that information from several sensory modalities
and knowledge domains be integrated in order to identify an environment
and determine current location and heading. This integration occurs
by the convergence of highly processed sensory information onto neural
systems in entorhinal cortex and hippocampus. Entorhinal neurons
combine angular and linear self-motion information to generate an
oriented metric signal that is then 'attached' to each environment
using information about landmarks and context. Neurons in hippocampus
use this signal to determine the animal's unique position within
a particular environment. Elucidating this process illuminates not
only spatial processing but also, more generally, how the brain builds
knowledge representations from inputs carrying heterogeneous sensory
and semantic content.},
doi = {10.1016/j.conb.2007.11.008},
keywords = {hippocampus},
owner = {sprekeler},
pii = {S0959-4388(07)00126-2},
pmid = {18249109},
timestamp = {2008.04.17},
url = {http://dx.doi.org/10.1016/j.conb.2007.11.008}
}
@article{Jeffery99,
author = {K. J. Jeffery and J. M. O'Keefe},
title = {Learned interaction of visual and idiothetic cues in the control
of place field orientation},
journal = {Experimental Brain Research},
year = {1999},
volume = {127},
pages = {151--161},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Jeffress48,
author = {L. A. Jeffress},
title = {A place theory of sound localisation},
journal = {J. Comp. Physiol. Psychol.},
year = {1948},
volume = {41},
pages = {35-39},
annote = {noch nicht gelesen, zitiert nach Carr+Konsihi 1990}
}
@article{Jensen96c,
author = {O. Jensen and J.E. Lisman},
title = {Hippocampal {\protect CA3} region predicts memory sequences: accounting
for the phase precession of place cells},
journal = {Learning and Memory},
year = {1996},
volume = {3},
pages = {279-287}
}
@article{Jodogne07,
author = {S. R. Jodogne and J. H. Piater},
title = {Closed-Loop Learning of Visual Control Policies},
journal = {J. Artificial Intelligence Research},
year = {2007},
volume = {28},
pages = {349-391}
}
@article{Joel02,
author = {Joel, D. and Niv, Y. and Ruppin, E.},
title = {{Actor--critic models of the basal ganglia: new anatomical and computational
perspectives}},
journal = {Neural Networks},
year = {2002},
volume = {15},
pages = {535--547},
number = {4-6},
publisher = {Elsevier}
}
@inproceedings{Johannesma68,
author = {P.I.M. Johannesma},
title = {Diffusion models of the stochastic acticity of neurons},
booktitle = {Neural Networks},
year = {1968},
pages = {116-144},
address = {Berlin},
publisher = {Springer},
comment = {A well written introduction to stochastic IF models, with a detailed
derivation of the diffusion approximation from Stein's model. Gives
hierarchy of equations for the moments of the ISI distribution, including
the MFPT also for reflecting boundary, shows that gamma dist.\ is
first order approx.\ to ISID.},
topic = {Stochastic models, Stein, diffusion model, integrate-and-fire, first
passage time, ISI}
}
@inproceedings{Johannesma86,
author = {P. Johannesma and A. Aertsen and H. van den Boogaad and J. Eggermont
and W. Epping},
title = {From synchrony to harmony: Ideas on the function of neural assemblies
and the interpretation of neural synchrony.},
booktitle = {Brain Theory},
year = {1986},
editor = {G. Palm and A. Aertsen},
pages = {25-47},
address = {Berlin Heidelberg New York},
publisher = {Springer--Verlag}
}
@article{Johansson04,
author = {R.S. Johansson and I. Birznieks},
title = {First spikes in ensembles of human tactile afferents code complex
spatial fingertip events},
journal = {Nature Neuroscience},
year = {2004},
volume = {7},
pages = {170-177}
}
@article{Johnson05,
author = {A Johnson and AD Redish},
title = {Hippocampal replay contributes to within session learning in a temporal
difference reinforcement learning model},
journal = {Neural Networks},
year = {2005},
volume = {18},
pages = {1163--1171},
number = {9}
}
@book{Joliffe86,
title = {Principal Component Analysis},
publisher = {Springer-Verlag,},
year = {1986},
author = {L.T. Joliffe},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Jolivet04c,
author = {Jolivet, Renaud and Gerstner, Wulfram},
title = {Predicting spike times of a detailed conductance-based neuron model
driven by stochastic spike arrival.},
journal = {J Physiol Paris},
year = {2004},
volume = {98},
pages = {442--451},
number = {4-6},
abstract = {Reduced models of neuronal activity such as integrate-and-fire models
allow a description of neuronal dynamics in simple, intuitive terms
and are easy to simulate numerically. We present a method to fit
an integrate-and-fire-type model of neuronal activity, namely a modified
version of the spike response model, to a detailed Hodgkin-Huxley-type
neuron model driven by stochastic spike arrival. In the Hogkin-Huxley
model, spike arrival at the synapse is modeled by a change of synaptic
conductance. For such conductance spike input, more than 70% of the
postsynaptic action potentials can be predicted with the correct
timing by the integrate-and-fire-type model. The modified spike response
model is based upon a linearized theory of conductance-driven integrate-and-fire
neurons.},
address = {School of Computer and Communication Sciences and Brain-Mind Institute,
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. renaud.jolivet@epfl.ch},
au = {Jolivet, R and Gerstner, W},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.jphysparis.2005.09.010},
da = {20051205},
date-added = {2008-03-28 12:02:03 +0100},
date-modified = {2008-03-28 12:02:25 +0100},
dcom = {20060217},
dep = {20051107},
doi = {10.1016/j.jphysparis.2005.09.010},
edat = {2005/11/09 09:00},
issn = {0928-4257 (Print)},
jid = {9309351},
jt = {Journal of physiology, Paris},
language = {eng},
lr = {20061115},
mh = {Action Potentials/*physiology; Animals; Computer Simulation; Excitatory
Postsynaptic Potentials/physiology; Humans; Mathematics; Membrane
Potentials/physiology; *Models, Neurological; *Neural Networks (Computer);
Neurons/*physiology; Stochastic Processes; Synapses/physiology; Time
Factors},
mhda = {2006/02/18 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2005/11/07 {$[$}aheadofprint{$]$}},
pii = {S0928-4257(05)00031-8},
pl = {France},
pmid = {16274972},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print-Electronic},
sb = {IM},
so = {J Physiol Paris. 2004 Jul-Nov;98(4-6):442-51. Epub 2005 Nov 7. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Jolivet05a,
author = {R. Jolivet and W. Gerstner},
title = {Predicting spike times of a detailed conductance- based neuron model
driven by stochastic spike arrival},
journal = {J. Physiol. Paris},
year = {2004},
volume = {98},
pages = {442-451}
}
@article{Jolivet07,
author = {Jolivet, R and Kobayashi, R and Rauch, A and Naud, R and Shinomoto,
S and Gerstner, W},
title = {A benchmark test for a quantitative assessment of simple neuron models.},
journal = {J Neurosci Methods},
year = {2007},
abstract = {Several methods and algorithms have recently been proposed that allow
for the systematic evaluation of simple neuron models from intracellular
or extracellular recordings. Models built in this way generate good
quantitative predictions of the future activity of neurons under
temporally structured current injection. It is, however, difficult
to compare the advantages of various models and algorithms since
each model is designed for a different set of data. Here, we report
about one of the first attempts to establish a benchmark test that
permits a systematic comparison of methods and performances in predicting
the activity of rat cortical pyramidal neurons. We present early
submissions to the benchmark test and discuss implications for the
design of future tests and simple neurons models.},
address = {Center for Psychiatric Neuroscience, University of Lausanne, 1015
Lausanne, Switzerland.},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.jneumeth.2007.11.006},
da = {20071227},
date-added = {2008-03-24 18:48:05 +0100},
date-modified = {2008-03-24 18:48:08 +0100},
dep = {20071119},
doi = {10.1016/j.jneumeth.2007.11.006},
edat = {2007/12/28 09:00},
issn = {0165-0270 (Print)},
jid = {7905558},
language = {ENG},
mhda = {2007/12/28 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2007/09/08 {$[$}received{$]$}; 2007/11/01 {$[$}revised{$]$}; 2007/11/10
{$[$}accepted{$]$}},
pii = {S0165-0270(07)00553-5},
pmid = {18160135},
pst = {aheadofprint},
pt = {JOURNAL ARTICLE},
pubm = {Print-Electronic},
so = {J Neurosci Methods. 2007 Nov 19;. },
stat = {Publisher},
timestamp = {2008.04.14}
}
@inproceedings{Jolivet03,
author = {R. Jolivet and T.J. Lewis and W. Gerstner},
title = {The Spike Response Model: A Framework to Predict Neuronal Spike Trains},
booktitle = {Proceedings of the Joint International Conference ICANN/ICONIP 2003},
year = {2003},
editor = {O. Kaynak and E. Alpaydin and E. Oja and L. Xu},
pages = {846-853},
address = {Heidelberg},
publisher = {Springer-Verlag}
}
@article{Jolivet04,
author = {R. Jolivet and T.J. Lewis and W. Gerstner},
title = {Generalized integrate-and-fire models of neuronal activity approximate
spike trains of a detailed model to a high degree of accuracy},
journal = {J. Neurophysiol.},
year = {2004},
volume = {92},
pages = {959-976}
}
@article{Jolivet04a,
author = {Jolivet, R. and Lewis, T.J. and Gerstner, W.},
title = {{Generalized Integrate-and-Fire Models of Neuronal Activity Approximate
Spike Trains of a Detailed Model to a High Degree of Accuracy}},
journal = {Journal of Neurophysiology},
year = {2004},
volume = {92},
pages = {959--976},
number = {2},
keywords = {neuronal-processing},
owner = {sprekeler},
publisher = {Am Physiological Soc},
timestamp = {2008.04.14}
}
@inproceedings{Jolivet06a,
author = {R. Jolivet and A. Rauch and H.-R. L\protect{\"u}scher and W. Gerstner},
title = {Integrate-and-Fire models with adaptation are good enough },
booktitle = {Advances in Neural Information Processing Systems 18},
year = {2006},
editor = {Y. Weiss and B. Schölkopf and J. Platt},
pages = {595-602},
publisher = {MIT Press Cambridge}
}
@article{Jolivet06,
author = {R. Jolivet and A. Rauch and H.-R. L\protect{\"u}scher and W. Gerstner},
title = {Predicting spike timing of neocortical pyramidal neurons by simple
threshold models},
journal = {J. Computational Neuroscience},
year = {2006},
volume = {21},
pages = {35-49 }
}
@article{Jolivet06b,
author = {Jolivet, Renaud and Rauch, Alexander and Luscher, Hans-Rudolf and
Gerstner, Wulfram},
title = {Predicting spike timing of neocortical pyramidal neurons by simple
threshold models.},
journal = {J Comput Neurosci},
year = {2006},
volume = {21},
pages = {35--49},
number = {1},
abstract = {Neurons generate spikes reliably with millisecond precision if driven
by a fluctuating current--is it then possible to predict the spike
timing knowing the input? We determined parameters of an adapting
threshold model using data recorded in vitro from 24 layer 5 pyramidal
neurons from rat somatosensory cortex, stimulated intracellularly
by a fluctuating current simulating synaptic bombardment in vivo.
The model generates output spikes whenever the membrane voltage (a
filtered version of the input current) reaches a dynamic threshold.
We find that for input currents with large fluctuation amplitude,
up to 75% of the spike times can be predicted with a precision of
+/-2 ms. Some of the intrinsic neuronal unreliability can be accounted
for by a noisy threshold mechanism. Our results suggest that, under
random current injection into the soma, (i) neuronal behavior in
the subthreshold regime can be well approximated by a simple linear
filter; and (ii) most of the nonlinearities are captured by a simple
threshold process.},
address = {Ecol Polytechnique Federale de Lausanne (EPFL), School of Computer
and Communication Sciences and Brain Mind Institute, Station 15,
CH-1015, Lausanne, Switzerland. renuad.jolivet@epfl.ch},
au = {Jolivet, R and Rauch, A and Luscher, HR and Gerstner, W},
bdsk-url-1 = {http://dx.doi.org/10.1007/s10827-006-7074-5},
da = {20060712},
date-added = {2007-12-05 18:22:01 +0100},
date-modified = {2007-12-05 18:22:08 +0100},
dcom = {20060919},
dep = {20060422},
doi = {10.1007/s10827-006-7074-5},
edat = {2006/04/25 09:00},
issn = {0929-5313 (Print)},
jid = {9439510},
jt = {Journal of computational neuroscience},
keywords = {Action Potentials/*physiology; Animals; Animals, Newborn; Differential
Threshold/*physiology; Female; Male; *Models, Neurological; Neural
Inhibition; Nonlinear Dynamics; Predictive Value of Tests; Probability;
Pyramidal Cells/*physiology; Rats; Rats, Wistar; Reaction Time/*physiology;
Reproducibility of Results; Somatosensory Cortex/*cytology; Time
Factors},
language = {eng},
lr = {20061115},
mhda = {2006/09/20 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2005/09/26 {$[$}received{$]$}; 2006/01/11 {$[$}accepted{$]$}; 2005/12/21
{$[$}revised{$]$}; 2006/04/22 {$[$}aheadofprint{$]$}},
pl = {United States},
pmid = {16633938},
pst = {ppublish},
pt = {Comparative Study; In Vitro; Journal Article; Research Support, Non-U.S.
Gov't},
pubm = {Print-Electronic},
sb = {IM},
so = {J Comput Neurosci. 2006 Aug;21(1):35-49. Epub 2006 Apr 22. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Jones97,
author = {K.E. Jones and P. Bawa},
title = {Computer simulation of the response of human motoneurons to composite
{\protect 1A EPFSP}: effects of background firing rate},
journal = {J. Neurophysiol.},
year = {1997},
volume = {77},
pages = {405-420}
}
@article{Joseph93,
author = {Aaron W. Joseph and Richard L. Hyson},
title = {Coincidence Detection by Binaural Neurons in the Chick Brain Stem},
journal = {J. Neurophysiol.},
year = {1993},
volume = {69},
pages = {1197--1211},
number = {4}
}
@book{Judd90,
title = {Neural Network Design and the Complexity of Learning},
publisher = {MIT Press},
year = {1990},
author = {J. C. Judd},
address = {Cambridge}
}
@article{Jung94,
author = {MW Jung and SI Wiener and BL McNaughton},
title = {Comparison of spatial firing characteristics of units in dorsal and
ventral hippocampus of the rat},
journal = {Journal of Neuroscience},
year = {1994},
volume = {14},
pages = {7347--7356},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Jung93a,
author = {M. W. Jung and B. L. McNaughton},
title = {{Spatial selectivity of unit activity in the hippocampal granular
layer}},
journal = {Hippocampus},
year = {1993},
volume = {3},
pages = {165--182},
number = {2},
month = apr,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Jung95,
author = {P. Jung},
title = {Stochastic resonance and optimal design of threshold detectors},
journal = {Physics Letters A},
year = {1995},
volume = {207},
pages = {93--104}
}
@article{Jung93,
author = {P.~Jung},
title = {Periodically driven stochastic systems},
journal = {Physics Reports},
year = {1993},
volume = {234},
pages = {175--295}
}
@article{Jutten03,
author = {Jutten, C. and Karhunen, J.},
title = {{Advances in nonlinear blind source separation}},
journal = {Proc. of the 4th Int. Symp. on Independent Component Analysis and
Blind Signal Separation (ICA2003)},
year = {2003},
pages = {245--256},
keywords = {ICA},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Koenig96,
author = {P. K\protect{\"o}nig and A. K. Engel and W. Singer},
title = {Integrator or coincidence detector? \protect{T}he role of the cortical
neuron revisited},
journal = {TINS},
year = {1996},
volume = {19},
pages = {130-137},
number = {4}
}
@article{Kuehn91,
author = {R. K\protect{\"u}hn and S. B\protect{\"o}s and J. L. van Hemmen},
title = {Statistical mechanics for networks of graded--response neurons.},
journal = {Phys. Rev. {A}},
year = {1991},
volume = {43},
pages = {2084--2087}
}
@article{Koenig91,
author = {P. K\protect{\"{o}}nig and T. B. Schillen},
title = {Stimulus--dependent assembly formation of oscillatory responses:
I. Synchronization.},
journal = {Neural Computation},
year = {1991},
volume = {3},
pages = {155--166}
}
@article{Kaelbling96,
author = {L.P. Kaelbling and M.L. Littman and A.W. Moore},
title = {Reinforcement learning, a survey},
journal = {J. Artificial Intelligence Research},
year = {1996},
volume = {4},
pages = {237-285}
}
@article{Kaelbling96a,
author = {L.~P. Kaelbling and M.~L. Littman and A.~W. Moore},
title = {Reinforcement Learning: A Survey},
journal = {Journal of Artificial Intelligence Research},
year = {1996},
volume = {4},
pages = {237},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/9605103}
}
@article{Kali00,
author = {S. Kali and P. Dayan},
title = {The Involvement of Recurrent Connections in Area CA3 in Establishing
the Properties of Place},
journal = {J. of Neuroscience},
year = {2000},
volume = {20},
pages = {7463-7477}
}
@article{Kampa07,
author = {Kampa, B.M. and Letzkus, J.J. and Stuart, G.J.},
title = {{Dendritic mechanisms controlling spike-timing-dependent synaptic
plasticity}},
journal = {Trends in Neurosciences},
year = {2007},
volume = {30},
pages = {456--463},
number = {9},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@book{Kampen92,
title = {Stochastic processes in physics and chemistry},
publisher = {North-Holland},
year = {1992},
author = {N. G. van Kampen},
address = {Amsterdam},
edition = {2nd}
}
@article{Kandel01,
author = {E.R. Kandel},
title = {The molecular biology of memory storage: a dialogue between genes
and synapses},
journal = {Science},
year = {2001},
volume = {294},
pages = {1030-1038},
annote = {excellent review article on how LTP is implemented by molecular and
genetic mechansims}
}
@book{Kandel91,
title = {Principles of Neural Science},
publisher = {Elsevier},
year = {1991},
author = {E. C. Kandel and J. H. Schwartz},
address = {New York},
edition = {3rd}
}
@article{Kanter87,
author = {I. Kanter and H. Sompolinsky},
title = {Mean-field theory of spin-glasses with finite coordination-number},
journal = {Phys. Rev. Lett.},
year = {1987},
volume = {58},
pages = {164-167}
}
@book{Kanerva88,
title = {Sparse distributed memory},
publisher = {MIT Press},
year = {1988},
author = {P. Kanverva}
}
@article{Kara02,
author = {P. Kara and P. Reinagel and R.C. Reid},
title = {Low response variability in simultaneously recorded retinal, thalamic,
and cortical neuron},
journal = {Neuron},
year = {2002},
volume = {27},
pages = {635-646}
}
@article{Karklin05,
author = {Karklin, Y. and Lewicki, M. S.},
title = {A hierarchical {B}ayesian model for learning non-linear statistical
regularities in non-stationary natural signals},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {397-423},
number = {2},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Karmarkar02,
author = {U.R. Karmarkar and D.V. Buonomano},
title = {A model of spike-timing dependent plasticity: one or two coincidence
detectors},
journal = {J. Neurophysiology},
year = {2002},
volume = {88},
pages = {507-513}
}
@article{Karmarkar06,
author = {U. Karmarkar and Y. Dan},
title = {Experience-dependant plasticity in adult visual cortex},
journal = {Neuron},
year = {2006},
volume = {52},
pages = {577-585},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Karmarkar02b,
author = {U.R. Karmarkar and M.T. Najarian and D.V. Buonomano},
title = {Mechanisms and significance of spike-timing dependent plasticity},
journal = {Biol. Cybernetics},
year = {2002},
volume = {87},
pages = {373-382}
}
@article{Karni97,
author = {A. Karni and G. Bertini},
title = {Learning perceptual skills: behavioral probes into adult cortical
plasticity},
journal = {Current Opinion in Neurobiology},
year = {1997},
volume = {7},
pages = {530-535},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Karni93,
author = {A. Karni and D. Sagi},
title = {The time course of learning a visual skill},
journal = {Nature},
year = {1993},
volume = {365},
pages = {250-252},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Karni91,
author = {A. Karni and D. Sagi},
title = {Where practice makes perfect in texture discrimination: evidence
for primary visual cortex plasticity},
journal = {PNAS},
year = {1991},
volume = {88},
pages = {4966-4970},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Kasai03,
author = {H. Kasai and M. Matsuzaki and J. Noguchi and N. Yasumatsu and H.
Nakahara},
title = {Structure-stability-function relationship of dendritic spines},
journal = {Trends in Neurosciences},
year = {2003},
volume = {26},
pages = {360-368}
}
@article{Kass01,
author = {R. E. Kass and V. Ventura},
title = {A spike-train probability model},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {1713-1720}
}
@article{Kastner01,
author = {S. Kastner and P. De Weerd and M. A. Pinsk and M. I. Elizondo and
R. Desimone and L. G. Ungerleider},
title = {{Modulation of sensory suppression: implications for receptive field
sizes in the human visual cortex}},
journal = {Journal of Neurophysiology},
year = {2001},
volume = {86},
pages = {1398--1411},
number = {3},
month = sep,
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Katz96,
author = {L. C. Katz and C. J. Shatz },
title = {Synaptic Activity and the Construction of Cortical Circuits },
journal = {Science},
year = {1996},
volume = {274},
pages = {1133-1138}
}
@book{Kay94,
title = {Information-theoretic neural networks for unsupervised learning:
mathematical and statistical considerations},
publisher = {Technical Report: Scottish agricultural statistics service},
year = {1994},
author = {J. Kay}
}
@article{Kayaert03,
author = {G. Kayaert and I. Biederman and R. Vogels},
title = {{Shape tuning in macaque inferior temporal cortex}},
journal = {Journal of Neuroscience},
year = {2003},
volume = {23},
pages = {3016--3027},
number = {7},
month = apr,
keywords = {Vision},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Kayser01,
author = {C. Kayser and W. Einh{\"a}user and O. D{\"u}mmer and K.P. K{\"o}rding
and P. K{\"o}nig},
title = {Extracting Slow Subspaces from Natural Videos Leads to Complex Cells},
booktitle = {Proc. Int. Conf. on Artif. Neural Networks (ICANN) Springer: Lecture
Notes in Computer Science},
year = {2001},
volume = {2130},
pages = {1075-1079},
keywords = {vision, Vision-Models, Slowness},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Keat01,
author = {J. Keat and P. Reinagel and R.C. Reid and M. Meister},
title = {Predicting every spike: A model for the responses of visual neurons},
journal = {Neuron},
year = {2001},
volume = {30},
pages = {803-817}
}
@article{Keat01a,
author = {Keat, J and Reinagel, P and Reid, R C and Meister, M},
title = {Predicting every spike: a model for the responses of visual neurons.},
journal = {Neuron},
year = {2001},
volume = {30},
pages = {803--817},
number = {3},
abstract = {In the early visual system, neuronal responses can be extremely precise.
Under a wide range of stimuli, cells in the retina and thalamus fire
spikes very reproducibly, often with millisecond precision on subsequent
stimulus repeats. Here we develop a mathematical description of the
firing process that, given the recent visual input, accurately predicts
the timing of individual spikes. The formalism is successful in matching
the spike trains from retinal ganglion cells in salamander, rabbit,
and cat, as well as from lateral geniculate nucleus neurons in cat.
It adapts to many different response types, from very precise to
highly variable. The accuracy of the model allows a compact description
of how these neurons encode the visual stimulus.},
address = {Molecular and Cellular Biology, Harvard University, 16 Divinity Avenue,
Cambridge, MA 02138, USA.},
au = {Keat, J and Reinagel, P and Reid, RC and Meister, M},
cin = {Neuron. 2001 Jun;30(3):646-7. PMID: 11430797},
da = {20010629},
date-added = {2008-03-28 12:01:25 +0100},
date-modified = {2008-03-28 12:01:29 +0100},
dcom = {20010726},
edat = {2001/06/30 10:00},
gr = {EY10020/EY/United States NEI; EY10115/EY/United States NEI; EY12196/EY/United
States NEI; NS07009/NS/United States NINDS},
issn = {0896-6273 (Print)},
jid = {8809320},
jt = {Neuron},
language = {eng},
lr = {20071114},
mh = {Action Potentials/*physiology; Animals; Cats; Computer Simulation;
Geniculate Bodies/cytology/physiology; *Models, Neurological; Rabbits;
Reproducibility of Results; Retinal Ganglion Cells/*physiology; Urodela},
mhda = {2001/07/28 10:01},
own = {NLM},
owner = {sprekeler},
pii = {S0896-6273(01)00322-1},
pl = {United States},
pmid = {11430813},
pst = {ppublish},
pt = {Journal Article; Research Support, U.S. Gov't, P.H.S.},
pubm = {Print},
sb = {IM},
so = {Neuron. 2001 Jun;30(3):803-17. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Kelso86,
author = {S. R. Kelso and A. H. Ganong and T. H. Brown},
title = {{\protect H}ebbian synapses in hippocampus.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1986},
volume = {83},
pages = {5326--5330}
}
@book{Kempter97,
title = {Hebbsches {L}ernen zeitlicher {C}odierung: {T}heorie der {S}challortung
im {H\"o}rsystem der {S}chleiereule},
publisher = {Naturwissenschaftliche Reihe, Bd. 17, Darmstadt},
year = {1997},
author = {R. Kempter}
}
@article{Kempter98b,
author = {R. Kempter and W. Gerstner and J.L. van Hemmen},
title = {How the threshold of a neuron determines its capacity for coincidence
detection},
journal = {BioSystems},
year = {1998},
volume = {48},
pages = {105-112}
}
@incollection{Kempter99b,
author = {R. Kempter and W. Gerstner and J. L. van Hemmen},
title = {Spike-Based Compared to Rate-Based Hebbian Learning},
booktitle = {Advances in Neural Information Processing Systems 11},
publisher = {MIT-Press},
year = {1999},
editor = {M.S. Kearns and S.A. Solla and D. A. Cohn},
pages = {125-131 }
}
@article{Kempter01,
author = {R. Kempter and W. Gerstner and J. L. van Hemmen},
title = {Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {2709-2741}
}
@article{Kempter99c,
author = {R. Kempter and W. Gerstner and J. L. van Hemmen},
title = {Hebbian learning and spiking neurons},
journal = {Phys. Rev. E},
year = {1999},
volume = {59},
pages = {4498--4514},
optnumber = {4}
}
@article{Kempter98,
author = {Kempter, R and Gerstner, W and van Hemmen, J L},
title = {How the threshold of a neuron determines its capacity for coincidence
detection.},
journal = {Biosystems},
year = {1998},
volume = {48},
pages = {105--112},
number = {1-3},
abstract = {Coherent oscillatory activity of a population of neurons is thought
to be a vital feature of temporal coding in the brain. We focus on
the question of whether a single neuron can transform a spike code
into a rate code. More precisely, how does a neuron vary its mean
output firing rate, if its input changes from random to coherent?
We investigate the coincidence detection properties of an integrate-and-fire
neuron in dependence upon internal parameters and input statistics.
In particular, we show how coincidence detection depends on the membrane
time constant and the threshold. Furthermore, we demonstrate that
there is an optimal threshold for coincidence detection and that
there is a broad range of near-optimal threshold values. Fine-tuning
is not necessary.},
address = {Physik Department der TU Munchen, Garching bei Munchen, Germany.
kempter@physik.tu-muenchen.de},
au = {Kempter, R and Gerstner, W and van Hemmen, JL},
da = {19990310},
date-added = {2008-03-29 12:57:50 +0100},
date-modified = {2008-03-29 12:58:03 +0100},
dcom = {19990310},
edat = {1999/01/14},
issn = {0303-2647 (Print)},
jid = {0430773},
jt = {Bio Systems},
language = {eng},
lr = {20061115},
mh = {Action Potentials; Models, Neurological; Neurons/*physiology},
mhda = {1999/01/14 00:01},
own = {NLM},
owner = {sprekeler},
pl = {IRELAND},
pmid = {9886637},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {Biosystems. 1998 Sep-Dec;48(1-3):105-12. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@incollection{Kempter99a,
author = {R. Kempter and W. Gerstner and J. L. van Hemmen and H. Wagner},
title = {The quality of Coincidence detection and ITD-tuning: a theoretical
framework},
booktitle = {Psychophysics, Physiology and Models of Hearing},
publisher = {World Scientific, Singapore},
year = {1999},
editor = {T. Dau and V. Hohmann and B. Kollmeier},
pages = {185-192}
}
@article{Kempter98a,
author = {R. Kempter and W. Gerstner and J. L. van Hemmen and H. Wagner},
title = {Extracting oscillations: {N}euronal coincidence detection with noisy
periodic spike input},
journal = {Neural Comput.},
year = {1998},
volume = {10},
pages = {1987--2017},
optnumber = {8}
}
@inproceedings{Kempter96,
author = {R. Kempter and W. Gerstner and J. L. van Hemmen and H. Wagner},
title = {Temporal coding in the sub-millisecond range: Model of {\em barn
owl} auditory pathway},
booktitle = {Advances in Neural Information Processing Systems 8},
year = {1996},
pages = {124-130},
address = {Cambridge, MA},
publisher = {MIT Press, }
}
@article{Kempter96b,
author = {R. Kempter and W. Gerstner and H. Wagner and J.L. van Hemmen},
title = {Model of map formation in the barn owl},
journal = {in preparation},
year = {1995}
}
@article{Kenet03,
author = {T. Kenet and D. Bibibtchkov and M. Tsodyks and A. Grinvald andA.
Arieli},
title = {Spontaneously emerging cortical representations of visual attributes},
journal = {Nature},
year = {2003},
volume = {425},
pages = {954-956}
}
@article{Kepecs02a,
author = {Kepecs, A. and van Rossum, M.C.W. and Song, S. and Tegner, J.},
title = {Spike-timing-dependent plasticity: Common themes and divergent vistas},
journal = {Biological Cybernetics},
year = {2002},
volume = {87},
pages = {446--458},
number = {5},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Springer},
timestamp = {2008.04.14}
}
@article{Kepecs02,
author = {A. Kepecs and M. C.W. van Rossum and S. Song and J. Tegner},
title = {Spike-timing-dependent plasticity: common themes and divergent vistas},
journal = {Biol. Cybern.},
year = {2002},
volume = {87},
pages = {446-458}
}
@article{Kepler92,
author = {Thomas B. Kepler and L. F. Abbott and Eve Marder},
title = {Reduction of conductance-based neuron models},
journal = {Biol. Cybern.},
year = {1992},
volume = {66},
pages = {381-387}
}
@article{Keren05,
author = {Keren, Naomi and Peled, Noam and Korngreen, Alon},
title = {Constraining compartmental models using multiple voltage recordings
and genetic algorithms.},
journal = {J Neurophysiol},
year = {2005},
volume = {94},
pages = {3730--3742},
number = {6},
abstract = {Compartmental models with many nonlinearly and nonhomogeneous distributions
of voltage-gated conductances are routinely used to investigate the
physiology of complex neurons. However, the number of loosely constrained
parameters makes manually constructing the desired model a daunting
if not impossible task. Recently, progress has been made using automated
parameter search methods, such as genetic algorithms (GAs). However,
these methods have been applied to somatically recorded action potentials
using relatively simple target functions. Using a genetic minimization
algorithm and a reduced compartmental model based on a previously
published model of layer 5 neocortical pyramidal neurons we compared
the efficacy of five cost functions (based on the waveform of the
membrane potential, the interspike interval, trajectory density,
and their combinations) to constrain the model. When the model was
constrained using somatic recordings only, a combined cost function
was found to be the most effective. This combined cost function was
then applied to investigate the contribution of dendritic and axonal
recordings to the ability of the GA to constrain the model. The more
recording locations from the dendrite and the axon that were added
to the data set the better was the genetic minimization algorithm
able to constrain the compartmental model. Based on these simulations
we propose an experimental scheme that, in combination with a genetic
minimization algorithm, may be used to constrain compartmental models
of neurons.},
address = {Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.},
au = {Keren, N and Peled, N and Korngreen, A},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00408.2005},
da = {20051118},
date-added = {2007-12-12 19:57:18 +0100},
date-modified = {2007-12-12 19:57:20 +0100},
dcom = {20060125},
dep = {20050810},
doi = {10.1152/jn.00408.2005},
edat = {2005/08/12 09:00},
issn = {0022-3077 (Print)},
jid = {0375404},
jt = {Journal of neurophysiology},
keywords = {Action Potentials/*physiology; *Algorithms; Animals; Cell Compartmentation/physiology;
Computer Simulation; Electric Conductivity; *Models, Neurological;
Neocortex/cytology; Neurons/*physiology/radiation effects; Patch-Clamp
Techniques; Time Factors},
language = {eng},
lr = {20061115},
mhda = {2006/01/26 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2005/08/10 {$[$}aheadofprint{$]$}},
pii = {00408.2005},
pl = {United States},
pmid = {16093338},
pst = {ppublish},
pt = {Comparative Study; Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print-Electronic},
sb = {IM},
so = {J Neurophysiol. 2005 Dec;94(6):3730-42. Epub 2005 Aug 10. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Kernell73,
author = {D. Kernell and H. Sj\protect{\"o}holm},
title = {Repetitive impulse firing: comparison between neuron models based
on 'voltage clamp equations' and spinal motoneurons},
journal = {Acta Physiol. Scand.},
year = {1973},
volume = {87},
pages = {40-56}
}
@article{Kerszberg90,
author = {M. Kerszberg and A. Zippelius},
title = {Synchronization in neural assemblies},
journal = {Physica Scripta},
year = {1990},
volume = {T33},
pages = {54-64}
}
@article{Kesner04,
author = {R. P. Kesner and J. Rogers},
title = {An analysis of independence and interactions of brain substrates
that subserve multiple attributes, memory systems, and underlying
processes},
journal = {Neurobiology of Learning and Memory},
year = {2004},
volume = {82},
pages = {199--215},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Keysers01,
author = {C. Keysers and D. K. Xiao and P. Foldiak and D. I. Perrett},
title = {The speed of sight},
journal = {J. Cognitive Neuroscience},
year = {2001},
volume = {13},
pages = {90-101}
}
@article{Khimenko82,
author = {Khimenko, V.},
title = {{Behavior of a derivative at moments of the crossing of a given level
by a random process}},
journal = {Radiofizika},
year = {1982},
volume = {25},
pages = {797--804},
number = {7},
keywords = {various-artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Kirkwood96,
author = {A. Kirkwood and M.G. Rioult and M.F. Bear},
title = {Experience-dependent modification of synaptic plasticity in visual
cortex},
journal = {Nature},
year = {1996},
volume = {381},
pages = {526-528}
}
@article{Kirkwood78,
author = {P.A. Kirkwood and P.A. Sears},
title = {The synaptic connexions to intercostal motoneurones as revealed by
the average common excitation potential.},
journal = {J. Physiology},
year = {1978},
volume = {275},
pages = {103--134}
}
@article{Kistler02b,
author = {W. M. Kistler},
title = {Spike-timing dependent plasiticity: a phenomenological framework},
journal = {Biological Cybernetics},
year = {2002},
volume = {xx},
pages = {xx}
}
@article{Kistler00d,
author = {W. M. Kistler},
title = {Stability properties of solitary waves and perodic wave trains in
a two-dimensional network of spiking neurons},
journal = {Phys. Rev. E},
year = {2000},
volume = {62},
pages = {8834-8837}
}
@article{Kistler02c,
author = {W. M. Kistler and C. I. {De Zeeuw}},
title = {Dynamical Working Memory and Timed Responses: The Role of Reverberating
Loops in the Olivo-Cerebellar System},
journal = {Neural Comput.},
year = {2002},
pages = {2597-2626}
}
@article{Kistler02a,
author = {W. M. Kistler and W. Gerstner},
title = {Stable Propagation of Activity Pulses in Populations of Spiking Neurons.},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {987-997}
}
@article{Kistler97,
author = {W. M. Kistler and W. Gerstner and J. Leo van Hemmen},
title = {Reduction of \protect{H}odgkin-\protect{H}uxley equations to a single-variable
threshold model},
journal = {Neural Comput.},
year = {1997},
volume = {9},
pages = {1015-1045}
}
@incollection{Kistler98b,
author = {W. M. Kistler and J. L. van Hemmen},
title = {An Analytically Solvable Model of Collectiv Excitation Patterns in
Cortical Tissue},
booktitle = {A perspective look at nonlinear media in physics, chemistry, and
biology},
publisher = {Springer},
year = {1998},
editor = {J. Parisi and S. C. M{\"u}ller and W. Zimmermann}
}
@article{Kistler00,
author = {W. M. Kistler and J. Leo van Hemmen},
title = {Modeling Synaptic Plasticity in Conjunction with the timing of pre-
and postsynaptic potentials},
journal = {Neural Comput.},
year = {2000},
volume = {12},
pages = {385-405}
}
@article{Kistler00a,
author = {W. M. Kistler and J. L. van Hemmen},
title = {Modeling Synaptic Plasticity in Conjunction with the Timing of Pre-
and Postsynaptic Action Potentials},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {385},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Kistler98a,
author = {W. M. Kistler and R. Seitz and J. L. van Hemmen},
title = {Modelling collective excitations in cortical tissue},
journal = {Physica D},
year = {1998},
volume = {114},
pages = {273-295}
}
@article{Kitajima00,
author = {T. Kitajima and K. Hara},
title = {A generalized Hebbian rule for activity-dependent synaptic modifications},
journal = {Neural Networks},
year = {2000},
volume = {13},
pages = {445-454}
}
@article{Kitajima90,
author = {T. Kitajima and K. Hara},
title = {A model of the mechanisms of long-term potentiation in the hippocampus.},
journal = {Biol. Cybern.},
year = {1990},
volume = {64},
pages = {33--39}
}
@book{Kittel86,
title = {{Introduction to solid state physics}},
publisher = {Wiley, New York},
year = {1986},
author = {Kittel, C. and others},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Kjaer94,
author = {T. W. Kjaer and J. A. Hertz and B. J. Richmond},
title = {Decoding cortical neuronal signals: network models, information estimation
and spatial tuning},
journal = {J. Comput. Neuroscience},
year = {1994},
volume = {1},
pages = {109-139}
}
@article{Kleinfeld86,
author = {Kleinfeld, D},
title = {Sequential state generation by model neural networks},
journal = {Proc.~Natl.~Acad.~Sci. USA},
year = {1986},
volume = {83},
pages = {9469-9473}
}
@article{Klopf88,
author = {A.H. Klopf},
title = {A neuronal model of classical conditioning},
journal = {Psychobiology},
year = {1988},
volume = {16},
pages = {85-125}
}
@book{Klopf82,
title = {The hedonistic neuron: a theory of memory, learning, and intelligence},
publisher = {Hemisphere},
year = {1982},
author = {A.H. Klopf}
}
@article{Knierim06,
author = {J.J. Knierim},
title = {Neural representations of location outside Hippocampus},
journal = {Learning \& Memory},
year = {2006},
volume = {13},
pages = {405--415},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Knierim02,
author = {J. J. Knierim},
title = {Dynamic interactions between local surface cues, distal landmarks,
and intrinsic circuitry in hippocampal place cells},
journal = {Journal of Neuroscience},
year = {2002},
volume = {22},
pages = {6254--6264},
number = {14},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Knierim98,
author = {J. J. Knierim and H. S. Kudrimoti and B. L. McNaughton},
title = {{Interactions between idiothetic cues and external landmarks in the
control of place cells and head direction cells}},
journal = {Journal of Neurophysiology},
year = {1998},
volume = {80},
pages = {425--446},
number = {1},
month = jul,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Knierim95,
author = {J. J. Knierim and H. S. Kudrimoti and B. L. McNaughton},
title = {{Place cells, head direction cells, and the learning of landmark
stability}},
journal = {Journal of Neuroscience},
year = {1995},
volume = {15},
pages = {1648--1659},
number = {3 Pt 1},
month = mar,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Knierim03,
author = {J. J. Knierim and G. Rao},
title = {{Distal landmarks and hippocampal place cells: effects of relative
translation versus rotation}},
journal = {Hippocampus},
year = {2003},
volume = {13},
pages = {604--617},
number = {5},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Knight00,
author = {B. W. Knight},
title = {Dynamics of encoding in neuron populations: some general mathematical
features},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {473-518}
}
@article{Knight72a,
author = {B. W. Knight},
title = {Dynamics of encoding in a population of neurons},
journal = {J. Gen. Physiology},
year = {1972},
volume = {59},
pages = {734-766}
}
@article{Knight72b,
author = {B. W. Knight},
title = {The relationship between the firing rate of a single neuron and the
level of activity in a population of neurons},
journal = {J. Gen. Physiology},
year = {1972},
volume = {59},
pages = {767-778}
}
@article{Knott06,
author = {Knott, G.W. and Holtmaat, A. and Wilbrecht, L. and Welker, E. and
Svoboda, K.},
title = {{Spine growth precedes synapse formation in the adult neocortex in
vivo}},
journal = {Nature Neuroscience},
year = {2006},
volume = {9},
pages = {1117--1124},
keywords = {various-artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Knox74,
author = {C. K. Knox},
title = {Cross-corrlation functions for a neuronal model},
journal = {Biophysical J.},
year = {1974},
volume = {14},
pages = {567-582}
}
@article{Knudsen87,
author = {E. Knudsen and S. DuLac and E. D. Esterly},
title = {Computational maps in the brain},
journal = {Annu. Rev. Neurosci.},
year = {1987},
volume = {10},
pages = {41-65}
}
@article{Knudsen79,
author = {Eric I. Knudsen and Gary G. Blasdel and Masakazu Konishi},
title = {Sound Localization by the Barn Owl (Tyto alba) Measured with the
Search Coil Technique},
journal = {J. Comp. Physiol.},
year = {1979},
volume = {133},
pages = {1--11}
}
@article{Kobatake94,
author = {E. Kobatake and K. Tanaka},
title = {{Neuronal selectivities to complex object features in the ventral
visual pathway of the macaque cerebral cortex}},
journal = {Journal of Neurophysiology},
year = {1994},
volume = {71},
pages = {856--867},
number = {3},
month = mar,
keywords = {Vision},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Kobayashi97,
author = {T. Kobayashi and H. Nishijo and M. Fukuda and J. Bures and T. Ono},
title = {{Task-dependent representations in rat hippocampal place neurons}},
journal = {Journal of Neurophysiology},
year = {1997},
volume = {78},
pages = {597--613},
number = {2},
month = aug,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Koch99,
title = {Biophysics of Computation},
publisher = {Oxford University Press},
year = {1999},
author = {C. Koch},
address = {New York, Oxford}
}
@book{Koch99a,
title = {Biophysics of computation : information processing in single neurons},
publisher = {Oxford University Press},
year = {1999},
author = {Koch, Christof},
address = {New York },
bdsk-url-1 = {http://www.loc.gov/catdir/enhancements/fy0605/97051390-d.html},
call-number = {QP357.5},
date-added = {2008-03-14 18:35:50 +0100},
date-modified = {2008-03-14 18:35:54 +0100},
dewey-call-number = {573.8/536},
genre = {Computational neuroscience},
isbn = {0195104919 (alk. paper)},
library-id = {97051390},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.loc.gov/catdir/enhancements/fy0605/97051390-d.html}
}
@article{Koch95,
author = {C. Koch and {\"O}. Bernander and R.J. Douglas},
title = {Do neurons have a voltage or a current threshold for action potential
initiation?},
journal = {J. Comput. Neurosci.},
year = {1995},
volume = {2},
pages = {63-82}
}
@article{Koch96,
author = {C. Koch and M. Rapp and I. Segev},
title = {A brief history of time constants},
journal = {Cerebral cortex},
year = {1996},
volume = {6},
pages = {93-101}
}
@article{Koch96a,
author = {Christof Koch and Moshe Rapp and Idan Segev},
title = {A Brief History of Time (Constants)},
journal = {{Cerebral Cortex}},
year = {1996},
volume = {6},
pages = {92--101},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Koch00,
author = {C. Koch and I. Segev},
title = {The role of single neurons in information processing},
journal = {Nature Neuroscience},
year = {2000},
volume = {3},
pages = {1171--1177},
number = {Supp}
}
@book{Koch89,
title = {Methods in Neuronal Modeling},
publisher = {MIT Press},
year = {1989},
author = {Christof Koch and Idan Segev}
}
@article{Kohn89,
author = {A. F. Kohn},
title = {Dendritic transformations on random synaptic inputs as measured from
a neurons spike train - modeling and simulation},
journal = {IEEE transactions on biomedical engineering},
year = {1989},
volume = {16},
pages = {44-54}
}
@article{Kohonen93,
author = {Teuvo Kohonen},
title = {Physiological Interpretation of the Self-Organizing Map Algorithm},
journal = {Neural Networks},
year = {1993},
volume = {6},
pages = {895--905}
}
@article{Kohonen90,
author = {T. Kohonen},
title = {The self-organizing map},
journal = {Proceedings of the IEEE},
year = {1990},
volume = {78},
pages = {1464-1480},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Kohonen89,
title = {Self-organization and associative memory, 3rd edition},
publisher = {Springer-Verlag},
year = {1989},
author = {T. Kohonen},
address = {Berlin Heidelberg New York}
}
@book{Kohonen84,
title = {Self-{O}rganization and {A}ssociative {M}emory},
publisher = {Springer-Verlag},
year = {1984},
author = {T. Kohonen},
address = {Berlin Heidelberg New York}
}
@article{Kohonen72,
author = {T. Kohonen},
title = {Correlation matrix memories},
journal = {IEEE trans. comp.},
year = {1972},
volume = {C-21},
pages = {353-359}
}
@article{Kole06,
author = {Kole, Maarten H P and Hallermann, Stefan and Stuart, Greg J},
title = {Single Ih channels in pyramidal neuron dendrites: properties, distribution,
and impact on action potential output.},
journal = {J Neurosci},
year = {2006},
volume = {26},
pages = {1677--1687},
number = {6},
abstract = {The hyperpolarization-activated cation current (Ih) plays an important
role in regulating neuronal excitability, yet its native single-channel
properties in the brain are essentially unknown. Here we use variance-mean
analysis to study the properties of single Ih channels in the apical
dendrites of cortical layer 5 pyramidal neurons in vitro. In these
neurons, we find that Ih channels have an average unitary conductance
of 680 +/- 30 fS (n = 18). Spectral analysis of simulated and native
Ih channels showed that there is little or no channel flicker below
5 kHz. In contrast to the uniformly distributed single-channel conductance,
Ih channel number increases exponentially with distance, reaching
densities as high as approximately 550 channels/microm2 at distal
dendritic sites. These high channel densities generate significant
membrane voltage noise. By incorporating a stochastic model of Ih
single-channel gating into a morphologically realistic model of a
layer 5 neuron, we show that this channel noise is higher in distal
dendritic compartments and increased threefold with a 10-fold increased
single-channel conductance (6.8 pS) but constant Ih current density.
In addition, we demonstrate that voltage fluctuations attributable
to stochastic Ih channel gating impact on action potential output,
with greater spike-timing precision in models with the experimentally
determined single-channel conductance. These data suggest that, in
the face of high current densities, the small single-channel conductance
of Ih is critical for maintaining the fidelity of action potential
output.},
address = {Division of Neuroscience, John Curtin School of Medical Research,
Australian National University, Canberra 0200, Australian Capital
Territory, Australia. maarten.kole@anu.edu.au},
au = {Kole, MH and Hallermann, S and Stuart, GJ},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.3664-05.2006},
da = {20060209},
date-added = {2008-03-29 13:51:44 +0100},
date-modified = {2008-03-29 13:51:46 +0100},
dcom = {20060426},
doi = {10.1523/JNEUROSCI.3664-05.2006},
edat = {2006/02/10 09:00},
issn = {1529-2401 (Electronic)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20061115},
mh = {Action Potentials/*physiology; Animals; Dendrites/*physiology; Ion
Channels/*physiology; Male; Patch-Clamp Techniques; Pyramidal Cells/*physiology;
Rats; Rats, Wistar; Reproducibility of Results},
mhda = {2006/04/28 09:00},
own = {NLM},
owner = {sprekeler},
pii = {26/6/1677},
pl = {United States},
pmid = {16467515},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
rn = {0 (Ion Channels)},
sb = {IM},
so = {J Neurosci. 2006 Feb 8;26(6):1677-87. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Kologziejski08,
author = {C. Kolodziejski and B. Porr and F. \protect{W\"org\"otter}},
title = {Mathematical properties of neuronal TD-rules and differential Hebbian
learning: a comparison},
journal = {Biol. Cybern.},
year = {2008},
volume = xx,
pages = {0}
}
@article{Kolodziejski08,
author = {Christoph Kolodziejski and Bernd Porr and Florentin W\&\#x00f6;rg\&\#x00f6;tter},
title = {{Mathematical properties of neuronal TD-rules and differential Hebbian
learning: a comparison}},
journal = {Biol. Cybern.},
year = {2008},
volume = {98},
pages = {259--272},
number = {3},
address = {Secaucus, NJ, USA},
doi = {http://dx.doi.org/10.1007/s00422-007-0209-6},
issn = {0340-1200},
owner = {sprekeler},
publisher = {Springer-Verlag New York, Inc.},
timestamp = {2008.04.14}
}
@article{Kondgen08,
author = {Kondgen, H and Geisler, C and Fusi, S and Wang, XJ and Luscher, HR
and Giugliano, M},
title = {The Dynamical Response Properties of Neocortical Neurons to Temporally
Modulated Noisy Inputs In Vitro.},
journal = {Cereb Cortex},
year = {2008},
abstract = {Cortical neurons are often classified by current-frequency relationship.
Such a static description is inadequate to interpret neuronal responses
to time-varying stimuli. Theoretical studies suggested that single-cell
dynamical response properties are necessary to interpret ensemble
responses to fast input transients. Further, it was shown that input-noise
linearizes and boosts the response bandwidth, and that the interplay
between the barrage of noisy synaptic currents and the spike-initiation
mechanisms determine the dynamical properties of the firing rate.
To test these model predictions, we estimated the linear response
properties of layer 5 pyramidal cells by injecting a superposition
of a small-amplitude sinusoidal wave and a background noise. We characterized
the evoked firing probability across many stimulation trials and
a range of oscillation frequencies (1-1000 Hz), quantifying response
amplitude and phase-shift while changing noise statistics. We found
that neurons track unexpectedly fast transients, as their response
amplitude has no attenuation up to 200 Hz. This cut-off frequency
is higher than the limits set by passive membrane properties ( approximately
50 Hz) and average firing rate ( approximately 20 Hz) and is not
affected by the rate of change of the input. Finally, above 200 Hz,
the response amplitude decays as a power-law with an exponent that
is independent of voltage fluctuations induced by the background
noise.},
address = {Department of Physiology, University of Bern, Bern CH-3012, Switzerland.},
bdsk-url-1 = {http://dx.doi.org/10.1093/cercor/bhm235},
da = {20080211},
date-added = {2008-03-28 22:29:27 +0100},
date-modified = {2008-03-28 22:29:59 +0100},
dep = {20080209},
doi = {10.1093/cercor/bhm235},
edat = {2008/02/12 09:00},
issn = {1460-2199 (Electronic)},
jid = {9110718},
jt = {Cerebral cortex (New York, N.Y. : 1991)},
language = {ENG},
mhda = {2008/02/12 09:00},
own = {NLM},
owner = {sprekeler},
pii = {bhm235},
pmid = {18263893},
pst = {aheadofprint},
pt = {JOURNAL ARTICLE},
pubm = {Print-Electronic},
so = {Cereb Cortex. 2008 Feb 9;. },
stat = {Publisher},
timestamp = {2008.04.14}
}
@article{Konig96,
author = {Konig, P and Engel, A K and Singer, W},
title = {Integrator or coincidence detector? The role of the cortical neuron
revisited.},
journal = {Trends Neurosci},
year = {1996},
volume = {19},
pages = {130--137},
number = {4},
abstract = {Neurons can operate in two distinct ways, depending on the duration
of the interval over which they effectively summate incoming synaptic
potentials. If this interval is of the order of the mean interspike
interval or longer, neurons act effectively as temporal integrators
and transmit temporal patterns with only low reliability. If, by
contrast, the integration interval is short compared to the interspike
interval, neurons act essentially as coincidence detectors, relay
preferentially synchronized input, and the temporal structure of
their output is a direct function of the input pattern. Recently,
interest in this distinction has been revived because experimental
and theoretical results suggest that synchronous firing of neurons
might play an important role for information processing in the cortex.
Here, we argue that coincidence detection, rather than temporal integration,
might be a prevalent operation mode of cortical neurons. We base
our arguments on established biophysical properties of cortical neurons
and on particular features of cortical dynamics.},
address = {Neurosciences Institute, San Diego, CA 92121, USA.},
au = {Konig, P and Engel, AK and Singer, W},
cin = {Trends Neurosci. 1996 Oct;19(10):415-6. PMID: 8888516},
da = {19960801},
date-added = {2008-03-29 12:52:47 +0100},
date-modified = {2008-03-29 12:52:50 +0100},
dcom = {19960801},
edat = {1996/04/01},
issn = {0166-2236 (Print)},
jid = {7808616},
jt = {Trends in neurosciences},
language = {eng},
lr = {20051116},
mh = {Animals; Cats; Cerebral Cortex/*cytology/physiology; Electrophysiology;
Membrane Potentials/physiology; Neurons/*physiology},
mhda = {1996/04/01 00:01},
own = {NLM},
owner = {sprekeler},
pii = {S0166-2236(96)80019-1},
pl = {ENGLAND},
pmid = {8658595},
pst = {ppublish},
pt = {Journal Article; Review},
pubm = {Print},
rf = {45},
sb = {IM},
so = {Trends Neurosci. 1996 Apr;19(4):130-7. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Konishi93,
author = {Masakazu Konishi},
title = {Listening with two ears},
journal = {Scientific American},
year = {1993},
volume = {268},
pages = {34--41},
month = {April},
annote = {Schoener recht allgemein verstaendlicher Artikel ueber das Hoersystem
der Schleiereule mit vielen recht guten Bildern.}
}
@article{Konishi86,
author = {Masakazu Konishi},
title = {Centrally synthesized maps of sensory space},
journal = {Trends in Neurosciences},
year = {1986},
volume = {9},
pages = {163--168},
number = {4},
month = {April},
annote = {Uebersichtlicher Artikel ueber die grundsaetzlichen Zuasammenhaenge
der Schalldetektion der Eule.}
}
@article{Kopell86,
author = {N. Kopell},
title = {Symmetry and phase locking in chains of weakly coupled oscillators},
journal = {Communications on pure and applied mathematics},
year = {1986},
volume = {39},
pages = {623-660}
}
@article{Kopell86b,
author = {N. Kopell},
title = {Phase methods for coupled oscillators and related topics: An annnotated
bibliography},
journal = {J.~Stat.~Phys.},
year = {1986},
volume = {44},
pages = {1035-1042}
}
@book{Kopka,
title = {Latex, vol. 1 and 2},
publisher = {Addison-Wesley},
year = {1991},
author = {H. Kopka},
address = {Deutschland}
}
@article{Kossel90,
author = {A. Kossel and T. Bonhoeffer and J. Bolz},
title = {Non-{H}ebbian synapses in rat visual cortex},
journal = {NeuroReport},
year = {1990},
volume = {1},
pages = {115--118}
}
@article{Koulakov02,
author = {A.A. Koulakov and S. Raghavachari and A. Kepecs and J.E. Lisman},
title = {Model for a robust neural integrator},
journal = {Nature Neuroscience},
year = {2002},
volume = {5},
pages = {775-782}
}
@article{Kourtzi06,
author = {Kourtzi, Z. and DiCarlo, J.J.},
title = {Learning and neural plasticity in visual object recognition},
journal = {Current Opinion in Neurobiology},
year = {2006},
volume = {16},
pages = {152--158},
number = {2},
keywords = {slowness, vision, vision-physiology, vision-models},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@book{Krueger91b,
title = {Neuronal Cooperativity},
publisher = {Springer},
year = {1991},
author = {J. Kr\protect{\"u}ger},
address = {Berlin Heidelberg New York},
opteditor = {J. Kr\protect{\"u}ger}
}
@article{Krueger83,
author = {J. Kr\protect{\"u}ger},
title = {Simultaneous individual recordings from many cerebral neurons: Techniques
and results},
journal = {Rev. Physiol. Biochem. Pharmacol.},
year = {1983},
volume = {98},
pages = {177--233}
}
@article{Krueger88,
author = {J. Kr\protect{\"u}ger and F. Aiple},
title = {Multimicroelectrode investigation of monkey striate cortex: spike
train correlations in the infragranular layers.},
journal = {J. Neurophysiol.},
year = {1988},
volume = {60},
pages = {798--828}
}
@article{Krueger91,
author = {J. Kr\protect{\"u}ger and J. D. Becker},
title = {Recognizing the visual stimulus from neuronal discharges.},
journal = {TINS},
year = {1991},
volume = {14},
pages = {282--286}
}
@article{Krauth87,
author = {W. Krauth and M. M\'{e}zard},
title = {Learning algorithms with optimal stability in neural networks.},
journal = {Phys. Rev. A},
year = {1987},
volume = {20},
pages = {L745-L752}
}
@article{Kreiter92,
author = {A. K. Kreiter and W. Singer},
title = {Oscillatory neuronal responses in the visual cortex of the awake
macaque monkey.},
journal = {Eur. J. Neurosci.},
year = {1992},
volume = {4},
pages = {369--375}
}
@article{Kretzberg01,
author = {J. Kretzberg and M. Egelhaaf and A.-K. Zarzecha},
title = {Membrane potential fluctuations determine the precision of spike
timing and synchronous activity: a model study},
journal = {J. Comput. Neuroscience},
year = {2001},
volume = {10},
pages = {79-97}
}
@article{Krone86,
author = {G. Krone and H. Mallot and G. Palm and A. Sch\protect{\"u}tz},
title = {Spatiotemporal receptive fields: a dynamical model derived from cortical
interactions},
journal = {Proc. Roy. Soc. London, Ser. B.},
year = {1986},
volume = { 226},
pages = {421-444}
}
@book{Kuffler84,
title = {From neuron to brain.},
publisher = {Sinauer},
year = {1984},
author = {S. W. Kuffler and J. G. Nicholls and A. R. Martin},
address = {Sunderland Mass.},
edition = {2nd}
}
@article{Kullmann07,
author = {D.M. Kullmann and K.P.Lamsa},
title = {Long-term synaptic plasticity in hippocampal interneurons},
journal = {Nat. Rev. Neurosci.},
year = {2007},
volume = {8},
pages = {687-699}
}
@article{Kullmann03,
author = {D. M. Kullmann},
title = {Silent synapses: what are they telling us about long-term potentiation?},
journal = {Phil. Trans. R. Soc. Lond B: Biological Sciences},
year = {2003},
volume = {358},
pages = {727 - 733}
}
@article{Kumar04,
author = {A. Kumar and J. Kremkov and S. Rotter and A. Aertsen},
title = {Synaptic integration in a 3-compartment model of layer 5 pyramidal
neurons},
journal = {FENS abstract},
year = {2004},
volume = {2},
pages = {A014.27}
}
@inproceedings{Kuramoto75,
author = {Y. Kuramoto},
title = {Self-entrainment of a population of coupled nonlinear oscillators},
booktitle = {International symposium on mathematical problems in theoretical physics},
year = {1975},
editor = {H. Araki},
pages = {420--422},
address = {Berlin Heidelberg New York},
publisher = {Springer-Verlag}
}
@article{Kuramoto91,
author = {Y. Kuramoto},
title = {Collective sunchronization of pulse-coupled oscillators and excitable
units},
journal = {Physica D},
year = {1991},
volume = {50},
pages = {15--30}
}
@book{Kuramoto84a,
title = {Chemical Oscillations, Waves, and Turbulence.},
publisher = {Springer},
year = {1984},
author = {Y. Kuramoto},
address = {Berlin Heidelberg New York},
note = {68--77}
}
@article{Kuramoto84b,
author = {Y. Kuramoto},
title = {Cooperative dynamics of oscillator community.},
journal = {Progress of theoretical physics Suppl.},
year = {1984},
volume = {79},
pages = {223--240}
}
@article{Kuramoto87,
author = {Y. Kuramoto and I. Nishikawa},
title = {Statistical macrodynamics of large dynamical systems. Case of a phase
transition in oscillatory communities.},
journal = {J. Stat. Phys.},
year = {1987},
volume = {49},
pages = {569--605}
}
@inproceedings{Kurrer90,
author = {C. Kurrer and B. Nieswand and K. Schulten},
title = {A model for synchroneous activity in the visual cortex.},
booktitle = {Self-Organiztion, emerging properties and learning.},
year = {1990},
editor = {Babloyantz A.},
pages = {81--85},
address = {New York},
publisher = {Plenum Press}
}
@unpublished{Kurrer94,
author = {C. Kurrer and K. Schulten},
title = {Noise-induced oscillations},
note = {preprint, University of Illinois},
year = {1994}
}
@unpublished{Koeppl95,
author = {Christine K{"o}ppl},
title = {Phase locking at high frequencies in the barn owl's auditory nerve},
note = {Abstracts of the eighteenth midwinter research meeting of the Association
for Research in Otolaryngology in St. Petersburg Beach, Florida},
year = {1995}
}
@article{Kording04,
author = {K{\"o}rding, K.P. and Kayser, C. and Einh{\"a}user, W. and K{\"o}nig,
P.},
title = {How Are Complex Cell Properties Adapted to the Statistics of Natural
Stimuli?},
journal = {Journal of Neurophysiology},
year = {2004},
volume = {91},
pages = {206--212},
number = {1},
keywords = {slowness, plasticity, vision, vision-models},
owner = {sprekeler},
publisher = {Am Physiological Soc},
timestamp = {2008.04.14}
}
@article{Koerding01,
author = {K.P. K{\"o}rding and P. K{\"o}nig},
title = {Neurons with two sites of synaptic integration learn invariant representations},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {2823-2849}
}
@article{Koerding00,
author = {K.P. K{\"o}rding and P. K{\"o}nig},
title = {A learning rule for dynamic recruitment and decorrelation},
journal = {Neural Networks},
year = {2000},
volume = {13},
pages = {1-9}
}
@article{Koerding00b,
author = {K.P. K{\"o}rding and P. K{\"o}nig},
title = {A spike based learning rule for generation of invariant representations},
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year = {2000},
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year = {2001},
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pages = {2823--2849},
number = {12},
month = dec,
keywords = {slowness},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://neco.mitpress.org/cgi/content/abstract/13/12/2823},
urlpaper = {http://neco.mitpress.org/cgi/content/full/13/12/2823},
urlpaper2 = {http://neco.mitpress.org/cgi/reprint/13/12/2823},
urlpaper3 = {http://www.ini.unizh.ch/~koerding/kording.nc.01.pdf}
}
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keywords = {various-artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {M. S. Landy and I. Oruc},
title = {{Properties of second-order spatial frequency channels}},
journal = {Vision Research},
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number = {19},
month = sep,
owner = {sprekeler},
timestamp = {2008.04.14}
}
@phdthesis{DeLange06,
author = {E. De Lange},
title = {Neuron models of the generic bifurcation type: network analysis and
data modeling},
school = {EPFL},
year = {2006},
type = {Ph.{D}.~thesis},
address = {Lausanne, Switzerland},
note = {Dir.~Martin Hasler}
}
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oscillators.},
booktitle = {Proceedings of the IEEE Int. Conference on Nonlinear Dynamics of
Electronic Systems (NDES'2003), Scuol, Switzerland},
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editor = {IEEE},
pages = {65-68}
}
@inproceedings{DeLange03,
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title = {Phase locking and coincidence detection in threshold coupled neural
oscillators},
booktitle = {Proc. NDES},
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pages = {65--68}
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(Cambridge Univ. Press, Cambridge, 1988)}
}
@article{Larkum04,
author = {M.E. Larkum and W. Senn and H.R. L\protect{\"u}scher},
title = {Top-down dendritic input increases the gain of layer 5 pyramidal
neurons },
journal = {CEREBRAL CORTEX },
year = {2004},
volume = {14},
pages = {1059-1079}
}
@article{Larkum01,
author = {M.E. Larkum and J.J. Zhu and B. Sakmann},
title = {Dendritic mechanisms underlying the coupling of the dendritic with
the axonal action potential initiation zone of adult rat layer 5
pyramidal neurons},
journal = {J. Physiology (London)},
year = {2001},
volume = {447-466}
}
@article{Larkum04a,
author = {Larkum, Matthew E and Senn, Walter and Luscher, Hans-R},
title = {Top-down dendritic input increases the gain of layer 5 pyramidal
neurons.},
journal = {Cereb Cortex},
year = {2004},
volume = {14},
pages = {1059--1070},
number = {10},
abstract = {The cerebral cortex is organized so that an important component of
feedback input from higher to lower cortical areas arrives at the
distal apical tufts of pyramidal neurons. Yet, distal inputs are
predicted to have much less impact on firing than proximal inputs.
Here we show that even weak asynchronous dendritic input to the distal
tuft region can significantly increase the gain of layer 5 pyramidal
neurons and thereby the output of columns in the primary somatosensory
cortex of the rat. Noisy currents injected in ramps at different
dendritic locations showed that the initial slope of the frequency-current
(f/I) relationship increases with the distance of the current injection
from the soma. The increase was due to the interaction of dendritic
depolarization with back-propagating APs which activated dendritic
calcium conductances. Gain increases were accompanied by a change
of firing mode from isolated spikes to bursting where the timing
of bursts coded the presence of coincident somatic and dendritic
inputs. We propose that this dendritic gain modulation and the timing
of bursts may serve to associate top-down and bottom-up input on
different time scales.},
address = {Institute of Physiology, University of Bern, Buhlplatz 5, CH-3012
Bern, Switzerland. larkum@pyl.unibe.ch},
au = {Larkum, ME and Senn, W and Luscher, HR},
bdsk-url-1 = {http://dx.doi.org/10.1093/cercor/bhh065},
da = {20040910},
date-added = {2008-01-31 11:57:18 +0100},
date-modified = {2008-01-31 11:57:32 +0100},
dcom = {20041109},
dep = {20040427},
doi = {10.1093/cercor/bhh065},
edat = {2004/04/30 05:00},
issn = {1047-3211 (Print)},
jid = {9110718},
jt = {Cerebral cortex (New York, N.Y. : 1991)},
keywords = {Action Potentials/*physiology; Animals; Dendrites/*physiology; Neocortex/physiology;
Pyramidal Cells/*physiology; Rats; Rats, Wistar},
language = {eng},
lr = {20061115},
mhda = {2004/11/13 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2004/04/27 {$[$}aheadofprint{$]$}},
pii = {bhh065},
pl = {United States},
pmid = {15115747},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print-Electronic},
sb = {IM},
so = {Cereb Cortex. 2004 Oct;14(10):1059-70. Epub 2004 Apr 27. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Larkum99,
author = {Larkum, M E and Zhu, J J and Sakmann, B},
title = {A new cellular mechanism for coupling inputs arriving at different
cortical layers.},
journal = {Nature},
year = {1999},
volume = {398},
pages = {338--341},
number = {6725},
abstract = {Pyramidal neurons in layer 5 of the neocortex of the brain extend
their axons and dendrites into all layers. They are also unusual
in having both an axonal and a dendritic zone for the initiation
of action potentials. Distal dendritic inputs, which normally appear
greatly attenuated at the axon, must cross a high threshold at the
dendritic initiation zone to evoke calcium action potentials but
can then generate bursts of axonal action potentials. Here we show
that a single back-propagating sodium action potential generated
in the axon facilitates the initiation of these calcium action potentials
when it coincides with distal dendritic input within a time window
of several milliseconds. Inhibitory dendritic input can selectively
block the initiation of dendritic calcium action potentials, preventing
bursts of axonal action potentials. Thus, excitatory and inhibitory
postsynaptic potentials arising in the distal dendrites can exert
significantly greater control over action potential initiation in
the axon than would be expected from their electrotonically isolated
locations. The coincidence of a single back-propagating action potential
with a subthreshold distal excitatory postsynaptic potential to evoke
a burst of axonal action potentials represents a new mechanism by
which the main cortical output neurons can associate inputs arriving
at different cortical layers.},
address = {Abt. Zellphysiologie, Max-Planck-Institut fur Medizinische Forschung,
Heidelberg, Germany. mlarkum@sunny.mpimf-heidelberg.mpg.de},
au = {Larkum, ME and Zhu, JJ and Sakmann, B},
bdsk-url-1 = {http://dx.doi.org/10.1038/18686},
da = {19990413},
date-added = {2008-03-28 23:39:33 +0100},
date-modified = {2008-03-28 23:39:43 +0100},
dcom = {19990413},
doi = {10.1038/18686},
edat = {1999/04/07 02:01},
issn = {0028-0836 (Print)},
jid = {0410462},
jt = {Nature},
language = {eng},
lr = {20061115},
mh = {Action Potentials; Animals; Axons/physiology; Calcium/physiology;
Cerebral Cortex/cytology/*physiology; Dendrites/physiology; Electrophysiology;
Excitatory Postsynaptic Potentials; Interneurons/physiology; Neural
Pathways/*physiology; Pyramidal Cells/*physiology; Rats; Rats, Wistar;
Sodium/physiology},
mhda = {2001/03/23 10:01},
own = {NLM},
owner = {sprekeler},
pl = {ENGLAND},
pmid = {10192334},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
rn = {7440-23-5 (Sodium); 7440-70-2 (Calcium)},
sb = {IM},
so = {Nature. 1999 Mar 25;398(6725):338-41. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Larson86,
author = {J. Larson and G. Lynch},
title = {Induction of synaptic potentiation in Hippocampus by patterned stimualtion
involves two events.},
journal = {Science},
year = {1986},
volume = {232},
pages = {985--988}
}
@article{Lasalle00,
author = {J.-M. Lassalle and T. Bataille and H. Halley},
title = {Reversible inactivation of the hippocampal mossy fiber synapses in
mice impaires spatial learning but neither consolidation nor memory
retrieval in the {M}orris navigation task},
journal = {Neurobiology of learning and memory},
year = {2000},
volume = {243-257}
}
@article{Latham04,
author = {P.E. Latham and S. Nirenberg},
title = {Computing and stability in cortical networks},
journal = {Neural Computation},
year = {2004},
volume = {16},
pages = {1385-1412}
}
@article{Latham00,
author = {P. E. Latham and B.J. Richmond and P.G Nelson and S. Nirenberg},
title = {Intrinsic dynamics in neuronal networks. \protect{I. T}heory},
journal = {J. Neurophysiology},
year = {2000},
volume = {83},
pages = {808-827}
}
@article{Latham00b,
author = {P. E. Latham and B.J. Richmond and S. Nirenberg and P.G Nelson},
title = {Intrinsic dynamics in neuronal networks. \protect{II. E}periments},
journal = {J. Neurophysiology},
year = {2000},
volume = {83},
pages = {828-835}
}
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author = {S. Laughlin},
title = {A simple coding procedure enhances a neurons information capacity},
journal = {Z. Naturforschung},
year = {1981},
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pages = {910-912}
}
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author = {S.B. Laughlin and R. R. {deRuyter van Steveninck} and J.C. Anderson},
title = {The metabolic cost of neural information},
journal = {Nature Neuroscience},
year = {1998},
volume = {1},
number = {36-41}
}
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author = {Gilles Laurant and Hananel Davidowitz},
title = {Encoding of Olfactory Information with Oscillating Neural Assemlies},
journal = {Science},
year = {1994},
volume = {265},
pages = {1872--1875}
}
@article{Lee06,
author = {A.K. Lee and I.D. Manns and B. Sakmann and M. Brecht},
title = {Whole-cell recordings in freely moving rats},
journal = {Neuron},
year = {2006},
volume = {51},
pages = {399--407},
number = {4},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Lee04,
author = {I. Lee and D. Yoganarasimha and G. Rao and J. J. Knierim},
title = {Comparison of population coherence of place cells in hippocampal
subfields {CA1} and {CA3}},
journal = {Nature},
year = {2004},
volume = {430},
pages = {456--459},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Legenstein05,
author = {R. Legenstein and C. Naeger and W. Maass},
title = {What can a neuron learn with spike-timing dependent plasticity},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {2337-2382}
}
@article{Legenstein05a,
author = {Robert Legenstein and Christian Naeger and Wolfgang Maass},
title = {What can a Neuron learn with Spike-Timing-Dependent Plasticity?},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {2337-2382},
number = {11},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@incollection{Legenstein08,
author = {Robert Legenstein and Dejan Pecevski and Wolfgang Maass},
title = {Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent
Plasticity},
booktitle = {Advances in Neural Information Processing Systems 20},
publisher = {MIT Press},
year = {2008},
editor = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
address = {Cambridge, MA}
}
@article{Leibold08a,
author = {Christian Leibold and Anja Gundlfinger and Robert Schmidt and Kay
Thurley and Dietmar Schmitz and Richard Kempter},
title = {Temporal compression mediated by short-term synaptic plasticity.},
journal = {Proc Natl Acad Sci U S A},
year = {2008},
volume = {105},
pages = {4417--4422},
number = {11},
month = {Mar},
abstract = {Time scales of cortical neuronal dynamics range from few milliseconds
to hundreds of milliseconds. In contrast, behavior occurs on the
time scale of seconds or longer. How can behavioral time then be
neuronally represented in cortical networks? Here, using electrophysiology
and modeling, we offer a hypothesis on how to bridge the gap between
behavioral and cellular time scales. The core idea is to use a long
time constant of decay of synaptic facilitation to translate slow
behaviorally induced temporal correlations into a distribution of
synaptic response amplitudes. These amplitudes can then be transferred
to a sequence of action potentials in a population of neurons. These
sequences provide temporal correlations on a millisecond time scale
that are able to induce persistent synaptic changes. As a proof of
concept, we provide simulations of a neuron that learns to discriminate
temporal patterns on a time scale of seconds by synaptic learning
rules with a millisecond memory buffer. We find that the conversion
from synaptic amplitudes to millisecond correlations can be strongly
facilitated by subthreshold oscillations both in terms of information
transmission and success of learning.},
doi = {10.1073/pnas.0708711105},
keywords = {Plasticity},
owner = {sprekeler},
pii = {0708711105},
pmid = {18337494},
timestamp = {2008.05.01},
url = {http://dx.doi.org/10.1073/pnas.0708711105}
}
@article{Leibold02,
author = {C. Leibold and J.L. van Hemmen},
title = {Mapping time},
journal = {Biol. Cybern.},
year = {2002},
volume = {87},
pages = {428-439}
}
@article{Leibold08b,
author = {Christian Leibold and Richard Kempter},
title = {Sparseness constrains the prolongation of memory lifetime via synaptic
metaplasticity.},
journal = {Cereb Cortex},
year = {2008},
volume = {18},
pages = {67--77},
number = {1},
month = {Jan},
abstract = {Synaptic changes impair previously acquired memory traces. The smaller
this impairment the larger is the longevity of memories. Two strategies
have been suggested to keep memories from being overwritten too rapidly
while preserving receptiveness to new contents: either introducing
synaptic meta levels that store the history of synaptic state changes
or reducing the number of synchronously active neurons, which decreases
interference. We find that synaptic metaplasticity indeed can prolong
memory lifetimes but only under the restriction that the neuronal
population code is not too sparse. For sparse codes, metaplasticity
may actually hinder memory longevity. This is important because in
memory-related brain regions as the hippocampus population codes
are sparse. Comparing 2 different synaptic cascade models with binary
weights, we find that a serial topology of synaptic state transitions
gives rise to larger memory capacities than a model with cross transitions.
For the serial model, memory capacity is virtually independent of
network size and connectivity.},
doi = {10.1093/cercor/bhm037},
keywords = {Plasticity},
owner = {sprekeler},
pii = {bhm037},
pmid = {17490993},
timestamp = {2008.05.01},
url = {http://dx.doi.org/10.1093/cercor/bhm037}
}
@article{Leibold06,
author = {Christian Leibold and Richard Kempter},
title = {Memory capacity for sequences in a recurrent network with biological
constraints.},
journal = {Neural Comput},
year = {2006},
volume = {18},
pages = {904--941},
number = {4},
month = {Apr},
abstract = {The CA3 region of the hippocampus is a recurrent neural network that
is essential for the storage and replay of sequences of patterns
that represent behavioral events. Here we present a theoretical framework
to calculate a sparsely connected network's capacity to store such
sequences. As in CA3, only a limited subset of neurons in the network
is active at any one time, pattern retrieval is subject to error,
and the resources for plasticity are limited. Our analysis combines
an analytical mean field approach, stochastic dynamics, and cellular
simulations of a time-discrete McCulloch-Pitts network with binary
synapses. To maximize the number of sequences that can be stored
in the network, we concurrently optimize the number of active neurons,
that is, pattern size, and the firing threshold. We find that for
one-step associations (i.e., minimal sequences), the optimal pattern
size is inversely proportional to the mean connectivity c, whereas
the optimal firing threshold is independent of the connectivity.
If the number of synapses per neuron is fixed, the maximum number
P of stored sequences in a sufficiently large, nonmodular network
is independent of its number N of cells. On the other hand, if the
number of synapses scales as the network size to the power of 3/2,
the number of sequences P is proportional to N. In other words, sequential
memory is scalable. Furthermore, we find that there is an optimal
ratio r between silent and nonsilent synapses at which the storage
capacity alpha = P//[c(1 + r)N] assumes a maximum. For long sequences,
the capacity of sequential memory is about one order of magnitude
below the capacity for minimal sequences, but otherwise behaves similar
to the case of minimal sequences. In a biologically inspired scenario,
the information content per synapse is far below theoretical optimality,
suggesting that the brain trades off error tolerance against information
content in encoding sequential memories.},
doi = {10.1162/089976606775774714},
keywords = {Hippocampus; plasticity},
owner = {sprekeler},
pmid = {16494695},
timestamp = {2008.05.01},
url = {http://dx.doi.org/10.1162/089976606775774714}
}
@article{Lengyel05,
author = {M. Lengyel and J. Kwag and O. Paulsen and P. Dayan},
title = {Matching storage and recall: hippocampal spike timing-dependent plasticity
and phase response curves},
journal = {Nat. Neurosci.},
year = {2005},
volume = {8},
pages = {1677-1683}
}
@article{Lestienne96,
author = {R\protect{\'e}my Lestienne},
title = {Determination of the precision of spike timing in the visual cortex
of anaesthetised cats},
journal = {Biol. Cybern.},
year = {1996},
volume = {74},
pages = {55-61}
}
@article{Letzkus06,
author = {Letzkus, Johannes J. and Kampa, Bjorn M. and Stuart, Greg J.},
title = {Learning Rules for Spike Timing-Dependent Plasticity Depend on Dendritic
Synapse Location},
journal = {J. Neurosci.},
year = {2006},
volume = {26},
pages = {10420--10429},
number = {41},
month = oct,
abstract = {Previous studies focusing on the temporal rules governing changes
in synaptic strength during spike timing-dependent synaptic plasticity
(STDP) have paid little attention to the fact that synaptic inputs
are distributed across complex dendritic trees. During STDP, propagation
of action potentials (APs) back to the site of synaptic input is
thought to trigger plasticity. However, in pyramidal neurons, backpropagation
of single APs is decremental, whereas high-frequency bursts lead
to generation of distal dendritic calcium spikes. This raises the
question whether STDP learning rules depend on synapse location and
firing mode. Here, we investigate this issue at synapses between
layer 2/3 and layer 5 pyramidal neurons in somatosensory cortex.
We find that low-frequency pairing of single APs at positive times
leads to a distance-dependent shift to long-term depression (LTD)
at distal inputs. At proximal sites, this LTD could be converted
to long-term potentiation (LTP) by dendritic depolarizations suprathreshold
for BAC-firing or by high-frequency AP bursts. During AP bursts,
we observed a progressive, distance-dependent shift in the timing
requirements for induction of LTP and LTD, such that distal synapses
display novel timing rules: they potentiate when inputs are activated
after burst onset (negative timing) but depress when activated before
burst onset (positive timing). These findings could be explained
by distance-dependent differences in the underlying dendritic voltage
waveforms driving NMDA receptor activation during STDP induction.
Our results suggest that synapse location within the dendritic tree
is a crucial determinant of STDP, and that synapses undergo plasticity
according to local rather than global learning rules.},
comment = {10.1523/JNEUROSCI.2650-06.2006},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.jneurosci.org/cgi/content/abstract/26/41/10420}
}
@article{Leutgeb05a,
author = {S. Leutgeb and J.K. Leutgeb and M.B. Moser and E.I. Moser},
title = {Place cells, spatial maps and the population code for memory},
journal = {Current Opinion in Neurobiology},
year = {2005},
volume = {15},
pages = {738--46},
number = {6},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Leutgeb05,
author = {S. Leutgeb and J. K. Leutgeb and C. A. Barnes and E. I. Moser and
B. L. McNaughton and M.-B. Moser},
title = {Independent Codes for Spatial and Episodic Memory in Hippocampal
Neuronal Ensembles},
journal = {Science},
year = {2005},
volume = {309},
pages = {619--623},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Leutgeb04,
author = {S. Leutgeb and J. K. Leutgeb and A. Treves and M.-B. Moser and E.
I. Moser},
title = {Distinct Ensemble Codes in Hippocampal Areas {CA3} and {CA1}},
journal = {Science},
year = {2004},
volume = {305},
pages = {1295--1298},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Leutgeb00,
author = {S. Leutgeb and K. E. Ragozzino and S. J. Mizumori},
title = {Convergence of head direction and place information in the ca1 region
of hippocampus},
journal = {Neuroscience},
year = {2000},
volume = {100},
pages = {11--19},
number = {1},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Lever02,
author = {Colin Lever and Neil Burgess and Francesca Cacucci and Tom Hartley
and John O'Keefe},
title = {What can the hippocampal representation of environmental geometry
tell us about Hebbian learning?},
journal = {Bio. Cybern.},
year = {2002},
volume = {87},
pages = {356-372}
}
@article{Lever02a,
author = {C. Lever and N. Burgess and F. Cacucci and T. Hartley and J. O'Keefe},
title = {What can the hippocampal representation of environmental geometry
tell us about hebbian learning?},
journal = {Biological Cybernetics},
year = {2002},
volume = {87},
pages = {356--372},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {J.E. Levin and J.P. Miller},
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by stochastic resonance},
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pages = {165-168}
}
@book{Levine91,
title = {Introduction to Neural and Cognitive Modeling},
publisher = {Lawrence Erlbaum},
year = {1991},
author = {D. S. Levine},
address = {Hillsdale}
}
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pages = {2517--2542},
number = {6},
month = jun,
keywords = {Vision},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {W.B. Levy and R.A. Baxter},
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year = {2002},
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pages = {4746-4755}
}
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pages = {xx}
}
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in visual search},
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pages = {10530-10535}
}
@article{Liao95,
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LTP in CA1 region of hippocampal slice },
journal = {Nature},
year = {1995},
volume = {375},
pages = {400-404}
}
@article{Liao01,
author = {D. Liao and R. H. Scannevin and R. Huganir},
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Recruitment of AMPA Receptors},
journal = {J. Neurosci.},
year = {2001},
volume = {21},
pages = {6008 - 6017 }
}
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author = {W. Lindemann},
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inhibition. {I}. {S}imulation of lateralization for stationary signals},
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pages = {1608--1622},
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}
@article{Linden99,
author = {D. J. Linden},
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of LTP and LTD},
journal = {Neuron},
year = {1999},
volume = {22},
pages = {661-666}
}
@article{Lindner01,
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year = {2001},
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pages = {2934-2937}
}
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author = {B. A. Linkenhofer and E. I. Knudsen},
title = {Incremental training increases the plasticity of the auditory space
map in adult barn owls
},
journal = {Nature},
year = {2002},
optpages = {293-296},
optvolume = {419}
}
@article{Linsker97,
author = {R. Linsker},
title = {A Local Learning Rule That Enables Information Maximization for Arbitrary
Input Distribution},
journal = {Neural Computation},
year = {1997},
volume = {9},
pages = {1661-1665},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Linsker93,
author = {R. Linsker},
title = {Deriving receptive fields using an optimal encoding criterion},
booktitle = {Advances in Neural Information Processing Systems},
year = {1993},
volume = {5},
pages = {953--960},
address = {San Mateo CA},
publisher = {Morgan Kaufmann}
}
@article{Linsker92,
author = {R. Linsker},
title = {Local Synaptic Learning Rules Suffice to Maximize Mutual Information
in a Linear Network },
journal = {Neural Computation},
year = {1992},
volume = {4},
pages = {691-702},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Linsker89,
author = {R. Linsker},
title = {How to Generate Ordered Maps by maximizing the Mutual Information
between Input and Output Signals},
journal = {Neural Computation},
year = {1989},
volume = {1},
pages = {402--411},
number = {3}
}
@article{Linsker88,
author = {R. Linsker},
title = {Self-organization in a perceptual network},
journal = {Computer},
year = {1988},
volume = {21},
pages = {105--117}
}
@article{Linsker86a,
author = {R. Linsker},
title = {From basic network principles to neural architecture: emergence of
spatial-opponent cells},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1986},
volume = {83},
pages = {7508-7512}
}
@article{Linsker86b,
author = {R. Linsker},
title = {From basic network principles to neural architecture: emergence of
orientation selective cells},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1986},
volume = {83},
pages = {8390-8394}
}
@article{Linsker86c,
author = {R. Linsker},
title = {From basic network principles to neural architecture: emergence of
orientation columns},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1986},
volume = {83},
pages = {8779-8783}
}
@book{NIPS91,
title = {Advances in Neural Information Processing Systems},
publisher = {Morgan Kaufmann Publishers},
year = {1991},
author = {R. P. Lippmann},
volume = {3},
address = {San Mateo}
}
@article{Lisman05b,
author = {John Lisman},
title = {The theta/gamma discrete phase code occuring during the hippocampal
phase precession may be a more general brain coding scheme.},
journal = {Hippocampus},
year = {2005},
volume = {15},
pages = {913--922},
number = {7},
abstract = {In the hippocampus, oscillations in the theta and gamma frequency
range occur together and interact in several ways, indicating that
they are part of a common functional system. It is argued that these
oscillations form a coding scheme that is used in the hippocampus
to organize the readout from long-term memory of the discrete sequence
of upcoming places, as cued by current position. This readout of
place cells has been analyzed in several ways. First, plots of the
theta phase of spikes vs. position on a track show a systematic progression
of phase as rats run through a place field. This is termed the phase
precession. Second, two cells with nearby place fields have a systematic
difference in phase, as indicated by a cross-correlation having a
peak with a temporal offset that is a significant fraction of a theta
cycle. Third, several different decoding algorithms demonstrate the
information content of theta phase in predicting the animal's position.
It appears that small phase differences corresponding to jitter within
a gamma cycle do not carry information. This evidence, together with
the finding that principle cells fire preferentially at a given gamma
phase, supports the concept of theta/gamma coding: a given place
is encoded by the spatial pattern of neurons that fire in a given
gamma cycle (the exact timing within a gamma cycle being unimportant);
sequential places are encoded in sequential gamma subcycles of the
theta cycle (i.e., with different discrete theta phase). It appears
that this general form of coding is not restricted to readout of
information from long-term memory in the hippocampus because similar
patterns of theta/gamma oscillations have been observed in multiple
brain regions, including regions involved in working memory and sensory
integration. It is suggested that dual oscillations serve a general
function: the encoding of multiple units of information (items) in
a way that preserves their serial order. The relationship of such
coding to that proposed by Singer and von der Malsburg is discussed;
in their scheme, theta is not considered. It is argued that what
theta provides is the absolute phase reference needed for encoding
order. Theta/gamma coding therefore bears some relationship to the
concept of "word" in digital computers, with word length corresponding
to the number of gamma cycles within a theta cycle, and discrete
phase corresponding to the ordered "place" within a word.},
doi = {10.1002/hipo.20121},
keywords = {Hippocampus, Neuronal-Processing},
owner = {sprekeler},
pmid = {16161035},
timestamp = {2008.05.08},
url = {http://dx.doi.org/10.1002/hipo.20121}
}
@article{Lisman03,
author = {J. Lisman},
title = {Long-term potentiation: outstanding questions and attempted synthesis
},
journal = {Phil. Trans. R. Soc. Lond B: Biological Sciences},
year = {2003},
volume = {358},
pages = {829 - 842 }
}
@article{Lisman99,
author = {J. Lisman},
title = {Relating hippocampal circuitry to function: recall of memory sequences
by reciprocal dentate-CA3 interactions},
journal = {Neuron},
year = {1999},
volume = {22},
pages = {233-242}
}
@article{Lisman89,
author = {J. Lisman},
title = {A mechanism for Hebb and anti-Hebb processes underlying learning
and memory.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1989},
volume = {86},
pages = {9574--9578}
}
@article{Lisman05,
author = {John Lisman and Nelson Spruston},
title = {Postsynaptic depolarization requirements for {LTP} and {LTD}: a critique
of spike timing-dependent plasticity},
journal = {{Nature Neuroscience}},
year = {2005},
volume = {8},
pages = {839--841},
number = {7},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Lisman01,
author = {J.E. Lisman and A.M. Zhabotinsky},
title = {A model of synaptic memory: A \protect{CaMKII/PP1} switch that potentiates
transmission by organizing an \protect{AMPA} receptor anchoring assembly},
journal = {Neuron},
year = {2001},
volume = {31},
pages = {191-201}
}
@article{Little74,
author = {W. A. Little},
title = {The existence of persistent states in the brain},
journal = {Math. Biosc.},
year = {1974},
volume = {19},
pages = {101-120}
}
@article{Little78,
author = {W. A. Little and G. L. Shaw},
title = {Analytical study of the memory storage capacity of a neural network},
journal = {Math. Biosc.},
year = {1978},
volume = {39},
pages = {281-290}
}
@article{Liu99,
author = {Z. Liu},
title = {Perceptual learning in motion discrimination that generalizes across
motion directions},
journal = {PNAS},
year = {1999},
volume = {96},
pages = {14085-14087},
number = {24},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Livet07,
author = {Livet, Jean and Weissman, Tamily A. and Kang, Hyuno and Draft, Ryan
W. and Lu, Ju and Bennis, Robyn A. and Sanes, Joshua R. and Lichtman,
Jeff W.},
title = {{{T}ransgenic strategies for combinatorial expression of fluorescent
proteins in the nervour system}},
journal = {Nature},
year = {2007},
volume = {450},
pages = {56--63},
number = {7166},
month = {Nov},
keywords = {various-artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Ljunberg92,
author = {T. Ljunberg and P. Apicella amd W. Schultz},
title = {Responses of monkey dopamine neurons during learning of behavioral
interactions},
journal = {J. Neurophysiol.},
year = {1992},
volume = {67},
pages = {145-163}
}
@article{Llano91,
author = {Llano, I and Marty, A and Armstrong, C M and Konnerth, A},
title = {Synaptic- and agonist-induced excitatory currents of Purkinje cells
in rat cerebellar slices.},
journal = {J Physiol},
year = {1991},
volume = {434},
pages = {183--213},
abstract = {1. Postsynaptic currents originating from activation of the two major
excitatory inputs to Purkinje cells were studied in thin slices of
rat cerebellum, using the tight-seal whole-cell recording technique.
Two types of excitatory postsynaptic currents were analysed: those
evoked by stimulation of the granule cell-parallel fibre system (PF-EPSC)
and those elicited by stimulation of the climbing fibres (CF-EPSC).
2. Both types of postsynaptic currents had a linear current-voltage
relation, reversing at membrane potentials close to 0 mV. Their time
course of activation was independent of the membrane potential. 3.
For both types of postsynaptic currents, the time course of decay
was well described by a single exponential function, with a time
constant which increased as the membrane potential was made more
positive. 4. Postsynaptic currents arising from stimulation of the
climbing fibre generally had a slightly faster time course of onset
and decay than those associated with stimulation of the granule cell-parallel
fibre system. The average values of the 10-90% rise time were 1.8
+/- 0.4 ms (means +/- S.D., n = 7) for PF-EPSCs and 0.8 +/- 0.3 ms
(n = 9) for CF-EPSCs. Time constants of decay, at a holding potential
of -60 mV, had values of 8.3 +/- 1.6 ms (n = 7) and 6.4 +/- 1.1 ms
(n = 9) for PF-EPSCs and CF-EPSCs respectively. 5. CF-EPSCs and PF-EPSCs
had the characteristics described above in slices derived from animals
aged 9-22 days old and 9-15 days old, respectively. The PF-EPSCs
in animals older than 15 days had very slow time courses and positive
apparent reversal potentials, suggesting that they originated from
distal locations, not under accurate voltage control. 6. In order
to assess the quality of the voltage clamp, responses to hyperpolarizing
pulses from -70 mV were analysed. The capacitive currents could be
fitted by the sum of two exponentials, and were interpreted with
an equivalent electrical circuit comprising two main compartments
(soma and proximal dendrites on one hand, distal dendrites on the
other). Analysis of synaptic currents in terms of this model suggested
that the recorded time course of decay was approximately correct.
7. CF-EPSCs as well as PF-EPSCs were insensitive to the NMDA receptor
antagonist 3-3(2-carboxypiperazine-4-yl)propyl-1-phosphonate (CPP),
but were blocked in a dose-dependent reversible manner by the non-NMDA
antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX).(ABSTRACT
TRUNCATED AT 400 WORDS)},
address = {Laboratoire de Neurobiologie, Ecole Normale Superieure, Paris, France.},
au = {Llano, I and Marty, A and Armstrong, CM and Konnerth, A},
da = {19910603},
date-added = {2008-03-28 23:27:20 +0100},
date-modified = {2008-03-28 23:27:39 +0100},
dcom = {19910603},
edat = {1991/03/01},
gr = {NS12547/NS/United States NINDS},
issn = {0022-3751 (Print)},
jid = {0266262},
jt = {The Journal of physiology},
language = {eng},
lr = {20071114},
mh = {6-Cyano-7-nitroquinoxaline-2,3-dione; Action Potentials/drug effects/*physiology;
Animals; Bicuculline/pharmacology; Electric Stimulation; Glutamates/*physiology;
Kainic Acid/pharmacology; Membrane Potentials/physiology; Piperazines/pharmacology;
Purkinje Cells/drug effects/*physiology; Quinoxalines/pharmacology;
Quisqualic Acid/pharmacology; Rats; Receptors, Glutamate; Receptors,
N-Methyl-D-Aspartate/drug effects/physiology; Receptors, Neurotransmitter/*physiology},
mhda = {1991/03/01 00:01},
own = {NLM},
owner = {sprekeler},
pl = {ENGLAND},
pmid = {1673717},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.},
pubm = {Print},
rn = {0 (Glutamates); 0 (Piperazines); 0 (Quinoxalines); 0 (Receptors, Glutamate);
0 (Receptors, N-Methyl-D-Aspartate); 0 (Receptors, Neurotransmitter);
100828-16-8 (3-(2-carboxypiperazin-4-yl)propyl-1-phosphonic acid);
115066-14-3 (6-Cyano-7-nitroquinoxaline-2,3-dione); 485-49-4 (Bicuculline);
487-79-6 (Kainic Acid); 52809-07-1 (Quisqualic Acid)},
sb = {IM},
so = {J Physiol. 1991 Mar;434:183-213. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{London05,
author = {Michael London and Michael H{\"a}usser},
title = {Dendritic Computation},
journal = {Annual Reviews of Neuroscience},
year = {2005},
volume = {28},
pages = {503--532},
keywords = {Neuronal-Processing},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{London01,
author = {M. London and I. Segev},
title = {Synaptic scaling in vitro and in vivo},
journal = {Nature Neuroscience},
year = {2001},
volume = {4},
pages = {853-854}
}
@article{London01a,
author = {Michael London and Idan Segev and Jeffrey C. Magee and Erik P. Cook},
title = {Synaptic Scaling in vivo and in vitro},
journal = {Nature Neuroscience},
year = {2001},
volume = {4},
pages = {853-855},
number = {9},
keywords = {Neuronal-Processing},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Longtin93,
author = {A. Longtin},
title = {Stochastic Resonance in Neuron Models},
journal = {J. Stat. Phys.},
year = {1993},
volume = {70},
pages = {309-327}
}
@article{Losonczy08,
author = {Losonczy, Attila and Makara, Judit K and Magee, Jeffrey C},
title = {Compartmentalized dendritic plasticity and input feature storage
in neurons.},
journal = {Nature},
year = {2008},
volume = {452},
pages = {436--441},
number = {7186},
abstract = {Although information storage in the central nervous system is thought
to be primarily mediated by various forms of synaptic plasticity,
other mechanisms, such as modifications in membrane excitability,
are available. Local dendritic spikes are nonlinear voltage events
that are initiated within dendritic branches by spatially clustered
and temporally synchronous synaptic input. That local spikes selectively
respond only to appropriately correlated input allows them to function
as input feature detectors and potentially as powerful information
storage mechanisms. However, it is currently unknown whether any
effective form of local dendritic spike plasticity exists. Here we
show that the coupling between local dendritic spikes and the soma
of rat hippocampal CA1 pyramidal neurons can be modified in a branch-specific
manner through an N-methyl-d-aspartate receptor (NMDAR)-dependent
regulation of dendritic Kv4.2 potassium channels. These data suggest
that compartmentalized changes in branch excitability could store
multiple complex features of synaptic input, such as their spatio-temporal
correlation. We propose that this 'branch strength potentiation'
represents a previously unknown form of information storage that
is distinct from that produced by changes in synaptic efficacy both
at the mechanistic level and in the type of information stored.},
address = {Howard Hughes Medical Institute, Janelia Farm Research Campus, 19700
Helix Dr Ashburn, Virginia 20147, USA. losonczya@janelia.hhmi.org},
au = {Losonczy, A and Makara, JK and Magee, JC},
bdsk-url-1 = {http://dx.doi.org/10.1038/nature06725},
da = {20080327},
date-added = {2008-03-30 22:52:05 +0200},
date-modified = {2008-03-30 22:52:19 +0200},
doi = {10.1038/nature06725},
edat = {2008/03/28 09:00},
issn = {1476-4687 (Electronic)},
jid = {0410462},
jt = {Nature},
language = {eng},
mhda = {2008/03/28 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2007/10/23 {$[$}received{$]$}; 2008/01/24 {$[$}accepted{$]$}},
pii = {nature06725},
pl = {England},
pmid = {18368112},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
sb = {IM},
so = {Nature. 2008 Mar 27;452(7186):436-41. },
stat = {In-Process},
timestamp = {2008.04.14}
}
@article{Lu07,
author = {J. Lu and C. Li and J.-P. Zhao and Mu-ming Poo and X. Zhang},
title = {Spike-timing-dependent plasticity of neocortical excitatory synapses
on ibhibitory interneurons depends on target cell type},
journal = {J. Neuroscience},
year = {2007},
volume = {27},
pages = {9711-9720}
}
@article{Lu07a,
author = {Lu, J. and Li, C. and Zhao, J.P. and Poo, M. and Zhang, X.},
title = {Spike-Timing-Dependent Plasticity of Neocortical Excitatory Synapses
on Inhibitory Interneurons Depends on Target Cell Type},
journal = {Journal of Neuroscience},
year = {2007},
volume = {27},
pages = {9711},
number = {36},
owner = {sprekeler},
publisher = {Soc Neuroscience},
timestamp = {2008.04.14}
}
@article{Ludvig04,
author = {N. Ludvig and H.M. Tang and B.C. Gohil and J.M. Botero},
title = {Detecting location-specific neuronal firing rate increases in the
hippocampus of freely-moving monkeys},
journal = {Brain Research},
year = {2004},
volume = {1014},
pages = {97--109},
number = {1--2},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Luksys07,
author = {G. Luksys and J. Knuesel and D. Sheynikhovich and C. Sandi and W.
Gerstner},
title = {Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement
Learning},
journal = {Advances in Neural Information Processing Systems 19},
year = {2007},
volume = {19},
pages = {937--944}
}
@article{Lytton91,
author = {W. W. Lytton and T. J. Sejnowsky},
title = {Simulations of cortical pyramidal neurons synchronized by inhibitory
interneurons},
journal = {J. Neurophysiology},
year = {1991},
volume = {66},
pages = {1059-1079}
}
@article{Lorincz00,
author = {A. L{\"o}rincz and G. Buzs{\'a}ki},
title = {Two-Phase Computational Model Training Long-Term Memories in the
Entorhinal-Hippocampal Region},
journal = {Annals of the New York Academy of Sciences},
year = {2000},
volume = {911},
pages = {83--111},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Hasler00,
author = {M. Hasler, T. Schimming},
title = {Chaos communication over noisy channels},
journal = {Intl. Journal of Bifurcations and Chaos},
year = {2000},
volume = {10},
pages = {719-736},
annote = {Hasler - paper cited in grant proposal}
}
@article{London02,
author = {M. London and A. Schreibman and M. H\"ausser and M. E. Larkum and I. Segev},
title = {The information efficacy of a synapse},
journal = {Nature Neuroscience},
year = {2002},
volume = {4},
pages = {332-340}
}
@article{Kilgard02,
author = {M. P. Kilgard and P. K. Pandya and N. D. Engineer and R. Moucha},
title = {Cortical network reorganization guided by sensory input features},
journal = {Biol. Cybernetics},
year = {2002},
volume = {87},
pages = {333-343}
}
@article{Mueller88,
author = {M. M\"uller and R. Wehner},
title = {{Path integration in desert ants, {\em Cataglyphis fortis}}},
journal = {Proc. Nat. Acad. Sci.},
year = {1988},
volume = {85d},
pages = {5287--5290}
}
@book{Mueller91,
title = {Neural networks: An introduction},
publisher = {Springer-Verlag},
year = {1991},
author = {B. M\protect{\"u}ller and J. Reinhard},
address = {Berlin Heidelberg New York}
}
@article{Maass00,
author = {W. Maas and T. Nathschl\protect{\"a}ger},
title = {A model for fast analog computation based on unreliable synapses},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {1679-1704}
}
@incollection{Maass95a,
author = {Wolfgang Maass},
title = {On the Computational Complexity of networks of spiking neurons},
booktitle = {Advancs in Neural Information Processing Systems 7},
publisher = {MIT-Press},
year = {1995},
editor = {xxx},
pages = {183-190}
}
@incollection{Maass98,
author = {W. Maass},
title = {Computing with spiking neurons},
booktitle = {Pulsed Neural Networks},
publisher = {MIT-Press},
year = {1998},
editor = {W. Maass and C.M. Bishop},
chapter = {2},
pages = {55-85}
}
@article{Maass96,
author = {Wolfgang Maass},
title = {Lower bounds for the computational power of spiking neurons},
journal = {Neural Comput.},
year = {1996},
volume = {8},
pages = {1-40}
}
@book{Maass98b,
title = {Pulsed Neural Networks},
publisher = {MIT-Press},
year = {1998},
author = {W. Maass and C. Bishop}
}
@article{Maass02,
author = {W. Maass and T. Natschl{\"a}ger and H. Markram},
title = {Real-time computing without stable states: a new framework for neural
computation based on perturbations},
journal = {Neural Computation},
year = {2002},
pages = {2531-2560}
}
@book{MacGregor87,
title = {Neural and Brain Modeling},
publisher = {Academic Press},
year = {1987},
author = {Ronald J. MacGregor},
address = {San Diego}
}
@article{MacGregor74,
author = {R. J. MacGregor and R. M. Oliver},
title = {A model for repetitive firing in neurons},
journal = {Kybernetik},
year = {1974},
volume = {16},
pages = {53-64}
}
@article{MacKay90,
author = {D. J. C. MacKay and K. D. Miller},
title = {Analysis of Linsker's application of Hebbian rules to linear networks},
journal = {Network},
year = {1990},
volume = {1},
pages = {257-297}
}
@article{MacKay52,
author = {D. M. MacKay and W. S. McCulloch},
title = {The limiting information capacity of a neuronal link.},
journal = {Bull. of Mathm. Biophysics},
year = {1952},
volume = {14},
pages = {127--135}
}
@article{Mackintosh75,
author = {N. J. Mackintosh},
title = {A theory of attention: variations in the associability of stimulus
with reinforcement},
journal = {Psychol. Rev.},
year = {1975},
volume = {82},
pages = {276-298}
}
@article{Magee99,
author = {Magee, J C},
title = {Dendritic lh normalizes temporal summation in hippocampal CA1 neurons.},
journal = {Nat Neurosci},
year = {1999},
volume = {2},
pages = {508--514},
number = {6},
abstract = {Most mammalian central neurons receive synaptic input over complicated
dendritic arbors. Therefore, timing of synaptic information should
vary with synapse location. However, I report that temporal summation
at CA1 pyramidal somata does not depend on the location of synaptic
input. This spatial normalization of temporal integration requires
a dendritic hyperpolarization-activated current (lh). Shaping of
synaptic activity by deactivating a nonuniform lh could counterbalance
filtering by dendrites and effectively remove location-dependent
variability in temporal integration, thus enhancing synchronization
of neuronal populations and functional capabilities of the hippocampal
CA1 region.},
address = {Neuroscience Center, Louisiana State University Medical Center, 2020
Gravier St., New Orleans, Louisiana 70112, USA. jmagee@lsumc.edu},
au = {Magee, JC},
bdsk-url-1 = {http://dx.doi.org/10.1038/9158},
da = {19990902},
date-added = {2008-03-28 23:31:38 +0100},
date-modified = {2008-03-28 23:31:41 +0100},
dcom = {19990902},
doi = {10.1038/9158},
edat = {1999/08/17 10:00},
gr = {NS35865/NS/United States NINDS},
issn = {1097-6256 (Print)},
jid = {9809671},
jt = {Nature neuroscience},
language = {eng},
lr = {20071114},
mh = {Animals; Dendrites/*physiology; Electric Conductivity; Electric Stimulation;
Excitatory Postsynaptic Potentials/physiology; Hippocampus/cytology/*physiology;
Neurons/*physiology; Rats; Rats, Sprague-Dawley; Synapses/physiology;
Time Factors},
mhda = {2001/03/23 10:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {10448214},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.},
pubm = {Print},
sb = {IM},
so = {Nat Neurosci. 1999 Jun;2(6):508-14. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Magee00,
author = {J. C. Magee and E. P. Cook},
title = {Somatic EPSP amplitude is independent of synapse location in hippocampal
pyramidal neurons},
journal = {Nature Neuroscience},
year = {2000},
volume = {3},
pages = {895-903}
}
@article{Magee00a,
author = {Jeffrey C. Magee and Erik P. Cook},
title = {Somatic {EPSP} amplitude is independent of synapse location in hippocampal
pyramidal neurons},
journal = {Nature Neuroscience},
year = {2000},
volume = {3},
pages = {895--903},
number = {9},
keywords = {Neuronal-Processing},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Magee05,
author = {Jeffrey C. Magee and Daniel Johnston},
title = {Plasticity of dendritic function},
journal = {Current Opinion in Neurobiology},
year = {2005},
volume = {15},
pages = {334--342},
keywords = {Neuronal-Processing, Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Magee97,
author = {J. C. Magee and D. Johnston},
title = {A synaptically controlled associative signal for Hebbian plastiticy
in hippocampal neurons},
journal = {Science},
year = {1997},
volume = {275},
pages = {209-213}
}
@article{Magee97a,
author = {Jeffrey C. Magee and Daniel Johnston},
title = {A Synaptically Controlled, Associative Signal for Hebbian Plasticity
in Hippocampal Neurons},
journal = {Science},
year = {1997},
volume = {275},
pages = {209--213},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Magnus88,
title = {Matrix Differential Calculus with Applications in Statistics and
Econometrics},
publisher = {Wiley},
year = {1988},
author = {J. R. Magnus and H. Neudecker},
address = {New York},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Maguire98,
author = {E.A. Maguire and N. Burgess and J.G. Donnet and R.S. J. Frackowiak
and C.D. Frith and J. O'Keefe},
title = {{Knowing where and getting there: A human navigation network}},
journal = {Science},
year = {1998},
volume = {280},
pages = {921--924}
}
@article{Mainen95b,
author = {Mainen, Z F and Joerges, J and Huguenard, J R and Sejnowski, T J},
title = {A model of spike initiation in neocortical pyramidal neurons.},
journal = {Neuron},
year = {1995},
volume = {15},
pages = {1427--1439},
number = {6},
abstract = {Neocortical pyramidal cells possess voltage-dependent dendritic sodium
channels that promote propagation of action potentials into the dendritic
tree but paradoxically may fail to originate dendritic spikes. A
biophysical model was constructed to reconcile these observations
with known anatomical and physiological properties. When dendritic
and somatic sodium channel densities compatible with electrophysiological
measurements were combined with much higher densities in the axon
initial segment then, regardless of the site of stimulation, spikes
initiated at the initial segment and subsequently invaded the dendrites.
The lower initial segment threshold arose from high current density
and electrical isolation from the soma. Failure of dendritic channels
to initiate spikes was due to inactivation and source-load considerations,
which were more favorable for conduction of back-propagated spikes.},
address = {Howard Hughes Medical Institute, Computational Neurobiology Laboratory,
Salk Institute for Biological Studies, La Jolla, California 92037,
USA.},
au = {Mainen, ZF and Joerges, J and Huguenard, JR and Sejnowski, TJ},
da = {19961021},
date-added = {2007-12-12 19:58:32 +0100},
date-modified = {2007-12-12 19:58:53 +0100},
dcom = {19961021},
edat = {1995/12/01},
gr = {NS06477/NS/United States NINDS; NS12151/NS/United States NINDS},
issn = {0896-6273 (Print)},
jid = {8809320},
jt = {Neuron},
keywords = {Action Potentials; Animals; Axons/physiology; Dendrites/physiology;
Models, Neurological; Pyramidal Cells/*physiology; Rats},
language = {eng},
lr = {20071114},
mhda = {1995/12/01 00:01},
own = {NLM},
owner = {sprekeler},
pii = {0896-6273(95)90020-9},
pl = {UNITED STATES},
pmid = {8845165},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.},
pubm = {Print},
sb = {IM},
so = {Neuron. 1995 Dec;15(6):1427-39. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Mainen96,
author = {Z. F. Mainen and Terrence J. Sejnowski},
title = {Influence of dendritic structure on firing pattern in model neocortical
neurons},
journal = {Nature},
year = {1996},
volume = {382},
pages = {363-366}
}
@article{Mainen96a,
author = {Mainen, Z F and Sejnowski, T J},
title = {Influence of dendritic structure on firing pattern in model neocortical
neurons.},
journal = {Nature},
year = {1996},
volume = {382},
pages = {363--366},
number = {6589},
abstract = {Neocortical neurons display a wide range of dendritic morphologies,
ranging from compact arborizations to highly elaborate branching
patterns. In vitro electrical recordings from these neurons have
revealed a correspondingly diverse range of intrinsic firing patterns,
including non-adapting, adapting and bursting types. This heterogeneity
of electrical responsivity has generally been attributed to variability
in the types and densities of ionic channels. We show here, using
compartmental models of reconstructed cortical neurons, that an entire
spectrum of firing patterns can be reproduced in a set of neurons
that share a common distribution of ion channels and differ only
in their dendritic geometry. The essential behaviour of the model
depends on partial electrical coupling of fast active conductances
localized to the soma and axon and slow active currents located throughout
the dendrites, and can be reproduced in a two-compartment model.
The results suggest a causal relationship for the observed correlations
between dendritic structure and firing properties and emphasize the
importance of active dendritic conductances in neuronal function.},
address = {Howard Hughes Medical Institute, Computational Neurobiology Laboratory,
Salk Institute for Biological Studies, La Jolla, California 92037,
USA.},
au = {Mainen, ZF and Sejnowski, TJ},
bdsk-url-1 = {http://dx.doi.org/10.1038/382363a0},
da = {19960820},
date-added = {2008-03-28 23:26:24 +0100},
date-modified = {2008-03-28 23:26:30 +0100},
dcom = {19960820},
doi = {10.1038/382363a0},
edat = {1996/07/25},
issn = {0028-0836 (Print)},
jid = {0410462},
jt = {Nature},
language = {eng},
lr = {20061115},
mh = {Action Potentials; Calcium Channels/metabolism; Cerebral Cortex/*cytology/physiology;
Dendrites/*physiology; Models, Neurological; Neurons/cytology/*physiology;
Potassium Channels/metabolism},
mhda = {1996/07/25 00:01},
own = {NLM},
owner = {sprekeler},
pl = {ENGLAND},
pmid = {8684467},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
rn = {0 (Calcium Channels); 0 (Potassium Channels)},
sb = {IM},
so = {Nature. 1996 Jul 25;382(6589):363-6. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Mainen95,
author = {Z. F. Mainen and Terrence J. Sejnowski},
title = {Reliability of Spike Timing in Neocortical Neurons},
journal = {Science},
year = {1995},
volume = {268},
pages = {1503-1506}
}
@article{Mainen95a,
author = {Mainen, Z F and Sejnowski, T J},
title = {Reliability of spike timing in neocortical neurons.},
journal = {Science},
year = {1995},
volume = {268},
pages = {1503--1506},
number = {5216},
abstract = {It is not known whether the variability of neural activity in the
cerebral cortex carries information or reflects noisy underlying
mechanisms. In an examination of the reliability of spike generation
using recordings from neurons in rat neocortical slices, the precision
of spike timing was found to depend on stimulus transients. Constant
stimuli led to imprecise spike trains, whereas stimuli with fluctuations
resembling synaptic activity produced spike trains with timing reproducible
to less than 1 millisecond. These data suggest a low intrinsic noise
level in spike generation, which could allow cortical neurons to
accurately transform synaptic input into spike sequences, supporting
a possible role for spike timing in the processing of cortical information
by the neocortex.},
address = {Howard Hughes Medical Institute, Salk Institute for Biological Studies,
La Jolla, CA 92037, USA.},
au = {Mainen, ZF and Sejnowski, TJ},
da = {19950705},
date-added = {2008-03-28 23:26:24 +0100},
date-modified = {2008-03-28 23:26:36 +0100},
dcom = {19950705},
edat = {1995/06/09},
issn = {0036-8075 (Print)},
jid = {0404511},
jt = {Science (New York, N.Y.)},
language = {eng},
lr = {20070319},
mh = {Animals; Electric Stimulation; *Evoked Potentials; Neurons/*physiology;
Occipital Lobe/cytology/*physiology; Rats; Rats, Sprague-Dawley;
*Synaptic Transmission},
mhda = {1995/06/09 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {7770778},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {Science. 1995 Jun 9;268(5216):1503-6. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Maistrenko00,
author = {Y. Maistrenko and O. Popovych and M. Hasler},
title = {On strong and weak chaotic partial synchronization},
journal = {Journal of Bifurcations and Chaos},
year = {2000},
volume = {10},
pages = {179-203},
annote = {Hasler - paper cited in grant proposal}
}
@article{Malenka99a,
author = {Malenka, R.C. and Nicoll, R.A.},
title = {{Long-Term Potentiation--A Decade of Progress?}},
journal = {Science},
year = {1999},
volume = {285},
pages = {1870--1874},
number = {5435},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Malenka04,
author = {Robert C. Malenka and Mark F. Bear},
title = {{LTP} and {LTD}: An Embarassment of Riches},
journal = {Neuron},
year = {2004},
volume = {44},
pages = {5--21},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Malenka89,
author = {R. C. Malenka and J. A. Kauer and D. J. Perkel and M. D. Mauk and
P. T. Kelly},
title = {An essential role for postsynaptic calmodulin and protein kinase
activity in long-term potentiation},
journal = {Nature},
year = {1989},
volume = {340},
pages = {554-557}
}
@article{Malenka88,
author = {R. C. Malenka and J.A. Kauer and R.S. Zucker and R. A. Nicoll},
title = {Postsynaptic calcium is sufficient for potentiation of hippocampal
synaptic transmission},
journal = {Science},
year = {1988},
volume = {242},
pages = {81-84}
}
@article{Malenka92,
author = {R. C. Malenka and B. Lancaster and R. S. Zucker},
title = {Temporal limits on the rise in postsynaptic calcium required for
the induction of long-term potentiation.
},
journal = {Neuron},
year = {1992},
volume = {9},
pages = {121-128}
}
@article{Malenka99,
author = {R. C. Malenka and R. A. Nicoll},
title = {Long-Term Potentiation--A Decade of Progress? },
journal = {Science},
year = {1999},
volume = {285},
pages = {1870-1874}
}
@article{Malenka93,
author = {R. C. Malenka and R. A. Nicoll},
title = {NMDA-receptor-dependent plasticity: multiple forms and mechanisms},
journal = {Trends Neurosci.},
year = {1993},
volume = {16},
pages = {480-487}
}
@article{Malinow03,
author = {R. Malinow},
title = {AMPA receptor trafficking and long-term potentiation},
journal = {Phil. Trans. R. Soc. Lond B: Biological Sciences},
year = {2003},
volume = {358},
pages = {707 - 714}
}
@article{Malinow86,
author = {R. Malinow and J. P. Miller},
title = {Synaptic hyperpolarization during conditioning reversibly blocks
induction of long-term potentiation.},
journal = {Nature},
year = {1986},
volume = {320},
pages = {529--530}
}
@article{Malinow89,
author = {R. Malinow and H. Schulman and R. W. Tsien},
title = {Inhibition of postsynaptic {PKC} or {CaMKII} blocks induction but
not expression of LTP},
journal = {Science},
year = {1989},
volume = {245},
pages = {862-866}
}
@article{Mallot00,
author = {H. A. Mallot and S. Gillner},
title = {Route navigation without place recognition: What is recognized in
recognition--triggered responses?},
journal = {Perception},
year = {2000},
volume = {29},
pages = {43-45}
}
@inproceedings{Malsburg94,
author = {C. von der Malsburg},
title = {The correltaion theory of brain function.},
booktitle = {Models of neural networks II},
year = {1994},
editor = {E. Domany and J. L. van Hemmen and K. Schulten},
pages = {95-119},
address = {New York},
publisher = {Springer-Verlag},
anote = {chap. 2}
}
@inproceedings{Malsburg86b,
author = {C. von der Malsburg},
title = {Am I thinking assemblies?},
booktitle = {Brain theory},
year = {1986},
editor = {G. Palm and A. Aertsen},
pages = {161-176},
address = {Berlin Heidelberg New York},
publisher = {Springer-Verlag}
}
@techreport{Malsburg81,
author = {C. von~der~Malsburg},
title = {The correlation theory of brain function.},
institution = {MPI f\protect{\"{u}}r Biophysikalische Chemie},
year = {1981},
type = {Internal Report},
number = {81-2},
address = {G\protect{\"{o}}ttingen},
note = {Reprinted in Models of Neural Networks II, Domany et al. (Eds.),
Springer, 1994, pp.95-119}
}
@article{Malsburg73,
author = {C. von der Malsburg},
title = {Self-organization of orientation selective cells in the striate cortex},
journal = {Kybernetik},
year = {1973},
volume = {14},
pages = {85-100}
}
@article{Malsburg92,
author = {C. von der Malsburg and J. Buhmann},
title = {Sensory segmentation with coupled neural oscillators.},
journal = {Biol. Cybern.},
year = {1992},
volume = {67},
pages = {233--242}
}
@article{Malsburg86,
author = {C. von der Malsburg and W. Schneider},
title = {A neural cocktail--party processor.},
journal = {Biol. Cybern.},
year = {1986},
volume = {54},
pages = {29--40}
}
@article{Manis91,
author = {P. B. Manis and S. O. Marx},
title = {xxx},
journal = {J. Neurosci.},
year = {1991},
volume = {11},
pages = {2865-2800}
}
@article{Manwani99a,
author = {A. Manwani and C. Koch},
title = {Detecting and Estimating Signals in Noisy Cable Structures, {I}:
Neuronal Noise Sources},
journal = {Neural Computation},
year = {1999},
volume = {11},
pages = {1797--1829},
comment = {Detailed analysis of different noise sources in spectral domain, attempting
to estimate influence on passive cable. First derive multiplicative
noise model, but then reduce to leaky intgrator for tractability.},
subject = {Theoretical neuroscience},
topic = {Cable theory, information theory, noise sources, shot noise, synaptic
noise, channel noise}
}
@article{Manwani99b,
author = {A. Manwani and C. Koch},
title = {Detecting and Estimating Signals in Noisy Cable Structures, {II}:
Information Theoretical Analysis},
journal = {NEURAL COMPUTATION},
year = {1999},
volume = {11},
pages = {1831--1873},
comment = {Sequel to Manw:1999(1797). On basis of those results now look at signal
estimation and detection properties of cables. Suggest that dendrites
must have nonlinear mechanisms to maintain decent SNR along cable,
but that SNR loss may also limit length of dendrites.},
subject = {Stochastic Processes},
topic = {Simulation, stochastic differential equation, colored noise, white
noise, Gaussian noise, nonlinear SDE, signal estimation, signal detection}
}
@article{Mar99,
author = {E. J. Mar and C. C. Chow and W. Gerstner and R.W. Adams and J.J.Collins},
title = {Noise-shaping in populations of coupled model neurons},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1999},
volume = {96},
pages = {10450-10455}
}
@article{Marcus89a,
author = {C. M. Marcus and R. M. Westervelt},
title = {Stability of analog neural networks with delays},
journal = {Phys.~Rev. A},
year = {1989},
volume = {39},
pages = {347--359}
}
@article{Marcus89b,
author = {C. M. Marcus and R. M. Westervelt},
title = {Dynamics of iterated map networks},
journal = {Phys.~Rev. A},
year = {1989},
volume = {40},
pages = {501--504}
}
@article{Markram06,
author = {Markram, Henry},
title = {The blue brain project.},
journal = {Nat Rev Neurosci},
year = {2006},
volume = {7},
pages = {153--160},
number = {2},
abstract = {IBM's Blue Gene supercomputer allows a quantum leap in the level of
detail at which the brain can be modelled. I argue that the time
is right to begin assimilating the wealth of data that has been accumulated
over the past century and start building biologically accurate models
of the brain from first principles to aid our understanding of brain
function and dysfunction.},
address = {Laboratory of Neural Microcircuitry, Brain Mind Institute, Ecole
Polytechnique Federale de Lausanne, Lausanne 1015, Switzerland. henry.markram@epfl.ch},
au = {Markram, H},
bdsk-url-1 = {http://dx.doi.org/10.1038/nrn1848},
da = {20060123},
date-added = {2007-12-05 18:23:13 +0100},
date-modified = {2007-12-05 18:23:29 +0100},
dcom = {20060330},
doi = {10.1038/nrn1848},
edat = {2006/01/24 09:00},
issn = {1471-003X (Print)},
jid = {100962781},
jt = {Nature reviews. Neuroscience},
keywords = {Animals; *Brain; Humans; *Models, Neurological; *Neural Networks (Computer);
Quantum Theory},
language = {eng},
lr = {20061115},
mhda = {2006/03/31 09:00},
own = {NLM},
owner = {sprekeler},
pii = {nrn1848},
pl = {England},
pmid = {16429124},
pst = {ppublish},
pt = {Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Review},
pubm = {Print},
rf = {45},
sb = {IM},
so = {Nat Rev Neurosci. 2006 Feb;7(2):153-60. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Markram97,
author = {H. Markram and J. L\protect{\"u}bke and M. Frotscher and B. Sakmann},
title = {Regulation of synaptic efficacy by coincidence of postysnaptic {AP}
and {EPSP}},
journal = {Science},
year = {1997},
volume = {275},
pages = {213-215}
}
@article{Markram97c,
author = {Henry Markram and Joachim L{\"u}bke and Michael Frotscher and Bert
Sakmann},
title = {Regulation of Synaptic Efficacy by Coincidence of Postsynaptic {AP}s
and {EPSP}s},
journal = {Science},
year = {1997},
volume = {275},
pages = {213--215},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Markram97d,
author = {Markram, H. and L{\"u}bke, J. and Frotscher, M. and Sakmann, B.},
title = {{Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs
and EPSPs}},
journal = {Science},
year = {1997},
volume = {275},
pages = {213--215},
number = {5297},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Markram95,
author = {H. Markram and B. Sakmann},
title = {Action potentials propagating back into dendrites trigger changes
in efficacy of single-axon synapses between layer V pyramidal neurons},
journal = {Soc. Neurosci. Abstr.},
year = {1995},
volume = {21},
pages = {2007},
number = {3}
}
@article{Markram04,
author = {H. Markram and M. Toledo-Rodrgiguez and Yun Wang and A. Gupta and
G. Silberberg and C. Wu},
title = {Interneurons of the neocortical inhibitory system},
journal = {Nature Review Neuroscienc},
year = {2004},
volume = {5},
pages = {793-807}
}
@article{Markram04a,
author = {Markram, Henry and Toledo-Rodriguez, Maria and Wang, Yun and Gupta,
Anirudh and Silberberg, Gilad and Wu, Caizhi},
title = {Interneurons of the neocortical inhibitory system.},
journal = {Nat Rev Neurosci},
year = {2004},
volume = {5},
pages = {793--807},
number = {10},
abstract = {Mammals adapt to a rapidly changing world because of the sophisticated
cognitive functions that are supported by the neocortex. The neocortex,
which forms almost 80% of the human brain, seems to have arisen from
repeated duplication of a stereotypical microcircuit template with
subtle specializations for different brain regions and species. The
quest to unravel the blueprint of this template started more than
a century ago and has revealed an immensely intricate design. The
largest obstacle is the daunting variety of inhibitory interneurons
that are found in the circuit. This review focuses on the organizing
principles that govern the diversity of inhibitory interneurons and
their circuits.},
address = {Laboratory of Neural Microcircuitry, Brain Mind Institute, Ecole
Polytechnique Federale de Lausanne, 1015 Lausanne, Switzerland. Henry.markram@epfl.ch},
au = {Markram, H and Toledo-Rodriguez, M and Wang, Y and Gupta, A and Silberberg,
G and Wu, C},
bdsk-url-1 = {http://dx.doi.org/10.1038/nrn1519},
da = {20040920},
date-added = {2007-12-05 18:23:13 +0100},
date-modified = {2007-12-05 18:24:35 +0100},
dcom = {20041116},
doi = {10.1038/nrn1519},
edat = {2004/09/21 05:00},
issn = {1471-003X (Print)},
jid = {100962781},
jt = {Nature reviews. Neuroscience},
keywords = {Animals; Axons/physiology; Calcium-Binding Proteins/metabolism; Dendrites/physiology;
Electrophysiology/methods; Humans; Interneurons/classification/cytology/*physiology;
Ion Channels/physiology; Membrane Potentials/physiology; Neocortex/*cytology;
Nerve Net/cytology/physiology; Neural Inhibition/*physiology; Neurons/classification/cytology/physiology;
Neuropeptides/metabolism; Synapses/classification/physiology; Synaptic
Transmission/physiology},
language = {eng},
lr = {20061115},
mhda = {2004/11/17 09:00},
own = {NLM},
owner = {sprekeler},
pii = {nrn1519},
pl = {England},
pmid = {15378039},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, Non-P.H.S.; Review},
pubm = {Print},
rf = {167},
rn = {0 (Calcium-Binding Proteins); 0 (Ion Channels); 0 (Neuropeptides)},
sb = {IM},
so = {Nat Rev Neurosci. 2004 Oct;5(10):793-807. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@incollection{Markram97b,
author = {H. Markram and M. Tsodyks},
title = {The information content of action potential trains: a synaptic basis},
booktitle = {Artificial Neural Networks - ICANN'97},
publisher = {Springer},
year = {1997},
editor = {W. Gerstner and A. Germond and M. Hasler and J.-d. Nicoud},
series = {Lecture Notes in Computer Science, 1327}
}
@article{Markram97a,
author = {Henry Markram and Misha Tsodyks},
title = {Redistribution of synaptic efficacy between neocortical pyramidal
neurons},
journal = {Nature},
year = {1997},
volume = {382},
pages = {807--810},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Markram96,
author = {H. Markram and M. Tsodyks},
title = {Redistribution of synaptic efficacy between neocortical pyramidal
neurons},
journal = {Nature},
year = {1996},
volume = {382},
pages = {807-810},
anote = {for the algorithm see the paper Markram98a}
}
@article{Markram98a,
author = {H. Markram and Y. Wu and M. Tosdyks},
title = {Differential signaling via the same axon of neocortical pyramidal
neurons},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1998},
volume = {95},
pages = {5323-5328}
}
@article{Markus95,
author = {E. J. Markus and Y. L. Qin and B. Leonard and W. E. Skaggs and B.
L. McNaughton and C. A. Barnes},
title = {Interactions between location and task affect the spatial and directional
firing of hippocampal neurons},
journal = {Journal of Neuroscience},
year = {1995},
volume = {15},
pages = {7079--7094},
number = {11},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Marsalek97,
author = {P. Mars{a}lek and C. Koch and J. Maunsell},
title = {On the relationship between synaptic input and spike output jitter
in individual neurons},
journal = {Proc. Natl. Acad. Sci. USA},
year = {1997},
volume = {94},
pages = {735--740},
annote = {time coding, spike precision}
}
@article{Martinez-Conde04,
author = {Martinez-Conde, Susana and Macknik, Stephen L and Hubel, David H},
title = {The role of fixational eye movements in visual perception.},
journal = {Nature Reviews Neuroscience},
year = {2004},
volume = {5},
pages = {229--240},
number = {3},
month = {Mar},
keywords = {Vision,Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1038/nrn1348}
}
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author = {M. Mascaro and D. J. Amit},
title = {Effective neural response function for collective population states},
journal = {Network},
year = {1999},
volume = {10},
pages = {351-373}
}
@article{Masquelier08,
author = {Timothée Masquelier and Rudy Guyonneau and Simon J Thorpe},
title = {Spike timing dependent plasticity finds the start of repeating patterns
in continuous spike trains.},
journal = {PLoS ONE},
year = {2008},
volume = {3},
pages = {e1377},
number = {1},
abstract = {Experimental studies have observed Long Term synaptic Potentiation
(LTP) when a presynaptic neuron fires shortly before a postsynaptic
neuron, and Long Term Depression (LTD) when the presynaptic neuron
fires shortly after, a phenomenon known as Spike Timing Dependant
Plasticity (STDP). When a neuron is presented successively with discrete
volleys of input spikes STDP has been shown to learn 'early spike
patterns', that is to concentrate synaptic weights on afferents that
consistently fire early, with the result that the postsynaptic spike
latency decreases, until it reaches a minimal and stable value. Here,
we show that these results still stand in a continuous regime where
afferents fire continuously with a constant population rate. As such,
STDP is able to solve a very difficult computational problem: to
localize a repeating spatio-temporal spike pattern embedded in equally
dense 'distractor' spike trains. STDP thus enables some form of temporal
coding, even in the absence of an explicit time reference. Given
that the mechanism exposed here is simple and cheap it is hard to
believe that the brain did not evolve to use it.},
doi = {10.1371/journal.pone.0001377},
keywords = {plasticity},
owner = {sprekeler},
pmid = {18167538},
timestamp = {2008.04.16},
url = {http://dx.doi.org/10.1371/journal.pone.0001377}
}
@article{Masuda07,
author = {Masuda, N. and Kori, H.},
title = {{Formation of feedforward networks and frequency synchrony by spike-timing-dependent
plasticity}},
journal = {Journal of Computational Neuroscience},
year = {2007},
volume = {22},
pages = {327--345},
number = {3},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Springer},
timestamp = {2008.04.14}
}
@inproceedings{Matas00barcelona,
author = {J. Matas and M. Hamouz and K. Jonsson and J. Kittler and Y. Li and
C. Kotropoulos and A. Tefas and I. Pitas and T. Tan and H. Yan and
F. Smeraldi and J. Big\"un and N. Capdevielle and W. Gerstner and
S. Ben-Yacoub and Y. Abdeljaoued and E. Mayoraz},
title = {Comparison of face verification results on the {XM2VTS} database},
booktitle = {Proceedings of the 15th International Conference on Pattern Recognition,
Barcelona (Spain), September 2000},
year = {2000},
volume = {4},
pages = {858--863},
month = {September},
publisher = {IEEE Comp. Soc. Order No. PR00750}
}
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author = {G. Mato and H. Sompolinski},
title = {Neural network models of perceptual learning of angle discrimination},
journal = {Neural Computation},
year = {1996},
volume = {8},
pages = {270-299},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {N. Matsumoto and M. Okada},
title = {Self-regulation mechanism of temporally asymmetric {H}ebbian plasticity},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {2883-2902},
annote = {Time window with integral zero, used in an associative memory for
spatio-temporal patterns, mathematical analysis of storage capacity}
}
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author = {N. Matthews and Z. Liu and B. J. Geesaman and N. Qyan},
title = {Perceptual learning on orientation and direction discrimination},
journal = {Vision Research},
year = {1999},
volume = {39},
pages = {3692-3701},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Matthews01,
author = {N. Matthews and Z. Liu and N. Qian},
title = {The effect of orientation learning on contrast sensitivity},
journal = {Vision Research},
year = {2001},
volume = {41},
pages = {463-471},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Matthews97,
author = {N. Matthews and L. Welch},
title = {Velocity-dependent improvements in single-dot direction discrimination},
journal = {Perception and Psychophysics},
year = {1997},
volume = {59},
pages = {60-72},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Mattia02,
author = {M. Mattia and P. {Del Giudice}},
title = {On the population dynamics of interacting spiking neurons},
journal = {Phys. Rev. E},
year = {2002},
volume = {xx},
pages = {xx}
}
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author = {M. Mattia and P. {Del Guidice}},
title = {Efficient event-driven simulation of large networks of spiking neurons
and dynamical synapses},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {2305-2329}
}
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author = {Mauro, A and Conti, F and Dodge, F and Schor, R},
title = {Subthreshold behavior and phenomenological impedance of the squid
giant axon.},
journal = {J Gen Physiol},
year = {1970},
volume = {55},
pages = {497--523},
number = {4},
au = {Mauro, A and Conti, F and Dodge, F and Schor, R},
da = {19700505},
date-added = {2008-03-14 16:50:44 +0100},
date-modified = {2008-03-14 16:50:45 +0100},
dcom = {19700505},
edat = {1970/04/01},
issn = {0022-1295 (Print)},
jid = {2985110R},
jt = {The Journal of general physiology},
language = {eng},
lr = {20031114},
mh = {Animals; Axons/*physiology; *Membrane Potentials; Models, Neurological;
Mollusca; *Neural Conduction; Potassium/physiology; Sodium/physiology},
mhda = {1970/04/01 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {5435782},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
rn = {7440-09-7 (Potassium); 7440-23-5 (Sodium)},
sb = {IM},
so = {J Gen Physiol. 1970 Apr;55(4):497-523. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Mayer84,
author = {M. L. Mayer and G. L. Westbrook and P. B. Guthrie},
title = {Voltage-dependent block by {$Mg^{2+}$} of NMDA responses in spinal
cord neurones},
journal = {Nature},
year = {1984},
volume = {309},
pages = {261-263}
}
@inproceedings{Mayor03,
author = {J. Mayor and W. Gerstner},
title = {Online processing of multiple inputs in a sparsely-connected recurrent
neural network},
booktitle = {Proceedings of the Joint International Conference ICANN/ICONIP 2003},
year = {2003},
editor = {O. Kaynak and E. Alpaydin and E. Oja and L. Xu},
pages = {839-845},
address = {Heidelberg},
publisher = {Springer-Verlag }
}
@article{Mayor05a,
author = {J. Mayor and W. Gerstner},
title = {Signal buffering in random networks of spiking neurons: microscopic
vs. macroscopic phenomena},
journal = {Phys. Rev. E},
year = {2005},
volume = {72},
pages = {051906}
}
@article{Mayor04,
author = {J. Mayor and W. Gerstner},
title = {Transient information flow in a network of excitatory and inhibitory
model neurons: role of noise and signal autocorrelation},
journal = {Journal of Physiology (Paris)},
year = {2004},
volume = {98},
pages = {417-428}
}
@article{Mayor05b,
author = {J. Mayor and W. Gerstner},
title = {Noise-enhanced computation in a model of a cortical column},
journal = {Neuroreport},
year = {2004},
volume = {16},
pages = {1237-1240}
}
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author = {M.E. Mazurek and M.N. Shadlen},
title = {Limits to the temporal fidelity of cortical spike rate signals},
journal = {Nature Neuroscience},
year = {2002},
volume = {5},
pages = {463-471}
}
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Models of Learning and Memory},
journal = {Psychological Review},
year = {1995},
volume = {102},
pages = {419--457},
number = {3},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {D. A. McCormick},
title = {Membrane properties and neurotransmitter actions.},
booktitle = {The synaptic organization of the brain.},
year = {1990},
editor = {Sheperd GM},
address = {Oxford},
publisher = {Oxford Univ. Press},
edition = {3rd}
}
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author = {McCormick, D A and Wang, Z and Huguenard, J},
title = {Neurotransmitter control of neocortical neuronal activity and excitability.},
journal = {Cereb Cortex},
year = {1993},
volume = {3},
pages = {387--398},
number = {5},
abstract = {The pattern of activity and excitability of cortical neurons and neuronal
circuits is dependent upon the interaction between glutamatergic
and GABAergic fast-activating transmitter systems as well as the
state of the more slowly acting transmitters such as ACh, norepinephrine,
5-HT, and histamine. Through the activation of GABAA receptors, GABAergic
neurons regulate the amplitude and duration of EPSPs and, in so doing,
control the level of functional activation of NMDA receptors. In
contrast, activation of muscarinic, adrenergic, serotoninergic, histaminergic,
and glutamate metabotropic receptors controls the excitability and
pattern of action potential generation in identified pyramidal cells
through increases or decreases in various K+ conductances. Activation
of muscarinic, alpha 1-adrenergic, or glutamate metabotropic receptors
on layer V burst-generating corticotectal or corticopontine neurons
results in depolarization through a reduction in a K+ conductance
and a switch in the firing mode from repetitive burst firing to single-spike
activity. In contrast, activation of muscarinic, beta-adrenergic,
H2-histaminergic, and serotoninergic receptors on regular-spiking
layer II/III, V, and/or VI corticogeniculate pyramidal cells results
in a decrease in spike frequency adaptation and increased responsiveness
to depolarizing inputs through a reduction in a slow Ca(2+)-activated
K+ current IAHP, and/or a voltage-dependent K+ current, IM. Through
these, and other, mechanisms the spatial and temporal pattern of
activity generated in cortical circuits is regulated by both intracortical
and extracortical neurotransmitter systems.},
address = {Section of Neurobiology, Yale University School of Medicine, New
Haven, Connecticut 06510.},
au = {McCormick, DA and Wang, Z and Huguenard, J},
da = {19940125},
date-added = {2008-03-14 14:48:21 +0100},
date-modified = {2008-03-14 14:49:10 +0100},
dcom = {19940125},
edat = {1993/09/01},
issn = {1047-3211 (Print)},
jid = {9110718},
jt = {Cerebral cortex (New York, N.Y. : 1991)},
language = {eng},
lr = {20061115},
mh = {Animals; Cerebral Cortex/cytology/drug effects/*physiology; Guinea
Pigs; Humans; Models, Neurological; Neurons/drug effects/*physiology;
Neurotransmitter Agents/*physiology; Pyramidal Cells/drug effects/physiology;
Receptors, GABA-A/drug effects/physiology; Receptors, GABA-B/drug
effects/physiology; Receptors, Neurotransmitter/drug effects/physiology;
gamma-Aminobutyric Acid/physiology},
mhda = {1993/09/01 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {7903176},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, U.S. Gov't, P.H.S.},
pubm = {Print},
rn = {0 (Neurotransmitter Agents); 0 (Receptors, GABA-A); 0 (Receptors,
GABA-B); 0 (Receptors, Neurotransmitter); 56-12-2 (gamma-Aminobutyric
Acid)},
sb = {IM},
so = {Cereb Cortex. 1993 Sep-Oct;3(5):387-98. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{McCormick07,
author = {D.A. McCormick and Y. Shu1 and Yuguo Yu},
title = {Neurophysiology: Hodgkin and Huxley model ? still standing?},
journal = {Nature},
year = {2007},
volume = {445},
pages = {E1-E2}
}
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author = {W. S. McCulloch and W. Pitts},
title = {A logical calculus of ideas immanent in nervous activity},
journal = {Bulletin of mathematical Biophys.},
year = {1943},
volume = {5},
pages = {115--133}
}
@article{McLaughlin05,
author = {McLaughlin, T. and O'Leary, DD},
title = {{Molecular gradients and development of retinotopic maps}},
journal = {Annual Reviews of Neuroscience},
year = {2005},
volume = {28},
pages = {327--355},
keywords = {Vision,Vision-Physiology},
owner = {sprekeler},
publisher = {Annual Reviews},
timestamp = {2008.04.14}
}
@article{McNamara89,
author = {B. McNamara and K. Wiesenfeld},
title = {Theory of stochastic resonance},
journal = {Physical Review A},
year = {1989},
volume = {39},
pages = {4854--4869}
}
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M.W. Jung and J.J. Knierim and H. Kudrimoti and Y. Qin and W.E. Skaggs
and M. Suster and K.L. Weaver },
title = {Deciphering the hippocampal polyglot: the hippocampus as a path integration
system},
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year = {1996},
volume = {199},
pages = {173-185}
}
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author = {B.L. McNaughton and C.A. Barnes and J.L. Gerrard and K. Gothard and
M.W. Jung and J.J. Knierim and H. Kudrimoti and Y. Qin and W.E. Skaggs
and M. Suster and K.L. Weaver},
title = {Deciphering the hippocampal polyglot: the hippocampus as a path integration
system},
journal = {Journal of Experimental Biology},
year = {1996},
volume = {199},
pages = {173--85},
number = {1},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{McNaughton83,
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year = {1983},
volume = {52},
pages = {41--49}
}
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author = {B.L. McNaughton and L.L. Chen and E.J. Markus},
title = {{Dead reckoning, landmark learning, and the sense of direction: A
neurophysiological and computational hypothesis}},
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year = {1991},
volume = {3},
pages = {190}
}
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author = {B. L. McNaughton and F. P. Battaglia and O. Jensen and E. I. Moser
and M. B. Moser},
title = {Path integration and the neural basis of the 'cognitive map'},
journal = {Nature Reviews Neuroscience},
year = {2006},
volume = {7},
pages = {663--678},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Mehaffey05,
author = {W. Hamish Mehaffey and Brent Doiron and Leonard Maler and Ray W.
Turner},
title = {Deterministic Multiplicative Gain Control with Active Dendrites},
journal = {Journal of Neuroscience},
year = {2005},
volume = {25},
pages = {9968--9977},
number = {43},
keywords = {Neuronal-Processing},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Mehta00b,
author = {Mehta, M.R. and Quirk, M.C. and Wilson, M.A.},
title = {Experience-Dependent Asymmetric Shape of Hippocampal Receptive Fields},
journal = {Neuron},
year = {2000},
volume = {25},
pages = {707--715},
number = {3},
keywords = {hippocampus, plasticity},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@article{Mehta00a,
author = {M.R. Mehta and M.A. Wilson},
title = {From hippocampus to V1: effect of LTP on spatio-temporal dynamics
of receptive fields},
journal = {Neurocomputing},
year = {2000},
volume = {32},
pages = {905-911}
}
@article{Mehta01,
author = {Mayank R. Mehta},
title = {Neuronal dynamics of predictive coding},
journal = {Neuroscientist},
year = {2001},
volume = {7},
pages = {490-495}
}
@article{Mehta97,
author = {Mehta, Mayank R. and Barnes, Carol A. and McNaughton, Bruce L.},
title = {Experience-dependent, asymmetric expansion of hippocampal place fields},
journal = {PNAS},
year = {1997},
volume = {94},
pages = {8918-8921},
number = {16},
doi = {10.1073/pnas.94.16.8918},
eprint = {http://www.pnas.org/cgi/reprint/94/16/8918.pdf},
keyword = {hippocampus, plasticity},
keywords = {hippocampus, plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.pnas.org/cgi/content/abstract/94/16/8918}
}
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author = {M. R. Mehta and A. K. Lee and M. A. Wilson},
title = {Role of experience of oscillations in transforming a rate code into
a temporal code},
journal = {Nature},
year = {2002},
volume = {417},
pages = {741-746}
}
@article{Mehta00,
author = {M. R. Mehta and M.C. Quirk and M.W. Wilson},
title = {Experience-dependent asymmetric shape of hippocampal receptive fields},
journal = {Neuron},
year = {2000},
volume = {25},
pages = {707-715}
}
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author = {Bartlett W. Mel},
title = {What the Synapse Tells the Neuron},
journal = {Science},
year = {2002},
volume = {295},
pages = {1845--1846},
keywords = {Neuronal-Processing},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {Bartlett W. Mel},
title = {Information Processing in Dendritic Trees},
journal = {Neural Comput.},
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}
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volume = {27},
pages = {11-14}
}
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author = {Melamed, O. and Gerstner, W. and Maass, W. and Tsodyks, M. and Markram,
H.},
title = {{Coding and learning of behavioral sequences}},
journal = {Trends Neurosci},
year = {2004},
volume = {27},
pages = {11--14},
number = {1},
keywords = {hippocampus, plasticity},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
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but this effect is not dependent on attention}},
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year = {2000},
volume = {17},
pages = {949--958},
number = {6},
month = nov,
keywords = {Vision},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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year = {2002},
volume = {25},
pages = {558-563}
}
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author = {C. Meyer and C. van Vreeswijk},
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journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {369-404}
}
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author = {M. Mezard and G. Parisi},
title = {Mean-field theory of randomly frustrated systems with finite connectivity},
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year = {1987},
volume = {3},
pages = {1067-1074}
}
@article{Middlebrooks94,
author = {John C. Middlebrooks and Ann E. Clock and Li Xu and David M. Green},
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abstract = {A key goal in neuroscience is to explain how the operations of a neuron
emerge from sets of active channels with specific dendritic distributions.
If general principles can be identified for these distributions,
dendritic channels should reflect the computational role of a given
cell type within its functional neural circuit. Here, we discuss
insights from experimental and computational data on the distribution
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it constitutes a step towards a 'functional proteomics' of nerve
cells, which will be essential for defining neuronal phenotypes.},
address = {Department of Neurobiology, Yale University School of Medicine, New
Haven, Connecticut 06520-8001, USA.},
au = {Migliore, M and Shepherd, GM},
bdsk-url-1 = {http://dx.doi.org/10.1038/nrn810},
da = {20020503},
date-added = {2008-03-28 23:29:20 +0100},
date-modified = {2008-03-28 23:30:26 +0100},
dcom = {20020617},
doi = {10.1038/nrn810},
edat = {2002/05/04 10:00},
issn = {1471-003X (Print)},
jid = {100962781},
jt = {Nature reviews. Neuroscience},
language = {eng},
lr = {20061115},
mh = {Animals; Dendrites/chemistry/*physiology; Humans; Ion Channels/analysis/physiology;
Neural Conduction/*physiology; Neurons/chemistry/physiology; Synapses/chemistry/physiology},
mhda = {2002/06/18 10:01},
own = {NLM},
owner = {sprekeler},
pii = {nrn810},
pl = {England},
pmid = {11988775},
pst = {ppublish},
pt = {Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research
Support, U.S. Gov't, P.H.S.; Review},
pubm = {Print},
rf = {71},
rn = {0 (Ion Channels)},
sb = {IM},
so = {Nat Rev Neurosci. 2002 May;3(5):362-70. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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address = {Department of Physiology, University of Berne, Buhlplatz 5, 3012
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da = {20070129},
date-added = {2008-03-28 23:32:15 +0100},
date-modified = {2008-03-28 23:32:20 +0100},
dcom = {20070403},
dep = {20070107},
doi = {10.1038/nn1826},
edat = {2007/01/09 09:00},
issn = {1097-6256 (Print)},
jid = {9809671},
jt = {Nature neuroscience},
language = {eng},
mh = {Action Potentials/physiology; Animals; Cell Membrane/physiology; Cerebral
Cortex/*cytology/*physiology; Dendrites/*physiology/*ultrastructure;
Dendritic Spines/physiology/ultrastructure; Excitatory Postsynaptic
Potentials/physiology; Membrane Potentials/physiology; Organ Culture
Techniques; Patch-Clamp Techniques; Pyramidal Cells/*cytology/*physiology;
Rats; Rats, Wistar; Receptors, N-Methyl-D-Aspartate/metabolism; Sodium/metabolism;
Synapses/physiology/ultrastructure; Synaptic Transmission/physiology},
mhda = {2007/04/04 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2006/08/21 {$[$}received{$]$}; 2006/12/04 {$[$}accepted{$]$}; 2007/01/07
{$[$}aheadofprint{$]$}},
pii = {nn1826},
pl = {United States},
pmid = {17206140},
pst = {ppublish},
pt = {Journal Article; Research Support, N.I.H., Extramural; Research Support,
Non-U.S. Gov't},
pubm = {Print-Electronic},
rn = {0 (Receptors, N-Methyl-D-Aspartate); 7440-23-5 (Sodium)},
sb = {IM},
so = {Nat Neurosci. 2007 Feb;10(2):206-14. Epub 2007 Jan 7. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Nevian06,
author = {Nevian, Thomas and Sakmann, Bert},
title = {Spine Ca2+ Signaling in Spike-Timing-Dependent Plasticity},
journal = {J. Neurosci.},
year = {2006},
volume = {26},
pages = {11001-11013},
number = {43},
abstract = {Calcium is a second messenger, which can trigger the modification
of synaptic efficacy. We investigated the question of whether a differential
rise in postsynaptic Ca2+ ([Ca2+]i) alone is sufficient to account
for the induction of long-term potentiation (LTP) and long-term depression
(LTD) of EPSPs in the basal dendrites of layer 2/3 pyramidal neurons
of the somatosensory cortex. Volume-averaged [Ca2+]i transients were
measured in spines of the basal dendritic arbor for spike-timing-dependent
plasticity induction protocols. The rise in [Ca2+]i was uncorrelated
to the direction of the change in synaptic efficacy, because several
pairing protocols evoked similar spine [Ca2+]i transients but resulted
in either LTP or LTD. The sequence dependence of near-coincident
presynaptic and postsynaptic activity on the direction of changes
in synaptic strength suggested that LTP and LTD were induced by two
processes, which were controlled separately by postsynaptic [Ca2+]i
levels. Activation of voltage-dependent Ca2+ channels before metabotropic
glutamate receptors (mGluRs) resulted in the phospholipase C-dependent
(PLC-dependent) synthesis of endocannabinoids, which acted as a retrograde
messenger to induce LTD. LTP required a large [Ca2+]i transient evoked
by NMDA receptor activation. Blocking mGluRs abolished the induction
of LTD and uncovered the Ca2+-dependent induction of LTP. We conclude
that the volume-averaged peak elevation of [Ca2+]i in spines of layer
2/3 pyramids determines the magnitude of long-term changes in synaptic
efficacy. The direction of the change is controlled, however, via
a mGluR-coupled signaling cascade. mGluRs act in conjunction with
PLC as sequence-sensitive coincidence detectors when postsynaptic
precede presynaptic action potentials to induce LTD. Thus presumably
two different Ca2+ sensors in spines control the induction of spike-timing-dependent
synaptic plasticity.},
doi = {10.1523/JNEUROSCI.1749-06.2006},
eprint = {http://www.jneurosci.org/cgi/reprint/26/43/11001.pdf},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.jneurosci.org/cgi/content/abstract/26/43/11001}
}
@article{Newpher08,
author = {Thomas M Newpher and Michael D Ehlers},
title = {Glutamate receptor dynamics in dendritic microdomains.},
journal = {Neuron},
year = {2008},
volume = {58},
pages = {472--497},
number = {4},
month = {May},
abstract = {Among diverse factors regulating excitatory synaptic transmission,
the abundance of postsynaptic glutamate receptors figures prominently
in molecular memory and learning-related synaptic plasticity. To
allow for both long-term maintenance of synaptic transmission and
acute changes in synaptic strength, the relative rates of glutamate
receptor insertion and removal must be tightly regulated. Interactions
with scaffolding proteins control the targeting and signaling properties
of glutamate receptors within the postsynaptic membrane. In addition,
extrasynaptic receptor populations control the equilibrium of receptor
exchange at synapses and activate distinct signaling pathways involved
in plasticity. Here, we review recent findings that have shaped our
current understanding of receptor mobility between synaptic and extrasynaptic
compartments at glutamatergic synapses, focusing on AMPA and NMDA
receptors. We also examine the cooperative relationship between intracellular
trafficking and surface diffusion of glutamate receptors that underlies
the expression of learning-related synaptic plasticity.},
doi = {10.1016/j.neuron.2008.04.030},
owner = {sprekeler},
pii = {S0896-6273(08)00408-X},
pmid = {18498731},
timestamp = {2008.06.03},
url = {http://dx.doi.org/10.1016/j.neuron.2008.04.030}
}
@article{Ngezahajo00,
author = {A. Ngezahayo and M. Schachner and A. Artola},
title = {Synaptic activation modulates the induction of bidirectional synaptic
changes in Adult mouse hippocamus},
journal = {J. Neuroscience},
year = {2000},
volume = {20},
pages = {2451-2458}
}
@book{Nicholls92,
title = {From Neuron to Brain},
publisher = {Sinauer Associates, Inc.},
year = {1992},
author = {John G. Nicholls and A. Robert Martin and Bruce G. Wallace},
address = {Sunderland, Massachusetts U.S.A.},
edition = {3rd}
}
@inproceedings{Niebur91,
author = {E. Niebur and D. M. Kammen and C. Koch and D. Rudermann and H. G.
Schuster},
title = {Phase-coupling in two dimensional networks of interacting oscillators},
booktitle = {Advances in Neural Information Processing Systems 3},
year = {1991},
editor = {R. P. Lippmann and J. E. Moody and D. S. Touretzky},
pages = {123-127},
address = {San Mateo CA},
publisher = {Morgan Kaufmann}
}
@article{Nirenberg03,
author = {S. Nirenberg and P.E. Latham},
title = {Decoding neuronal spike trains: How important are correlations? },
journal = {Proc. Natl. Acad. Sci. USA},
year = {2003},
volume = {100},
pages = {7348-7353}
}
@article{Nishiyama00,
author = {M. Nishiyama and K. Hong and K. Mikoshiba nd M.M. Poo and K. Kato},
title = {Calcium stores regulate the polarity and input specificity of synaptic
modification},
journal = {Nature},
year = {2000},
volume = {408},
pages = {584-588}
}
@article{Nishiyama00a,
author = {Makoto Nishiyama and Kyonsoo Hong and Katsuhiko Miskoshiba and Mu{-}ming
Poo and Kunio Kato},
title = {Calcium stores regulate the polarity and input specificity of synaptic
modification},
journal = {Nature},
year = {2000},
volume = {208},
pages = {584--588},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Nowak84,
author = {L. Nowak and P. Bregestovski and P. Asher and A. Herbet and A. Prochiantz},
title = {Magnesium gates glutamate-activiated channels in mouse central neurons},
journal = {Nature},
year = {1984},
volume = {307},
pages = {462-465}
}
@article{Nowotny07,
author = {Nowotny, Thomas and Szucs, Attila and Levi, Rafael and Selverston,
Allen I},
title = {Models wagging the dog: are circuits constructed with disparate parameters?},
journal = {Neural Comput},
year = {2007},
volume = {19},
pages = {1985--2003},
number = {8},
abstract = {In a recent article, Prinz, Bucher, and Marder (2004) addressed the
fundamental question of whether neural systems are built with a fixed
blueprint of tightly controlled parameters or in a way in which properties
can vary largely from one individual to another, using a database
modeling approach. Here, we examine the main conclusion that neural
circuits indeed are built with largely varying parameters in the
light of our own experimental and modeling observations. We critically
discuss the experimental and theoretical evidence, including the
general adequacy of database approaches for questions of this kind,
and come to the conclusion that the last word for this fundamental
question has not yet been spoken.},
address = {Institute for Nonlinear Science, University of California, San Diego,
La Jolla, CA 92093-0402, USA. T.Nowotny@sussex.ac.uk},
au = {Nowotny, T and Szucs, A and Levi, R and Selverston, AI},
bdsk-url-1 = {http://dx.doi.org/10.1162/neco.2007.19.8.1985},
da = {20070618},
date-added = {2008-03-28 11:57:40 +0100},
date-modified = {2008-03-28 11:57:43 +0100},
dcom = {20070907},
doi = {10.1162/neco.2007.19.8.1985},
edat = {2007/06/19 09:00},
gr = {R01 NS-050945/NS/United States NINDS},
issn = {0899-7667 (Print)},
jid = {9426182},
jt = {Neural computation},
language = {eng},
lr = {20071203},
mh = {4-Aminopyridine/pharmacology; Action Potentials/drug effects/physiology;
Animals; Databases, Factual; Feedback; Ganglia, Invertebrate/cytology;
Ion Channels/drug effects/physiology; *Models, Neurological; Nerve
Net/*physiology; *Neural Networks (Computer); Neurons/drug effects/*physiology;
Palinuridae; Potassium Channel Blockers/pharmacology; Time Factors},
mhda = {2007/09/08 09:00},
own = {NLM},
owner = {sprekeler},
pl = {United States},
pmid = {17571936},
pst = {ppublish},
pt = {Journal Article; Research Support, N.I.H., Extramural; Research Support,
Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.},
pubm = {Print},
rn = {0 (Ion Channels); 0 (Potassium Channel Blockers); 504-24-5 (4-Aminopyridine)},
sb = {IM},
so = {Neural Comput. 2007 Aug;19(8):1985-2003. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Nykamp00,
author = {D. Nykamp and D. Tranchina},
title = {A population density approach that facilitates large-scale modeling
of neural networks: Analysis and application to orientation tuning},
journal = {J. Computational Neuroscience},
year = {2000},
volume = {8},
pages = {19-50}
}
@article{O'Connor05a,
author = {O'Connor, D.H. and Wittenberg, G.M. and Wang, S.S.H.},
title = {{Dissection of Bidirectional Synaptic Plasticity Into Saturable Unidirectional
Processes}},
journal = {Journal of Neurophysiology},
year = {2005},
volume = {94},
pages = {1565--1573},
number = {2},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Am Physiological Soc},
timestamp = {2008.04.14}
}
@article{O'Connor05,
author = {Daniel H. O'Connor and Gayle M. Wittenberg and Samuel S.-H. Wang},
title = {Graded bidirectional synaptic plasticity is composed of switch-like
unitary events},
journal = {PNAS},
year = {2005},
volume = {102},
pages = {9679--9684},
number = {26},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{OConnor05,
author = {D.H. {O'Connor} and G.M. Wittenberg and S.S.-H. Wang.},
title = {Graded bidirectional synaptic plasticity is composed of switch-like
unitary events.},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2005},
volume = {102},
pages = {9679-9684}
}
@article{ODoherty03,
author = {J O'Doherty and P Dayan and K Friston and H Critchley and R Dolan},
title = {Temporal difference learning model accounts for responses in human
ventral striatum and orbitofrontal cortex during Pavlovian appetitive
learning},
journal = {Neuron},
year = {2003},
volume = {38},
pages = {329--337}
}
@article{Odoherty04,
author = {O'Doherty, J. and P. Dayan and J. Schultz and R Deischmann and K.
Friston and R.J. Dolan},
title = { Dissociable roles of ventral and dorsal striatum in instrumental
conditioning. },
journal = {Science},
year = {2004},
volume = {304},
pages = {452-454}
}
@incollection{O'Keefe07,
author = {J. O'Keefe},
title = {Hippocampal Neurophysiology in the behaving animal},
booktitle = {The hippocampus book},
publisher = {Oxford university press},
year = {2007},
pages = {475--548},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{O'Keefe05,
author = {J. O'Keefe and N. Burgess},
title = {Dual phase and rate coding in hippocampal place cells: theoretical
significance and relationship to entorhinal grid cells},
journal = {Hippocampus},
year = {2005},
volume = {15},
pages = {853--866},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Okeefe96,
author = {J. O'Keefe and N. Burgess},
title = {{Geometric determinants of the place fields of hippocampal= neurons}},
journal = {Nature},
year = {1996},
volume = {381},
pages = {425--428}
}
@article{O'Keefe71,
author = {J. O'Keefe and J. Dostrovsky},
title = {The hippocampus as a spatial map: preliminary evidence from unit
activity in the freely moving rat},
journal = {Brain Research},
year = {1971},
volume = {34},
pages = {171--175},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{O'Keefe78,
title = {The Hippocampus as a cognitive map},
publisher = {Clarendon Press},
year = {1978},
author = {J. O'Keefe and L. Nadel},
address = {Oxford},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Okeefe71,
author = {J. {O'K}eefe and L. Nada;},
title = {The hippocampus as a spatial map: preliminary evidence from unit
activity in the freely-moving rat.},
journal = {Brain Res.},
year = {1971},
volume = {34},
pages = {171-175}
}
@article{O'Reilly94a,
author = {O'Reilly, Randall C. and Johnson, Mark H.},
title = {Object Recognition and Sensitive Periods: {A} Computational Analysis
of Visual Imprinting.},
journal = {Neural Computation},
year = {1994},
volume = {6},
pages = {357--389},
number = {3},
keywords = {slowness},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://mitpress.mit.edu/journal-issue-abstracts.tcl?issn=08997667&volume=6&issue=3}
}
@article{O'Reilly94,
author = {R. C. O'Reilly and J. L. McClelland},
title = {Hippocampal conjunctive encoding, storage, and recall: avoiding a
trade-off.},
journal = {Hippocampus},
year = {1994},
volume = {4},
pages = {661--682},
number = {6},
month = dec,
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=7704110&dopt=Abstract}
}
@article{Obermayer92,
author = {K. Obermayer and G. G. Blasdel and K. Schulten},
title = {Statistical-mechanics analysis of self-organization and pattern formation
during the development of visual maps},
journal = {Phys. Rev. E},
year = {1992},
volume = {45},
pages = {7568-7589}
}
@article{Oertel83,
author = {Donata Oertel},
title = {Synaptic responses and electrical properties of cells in brain slices
of the mouse anteroventral cochlear nucleus},
journal = {The Journal of Neuroscience},
year = {1983},
volume = {3},
pages = {2043--2053},
number = {10}
}
@article{Oja82,
author = {E. Oja},
title = {A simplified neuron model as a principal component analyzer},
journal = {J. Mathematical Biology},
year = {1982},
volume = {15},
pages = {267-273}
}
@article{Oja82a,
author = {Erkki Oja},
title = {A simplified Neuron as a Principal Component Analyzer},
journal = {Journal of Mathematical Biology},
year = {1982},
volume = {15},
pages = {267-273},
keywords = {Various-Artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Oja95,
author = {Oja, E. and Karhunen, J.},
title = {{Signal separation by nonlinear Hebbian learning}},
journal = {Computational Intelligence: A Dynamic System Perspective},
year = {1995},
pages = {83--97},
keywords = {plasticity, ICA},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Okatan05,
author = {M. Okatan and M.A. Wilson and E.N. Brown},
title = {Analyzing functional connectivity using a network likelihood model
of ensemble neural spiking activity},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {1927-1961}
}
@article{Olami92,
author = {Z. Olami and H. J. S. Feder and K. Christensen},
title = {Self-organized criticality in a continuous, nonconservative cellular
automaton modelling earthquakes},
journal = {Phys. Rev. Lett.},
year = {1992},
volume = {68},
pages = {1244-1247}
}
@incollection{Olshausen03,
author = {B. A. Olshausen},
title = {Principles of Image Representation in Visual Cortex},
booktitle = {{The Visual Neurosciences}},
publisher = {MIT Press},
year = {2003},
editor = {L.M. Chalupa and J.S. Werner},
keywords = {Vision-Models, vision, sparse coding},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Olshausen04,
author = {Olshausen, B. A. and Field, D.J.},
title = {Sparse coding of sensory inputs},
journal = {Current Opinion in Neurobiology},
year = {2004},
volume = {14},
pages = {481--487},
number = {4},
keywords = {vision, vision-models,optimal-coding},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Olshausen05,
author = {B. A. Olshausen and D. J. Field},
title = {{How Close Are We to Understanding V1?}},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {1665--1699},
keywords = {Vision-Models, vision, sparse coding},
numbers = {8},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Olshausen97,
author = {B. A. Olshausen and D. J. Field},
title = {Sparse Coding with an Overcomplete Basis Set: A Strategy Employed
by {V1}?},
journal = {Vision Research},
year = {1997},
volume = {37},
pages = {3311--3325},
keywords = {Vision-Models, vision, sparse coding},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Olshausen96,
author = {B. A. Olshausen and D. J. Field},
title = {Natural Image Statistics and Efficient Coding},
journal = {Network: Computation in Neural Systems},
year = {1996},
volume = {7},
pages = {333--339},
keywords = {Vision-Models, vision, sparse coding},
numbers = {2},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Olshausen96a,
author = {B. A. Olshausen and D. J. Field},
title = {Emergence of simple-cell receptive field properties by learning a
sparse code for natural images},
journal = {Nature},
year = {1996},
volume = {381},
pages = {607--609},
keywords = {Vision-Models, vision, sparse coding},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Omurtag00,
author = {A. Omurtag and B.W. Knight and L. Sirovich},
title = {On the simulation of a large population of neurons},
journal = {J. Computational Neuroscience},
year = {2000},
volume = {8},
pages = {51-63}
}
@article{Ooyen94,
author = {A. van Ooyen},
title = {Activity-dependent neural network development},
journal = {Network},
year = {1994},
volume = {5},
pages = {401-423}
}
@article{Optican87,
author = {L. M. Optican and B. J. Richmond},
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analysis.},
journal = {J. Neurophysiol.},
year = {1987},
volume = {57},
pages = {162--178}
}
@article{Oram99,
author = {M.W. Oram and M.C. Wiener and R. Lestienne and B.J. Richmond},
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neuronal responses},
journal = {J. Neurophysiology},
year = {1999},
volume = {81},
pages = {3021-3033}
}
@book{Orban92,
title = {Artificial and Biological Vision Systems},
publisher = {Springer Verlag},
year = {1992},
author = {G. A. Orban and H. N. Nagel},
address = {Berlin}
}
@article{Otto92,
author = {T Otto and H Eichenbaum},
title = {{{N}euronal activity in the hippocampus during delayed non-match
to sample performance in rats: evidence for hippocampal processing
in recognition memory}},
journal = {Hippocampus},
year = {1992},
volume = {2},
pages = {323--334},
number = {3},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Overholt92,
author = {Edwin~M. Overholt and Edwin~W. Rubel and Richard~L. Hyson},
title = {A Circuit for Coding Interaural Time Differences in the Chick Brainstem},
journal = {The Journal of Neuroscience},
year = {1992},
volume = {12},
pages = {1698--1708},
number = {5}
}
@article{Ashwin94,
author = {P.Ashwin and J.Buescu and I.Stewart},
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journal = {Phys. Lett. A},
year = {1994},
volume = {193},
pages = {126-139},
annote = {hasler-citation}
}
@article{Pack01,
author = {Pack, C C and Berezovskii, V K and Born, R T},
title = {{{D}ynamic properties of neurons in cortical area {M}{T} in alert
and anaesthetized macaque monkeys}},
journal = {Nature},
year = {2001},
volume = {414},
pages = {905--908},
number = {6866},
month = {Dec},
keywords = {vision, vision-physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Pagnoni02,
author = {G Pagnoni and CF Zink and PR Montague and GS Berns},
title = {Activity in human ventral striatum locked to errors in reward prediction},
journal = {Nature Neuroscience},
year = {2002},
volume = {5},
pages = {97--98},
number = {2}
}
@article{Pakdaman02,
author = {K. Pakdaman},
title = {The reliability of the stochastic active rotator},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {781-792}
}
@article{Pakdaman01a,
author = {K. Pakdaman and S. Tanabe},
title = {External noise synchronizes forced oscillators},
journal = {Phys. Rev. E},
year = {2001},
volume = {64},
pages = {30901}
}
@article{Pakdaman01b,
author = {K. Pakdaman and S. Tanabe},
title = {Random Dynamics of the Hodgkin-Huxley model},
journal = {Phys. Rev. E},
year = {2001},
volume = {64},
pages = {50902}
}
@article{Pakdaman01c,
author = {K. Pakdaman and S. Tanabe and T. Shimokawa},
title = {Coherence resonance and discharge reliability in neurons and neuronal
models},
journal = {Neural Networks},
year = {2001},
volume = {14},
pages = {895-905}
}
@book{Palm86,
title = {Brain Theory},
publisher = {Springer},
year = {1986},
author = {G. Palm},
address = {Berlin}
}
@article{Palm80,
author = {G. Palm},
title = {On associative memory},
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year = {1980},
volume = {36},
pages = {19--31}
}
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author = {G. Palm and A. Aertsen and G. L. Gerstein},
title = {On the significance of correlations among neuronal spike trains.},
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year = {1988},
volume = {59},
pages = {1--11}
}
@article{Paninski06,
author = {L. Paninski},
title = {The spike-triggered average of the integrate-and-fire cell driven
by Gaussian white noise},
journal = {Neural Computation},
year = {2006},
volume = {18},
pages = {2592-2616}
}
@article{Paninski06b,
author = {L. Paninski},
title = {The most likely voltage path and large deviations approximations
for integrate-and-fire neurons},
journal = {J. Comput. Neuroscience},
year = {2006},
volume = {21},
pages = {71-87}
}
@article{Paninski03,
author = {L. Paninski},
title = {Convergence properties of three spike-triggered analysis techniques},
journal = {Network},
year = {2003},
volume = {14},
pages = {437-464}
}
@article{Paninski08,
author = {Liam Paninski and Adrian Haith and Gabor Szirtes},
title = {Integral equation methods for computing likelihoods and their derivatives
in the stochastic integrate-and-fire model.},
journal = {J Comput Neurosci},
year = {2008},
volume = {24},
pages = {69--79},
number = {1},
month = {Feb},
abstract = {We recently introduced likelihood-based methods for fitting stochastic
integrate-and-fire models to spike train data. The key component
of this method involves the likelihood that the model will emit a
spike at a given time t. Computing this likelihood is equivalent
to computing a Markov first passage time density (the probability
that the model voltage crosses threshold for the first time at time
t). Here we detail an improved method for computing this likelihood,
based on solving a certain integral equation. This integral equation
method has several advantages over the techniques discussed in our
previous work: in particular, the new method has fewer free parameters
and is easily differentiable (for gradient computations). The new
method is also easily adaptable for the case in which the model conductance,
not just the input current, is time-varying. Finally, we describe
how to incorporate large deviations approximations to very small
likelihoods.},
doi = {10.1007/s10827-007-0042-x},
keywords = {neuronal-processing},
owner = {sprekeler},
pmid = {17492371},
timestamp = {2008.05.14},
url = {http://dx.doi.org/10.1007/s10827-007-0042-x}
}
@article{Paninski04,
author = {L. Paninski and J. Pillow and E. Simoncelli},
title = {Maximum likelihood estimate of a stochastic integrate-and-fire neural
encoding model},
journal = {Neural computation},
year = {2004},
volume = {16},
pages = {2533-2561}
}
@article{Paninski04b,
author = {L. Paninski and J. Pillow and E. Simoncelli},
title = {Comparing integrate-and-fire-like models estimated using intracellular
and extracellular data. },
journal = {Neurocomputing},
year = {2004},
volume = {xx},
pages = {xx}
}
@article{Paninski04a,
author = {Paninski, Liam and Pillow, Jonathan W and Simoncelli, Eero P},
title = {Maximum likelihood estimation of a stochastic integrate-and-fire
neural encoding model.},
journal = {Neural Comput},
year = {2004},
volume = {16},
pages = {2533--2561},
number = {12},
abstract = {We examine a cascade encoding model for neural response in which a
linear filtering stage is followed by a noisy, leaky, integrate-and-fire
spike generation mechanism. This model provides a biophysically more
realistic alternative to models based on Poisson (memoryless) spike
generation, and can effectively reproduce a variety of spiking behaviors
seen in vivo. We describe the maximum likelihood estimator for the
model parameters, given only extracellular spike train responses
(not intracellular voltage data). Specifically, we prove that the
log-likelihood function is concave and thus has an essentially unique
global maximum that can be found using gradient ascent techniques.
We develop an efficient algorithm for computing the maximum likelihood
solution, demonstrate the effectiveness of the resulting estimator
with numerical simulations, and discuss a method of testing the model's
validity using time-rescaling and density evolution techniques.},
address = {Howard Hughes Medical Institute, Center for Neural Science, New York
University, New York, NY 10003, USA. liam@cns.nyu.edu},
au = {Paninski, L and Pillow, JW and Simoncelli, EP},
bdsk-url-1 = {http://dx.doi.org/10.1162/0899766042321797},
da = {20041101},
date-added = {2008-03-28 12:03:45 +0100},
date-modified = {2008-03-28 12:03:52 +0100},
dcom = {20050119},
doi = {10.1162/0899766042321797},
edat = {2004/11/02 09:00},
issn = {0899-7667 (Print)},
jid = {9426182},
jt = {Neural computation},
language = {eng},
lr = {20061115},
mh = {Algorithms; Biophysics; Electrophysiology; Likelihood Functions; Membrane
Potentials; Models, Neurological; Models, Statistical; Neurons/*physiology;
Nonlinear Dynamics; Poisson Distribution},
mhda = {2005/01/20 09:00},
own = {NLM},
owner = {sprekeler},
pl = {United States},
pmid = {15516273},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, Non-P.H.S.},
pubm = {Print},
sb = {IM},
so = {Neural Comput. 2004 Dec;16(12):2533-61. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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year = {2001},
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timestamp = {2008.04.14}
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timestamp = {2008.04.14}
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timestamp = {2008.04.14},
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keywords = {Plasticity},
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timestamp = {2008.04.14}
}
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author = {N. Petkov and P. Kruizinga},
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}
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owner = {sprekeler},
timestamp = {2008.04.14}
}
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year = {2006},
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}
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title = {{Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity}},
journal = {Journal of Neuroscience},
year = {2006},
volume = {26},
pages = {9673},
number = {38},
keywords = {plasticity},
owner = {sprekeler},
publisher = {Soc Neuroscience},
timestamp = {2008.04.14}
}
@article{Pfister06,
author = {J.-P. Pfister and T. Toyoizumi and D. Barber and W. Gerstner},
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Firing in Supervised Learning},
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year = {2006},
volume = {18},
pages = {1309-1339}
}
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author = {Pfister, Jean-Pascal and Toyoizumi, Taro and Barber, David and Gerstner,
Wulfram},
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Firing in Supervised Learning}},
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timestamp = {2008.04.14}
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year = {2002},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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@inproceedings{Pillow04,
author = {J.W. Pillow and L. Paninski and E.P. Simoncelli},
title = {Maximum Likelihood estimation of a stochastic integrate-and-fire
model},
booktitle = {Advances in Neural Information Processing Systems},
year = {2004},
editor = {S. Thrun and L. Saul and B. Sch{\"o}lkopf},
volume = {16},
pages = {1311-1318}
}
@article{Pillow05,
author = {J.W. Pillow and L. Paninski and V.J. Uzzell and E.P. Simoncelli and
E.J.Chichilnisky},
title = {Prediction and decoding of retinal ganglion cell responses with a
probabilistic spiking model},
journal = {J. Neuroscience},
year = {2005},
volume = {25},
pages = {11003-11023}
}
@article{Pillow05a,
author = {Pillow, Jonathan W and Paninski, Liam and Uzzell, Valerie J and Simoncelli,
Eero P and Chichilnisky, E J},
title = {Prediction and decoding of retinal ganglion cell responses with a
probabilistic spiking model.},
journal = {J Neurosci},
year = {2005},
volume = {25},
pages = {11003--11013},
number = {47},
abstract = {Sensory encoding in spiking neurons depends on both the integration
of sensory inputs and the intrinsic dynamics and variability of spike
generation. We show that the stimulus selectivity, reliability, and
timing precision of primate retinal ganglion cell (RGC) light responses
can be reproduced accurately with a simple model consisting of a
leaky integrate-and-fire spike generator driven by a linearly filtered
stimulus, a postspike current, and a Gaussian noise current. We fit
model parameters for individual RGCs by maximizing the likelihood
of observed spike responses to a stochastic visual stimulus. Although
compact, the fitted model predicts the detailed time structure of
responses to novel stimuli, accurately capturing the interaction
between the spiking history and sensory stimulus selectivity. The
model also accounts for the variability in responses to repeated
stimuli, even when fit to data from a single (nonrepeating) stimulus
sequence. Finally, the model can be used to derive an explicit, maximum-likelihood
decoding rule for neural spike trains, thus providing a tool for
assessing the limitations that spiking variability imposes on sensory
performance.},
address = {Howard Hughes Medical Institute, Center for Neural Science, Courant
Institute of Mathematical Sciences, New York University, New York,
New York 10003, USA. pillow@cns.nyu.edu},
au = {Pillow, JW and Paninski, L and Uzzell, VJ and Simoncelli, EP and Chichilnisky,
EJ},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.3305-05.2005},
da = {20051124},
date-added = {2008-03-28 12:03:45 +0100},
date-modified = {2008-03-28 12:03:48 +0100},
dcom = {20060307},
doi = {10.1523/JNEUROSCI.3305-05.2005},
edat = {2005/11/25 09:00},
issn = {1529-2401 (Electronic)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20061115},
mh = {*Action Potentials; Animals; Macaca; *Models, Neurological; *Models,
Statistical; Photic Stimulation; Retinal Ganglion Cells/*physiology},
mhda = {2006/03/08 09:00},
own = {NLM},
owner = {sprekeler},
pii = {25/47/11003},
pl = {United States},
pmid = {16306413},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {J Neurosci. 2005 Nov 23;25(47):11003-13. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Pillow03,
author = {J. W. Pillow and E. P. Simoncelli},
title = {Biases in white-noise analysis due to non-Poissonian spike generation},
journal = {Neurocomputing},
year = {2003},
volume = {52-54},
pages = {109-115}
}
@article{Pinsky94,
author = {Pinsky, P F and Rinzel, J},
title = {Intrinsic and network rhythmogenesis in a reduced Traub model for
CA3 neurons.},
journal = {J Comput Neurosci},
year = {1994},
volume = {1},
pages = {39--60},
number = {1-2},
abstract = {We have developed a two-compartment, eight-variable model of a CA3
pyramidal cell as a reduction of a complex 19-compartment cable model
[Traub et al, 1991]. Our reduced model segregates the fast currents
for sodium spiking into a proximal, soma-like, compartment and the
slower calcium and calcium-mediated currents into a dendrite-like
compartment. In each model periodic bursting gives way to repetitive
soma spiking as somatic injected current increases. Steady dendritic
stimulation can produce periodic bursting of significantly higher
frequency (8-20 Hz) than can steady somatic input (< 8 Hz). Bursting
in our model occurs only for an intermediate range of electronic
coupling conductance. It depends on the segregation of channel types
and on the coupling current that flows back-and-forth between compartments.
When the soma and dendrite are tightly coupled electrically, our
model reduces to a single compartment and does not burst. Network
simulations with our model using excitatory AMPA and NMDA synapses
(without inhibition) give results similar to those obtained with
the complex cable model [Traub et al, 1991; Traub et al, 1992]. Brief
stimulation of a single cell in a resting network produces multiple
synchronized population bursts, with fast AMPA synapses providing
the dominant synchronizing mechanism. The number of bursts increases
with the level of maximal NMDA conductance. For high enough maximal
NMDA conductance synchronized bursting repeats indefinitely. We find
that two factors can cause the cells to desynchronize when AMPA synapses
are blocked: heterogeneity of properties amongst cells and intrinsically
chaotic burst dynamics. But even when cells are identical, they may
synchronize only approximately rather than exactly. Since our model
has a limited number of parameters and variables, we have studied
its cellular and network dynamics computationally with relative ease
and over wide parameter ranges. Thereby, we identify some qualitative
features that parallel or are distinguished from those of other neuronal
systems; e.g., we discuss how bursting here differs from that in
some classical models.},
address = {Mathematical Research Branch, NIDDK, National Institutes of Health,
Bethesda, MD 20892, USA.},
au = {Pinsky, PF and Rinzel, J},
da = {19961203},
date-added = {2008-03-28 23:25:01 +0100},
date-modified = {2008-03-28 23:25:18 +0100},
dcom = {19961203},
edat = {1994/06/01},
ein = {J Comput Neurosci 1995 Sep;2(3):275},
issn = {0929-5313 (Print)},
jid = {9439510},
jt = {Journal of computational neuroscience},
language = {eng},
lr = {20031114},
mh = {Animals; Dendrites/*physiology; Hippocampus/*physiology; Membrane
Potentials/physiology; *Neural Networks (Computer); Pyramidal Cells/*physiology;
Time Factors},
mhda = {1994/06/01 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {8792224},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
sb = {IM},
so = {J Comput Neurosci. 1994 Jun;1(1-2):39-60. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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journal = {PNAS},
year = {2001},
volume = {98},
pages = {12255-12260},
number = {21},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@phdthesis{Plesser-diss,
author = {H.E. Plesser},
title = {Aspects of Signal Processing in Noisy Neurons},
school = {Georg-August-Universit{\"a}t},
year = {1999},
address = {G{\"o}ttingen}
}
@techreport{Plesser-report,
author = {H. E. Plesser},
title = {The {M}od{U}hl Software collection},
institution = {MPI f{\"u}r Str{\"o}mungsforschung},
year = {2000},
address = {G{\"o}ttingen},
note = {http://www.chaos.gwgd.de/~plesser/ModUhl.htm}
}
@unpublished{Plesser98b,
author = {H. E. Plesser and others},
note = {In preparation},
month = {March},
year = {1998}
}
@unpublished{Plesser98a,
author = {H. E. Plesser and T. Geisel},
title = {Bandpass Properties of Integrate-Fire Neurons},
note = {Submitted to Computation in Neurosciences 1998},
month = {January},
year = {1998}
}
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author = {H. E. Plesser and W. Gerstner},
title = {Noise in integrate-and-fire models: from stochastic input to escape
rates.},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {367-384}
}
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author = {H. E. Plesser and W. Gerstner},
title = {Escape rate models for noisy integrate-and-fire neurons},
journal = {Neurocomputing},
year = {2000},
volume = {32-33},
pages = {219-224}
}
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author = {H. E. Plesser and W. Gerstner},
title = {Noise in integrate-and-fire models: from stochastic input to escape
rates.},
journal = {Neural Computation},
year = {2000},
volume = {12},
pages = {367-384}
}
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pages = {228--234}
}
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keywords = {Perceptual-Learning},
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}
@article{Poggio04,
author = {T. Poggio and R. Rifkin and S. Mukherjee and P. Niyogi},
title = {General conditions for predictivity in learning theory},
journal = {Nature},
year = {2004},
volume = {428},
pages = {419-422}
}
@article{Poirazi03,
author = {P. Poirazi and T. Brannon and B.W. Mel},
title = {Pyramidal neuron as two-layer neural network },
journal = {Neuron},
year = {2003},
volume = {37},
pages = {989-999}
}
@article{Poirazi03a,
author = {Poirazi, Panayiota and Brannon, Terrence and Mel, Bartlett W},
title = {Pyramidal neuron as two-layer neural network.},
journal = {Neuron},
year = {2003},
volume = {37},
pages = {989--999},
number = {6},
abstract = {The pyramidal neuron is the principal cell type in the mammalian forebrain,
but its function remains poorly understood. Using a detailed compartmental
model of a hippocampal CA1 pyramidal cell, we recorded responses
to complex stimuli consisting of dozens of high-frequency activated
synapses distributed throughout the apical dendrites. We found the
cell's firing rate could be predicted by a simple formula that maps
the physical components of the cell onto those of an abstract two-layer
"neural network." In the first layer, synaptic inputs drive independent
sigmoidal subunits corresponding to the cell's several dozen long,
thin terminal dendrites. The subunit outputs are then summed within
the main trunk and cell body prior to final thresholding. We conclude
that insofar as the neural code is mediated by average firing rate,
a two-layer neural network may provide a useful abstraction for the
computing function of the individual pyramidal neuron.},
address = {Institute of Molecular Biology and Biotechnology, Foundation for
Research and Technology, Hellas (FORTH), Vassilica Vouton, PO Box
1527, GR 711 10 Heraklion, Crete, Greece. poirazi@imbb.forth.gr},
au = {Poirazi, P and Brannon, T and Mel, BW},
da = {20030402},
date-added = {2008-03-29 12:59:28 +0100},
date-modified = {2008-03-29 12:59:37 +0100},
dcom = {20030501},
edat = {2003/04/03 05:00},
issn = {0896-6273 (Print)},
jid = {8809320},
jt = {Neuron},
language = {eng},
lr = {20061115},
mh = {Biophysics; Calcium/metabolism; Dendrites/physiology; Electric Conductivity;
Electrophysiology; Mathematics; Models, Biological; Nerve Net/*physiology/ultrastructure;
Potassium Channels/physiology; Pyramidal Cells/*physiology/ultrastructure;
Sodium Channels/physiology; Synapses/physiology},
mhda = {2003/05/02 05:00},
own = {NLM},
owner = {sprekeler},
pii = {S0896627303001491},
pl = {United States},
pmid = {12670427},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, Non-P.H.S.},
pubm = {Print},
rn = {0 (Potassium Channels); 0 (Sodium Channels); 7440-70-2 (Calcium)},
sb = {IM},
so = {Neuron. 2003 Mar 27;37(6):989-99. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Poirazi01,
author = {Poirazi, P and Mel, B W},
title = {Impact of active dendrites and structural plasticity on the memory
capacity of neural tissue.},
journal = {Neuron},
year = {2001},
volume = {29},
pages = {779--796},
number = {3},
abstract = {We consider the combined effects of active dendrites and structural
plasticity on the storage capacity of neural tissue. We compare capacity
for two different modes of dendritic integration: (1) linear, where
synaptic inputs are summed across the entire dendritic arbor, and
(2) nonlinear, where each dendritic compartment functions as a separately
thresholded neuron-like summing unit. We calculate much larger storage
capacities for cells with nonlinear subunits and show that this capacity
is accessible to a structural learning rule that combines random
synapse formation with activity-dependent stabilization/elimination.
In a departure from the common view that memories are encoded in
the overall connection strengths between neurons, our results suggest
that long-term information storage in neural tissue could reside
primarily in the selective addressing of synaptic contacts onto dendritic
subunits.},
address = {Department of Biomedical Engineering, University of Southern California,
Los Angeles, CA 90089, USA.},
au = {Poirazi, P and Mel, BW},
da = {20010413},
date-added = {2008-03-26 15:20:25 +0100},
date-modified = {2008-03-26 15:21:02 +0100},
dcom = {20010426},
edat = {2001/04/13 10:00},
issn = {0896-6273 (Print)},
jid = {8809320},
jt = {Neuron},
language = {eng},
lr = {20051116},
mh = {Animals; Brain/*physiology/ultrastructure; Computer Simulation; Dendrites/*physiology;
Humans; Learning/physiology; Mathematics; Memory/*physiology; Models,
Biological; *Neuronal Plasticity; Neurons/physiology/ultrastructure;
Synapses/physiology},
mhda = {2001/05/01 10:01},
own = {NLM},
owner = {sprekeler},
pii = {S0896-6273(01)00252-5},
pl = {United States},
pmid = {11301036},
pst = {ppublish},
pt = {Journal Article; Review},
pubm = {Print},
rf = {106},
sb = {IM},
so = {Neuron. 2001 Mar;29(3):779-96. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Vanderpol26,
author = {B. van der Pol},
title = {On relaxation oscillators},
journal = {Phil. Mag.},
year = {1926},
volume = {2},
pages = {978-992}
}
@article{Poliakov97,
author = {A. V. Poliakov and R. K. Powers and M. C. Binder},
title = {Functional identification of input-output transforms of motoneurons
in cat},
journal = {J. Physiology},
year = {1997},
volume = {504},
pages = {401-424}
}
@article{Poliakov96,
author = {A. V. Poliakov and R. K. Powers and A. Sawczuk and M. C. Binder},
title = {Effects of background noise on the response of rat and cat motoneurones
to excitatory current transients.},
journal = {J. Physiology},
year = {1996},
volume = {495},
pages = {143-157}
}
@article{Polley06,
author = {D. B. Polley and E. Steinberg and M. Merzenich},
title = {Perceptual learning directs auditory cortical map reorganization
through top-down influences},
journal = {The Journal of Neuroscience},
year = {2006},
volume = {26},
pages = {4970-4982},
number = {18},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Polsky04,
author = {A. Polsky and B. W Mel and J. Schiller},
title = {Computational subunits in thin dendrites of pyramidal cells},
journal = {Nature Neuroscience},
year = {2004},
volume = {7},
pages = {621-627}
}
@article{Polsky04a,
author = {Alon Polsky and Bartlett W. Mel and Jackie Schiller},
title = {Computational Subunits in thin dendrites of pyramidal cells},
journal = {Nature Neuroscience},
year = {2004},
volume = {6},
pages = {621--627},
number = {7},
keywords = {Neuronal-Processing},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Porr01,
author = {B. Porr and F. W\"org\"otter},
title = {Temporal Hebbian Learning in Rate-Coded Neural Networks: A Theoretical
Approach towards Classical Conditioning},
booktitle = {Artificial Neural Networks --- ICANN 2001},
year = {2001},
editor = {Dorffner, Georg and Bischof, Horst and Hornik, Kurt},
volume = {2130},
pages = {1115--1120},
address = {Berlin},
publisher = {Springer}
}
@article{Porta05,
author = {J. M. Porta and E. Celaya},
title = {Reinforcement Learning for Agents with Many Sensors and Actuators
Acting in Categorizable Environments},
journal = {Journal of Artificial Intelligence Research},
year = {2005},
volume = {23},
pages = {79-122}
}
@article{Pospischil07,
author = {M. Pospischil and Z. Piwkowska and M. Rudolph and T. Bal and A. Destexhe},
title = {Calculating event-triggered average synaptic conductances from the
membrane potential},
journal = {J. Neurophysiology},
year = {2007},
volume = {97},
pages = {2544-2552}
}
@article{Poucet03,
author = {B. Poucet and P. P. Lenck-Santini and V. E. Paz-Villagr{\'a}n and
E. Save},
title = {Place cells, neocortex and spatial navigation: a short review},
journal = {Journal of physiology (Paris)},
year = {2003},
volume = {97},
pages = {537--546},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Pouget03,
author = {A. Pouget and P. Dayan and R. Zemel},
title = {Inference and computation with population codes},
journal = {Annual Reviews in Neuroscience},
year = {2003},
volume = {26},
pages = {381-410},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Powers96,
author = {R.K.D.B. Powers and M.D. Binder},
title = {Experimental evaluation of input-output models of motoneuron discharges},
journal = {J. Neurophysiology},
year = {1996},
volume = {75},
pages = {367-379}
}
@article{Pratt08,
author = {Kara G Pratt and Wei Dong and Carlos D Aizenman},
title = {Development and spike timing-dependent plasticity of recurrent excitation
in the Xenopus optic tectum.},
journal = {Nat Neurosci},
year = {2008},
volume = {11},
pages = {467--475},
number = {4},
month = {Apr},
abstract = {Much of the information processing in the brain occurs at the level
of local circuits; however, the mechanisms underlying their initial
development are poorly understood. We sought to examine the early
development and plasticity of local excitatory circuits in the optic
tectum of Xenopus laevis tadpoles. We found that retinal input recruits
persistent, recurrent intratectal synaptic excitation that becomes
more temporally compact and less variable over development, thus
increasing the temporal coherence and precision of tectal cell spiking.
We also saw that patterned retinal input can sculpt recurrent activity
according to a spike timing-dependent plasticity rule, and that impairing
this plasticity during development results in abnormal refinement
of the temporal characteristics of recurrent circuits. This plasticity
is a previously unknown mechanism by which patterned retinal activity
allows intratectal circuitry to self-organize, optimizing the temporal
response properties of the tectal network, and provides a substrate
for rapid modulation of tectal neuron receptive-field properties.},
doi = {10.1038/nn2076},
keywords = {plasticity, vision},
owner = {sprekeler},
pii = {nn2076},
pmid = {18344990},
timestamp = {2008.04.23},
url = {http://dx.doi.org/10.1038/nn2076}
}
@article{Prescott06,
author = {Prescott, Steven A and Ratte, Stephanie and De Koninck, Yves and
Sejnowski, Terrence J},
title = {Nonlinear interaction between shunting and adaptation controls a
switch between integration and coincidence detection in pyramidal
neurons.},
journal = {J Neurosci},
year = {2006},
volume = {26},
pages = {9084--9097},
number = {36},
abstract = {The membrane conductance of a pyramidal neuron in vivo is substantially
increased by background synaptic input. Increased membrane conductance,
or shunting, does not simply reduce neuronal excitability. Recordings
from hippocampal pyramidal neurons using dynamic clamp revealed that
adaptation caused complete cessation of spiking in the high conductance
state, whereas repetitive spiking could persist despite adaptation
in the low conductance state. This behavior was reproduced in a phase
plane model and was explained by a shunting-induced increase in voltage
threshold. The increase in threshold allows greater activation of
the M current (I(M)) at subthreshold potentials and reduces the minimum
adaptation required to stabilize the system; in contrast, activation
of the afterhyperpolarization current is unaffected by the increase
in threshold and therefore remains unable to stop repetitive spiking.
The nonlinear interaction between shunting and I(M) has other important
consequences. First, timing of spikes elicited by brief stimuli is
more precise when background spikes elicited by sustained input are
prohibited, as occurs exclusively with I(M)-mediated adaptation in
the high conductance state. Second, activation of I(M) at subthreshold
potentials, which is increased in the high conductance state, hyperpolarizes
average membrane potential away from voltage threshold, allowing
only large, rapid fluctuations to reach threshold and elicit spikes.
These results suggest that the shift from a low to high conductance
state in a pyramidal neuron is accompanied by a switch from encoding
time-averaged input with firing rate to encoding transient inputs
with precisely timed spikes, in effect, switching the operational
mode from integration to coincidence detection.},
address = {Computational Neurobiology Laboratory, Howard Hughes Medical Institute,
Salk Institute, La Jolla, California 92037, USA. sprescott@salk.edu},
au = {Prescott, SA and Ratte, S and De Koninck, Y and Sejnowski, TJ},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.1388-06.2006},
da = {20060907},
date-added = {2008-03-27 14:14:33 +0100},
date-modified = {2008-03-27 14:14:38 +0100},
dcom = {20060926},
doi = {10.1523/JNEUROSCI.1388-06.2006},
edat = {2006/09/08 09:00},
issn = {1529-2401 (Electronic)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20061115},
mh = {Action Potentials/*physiology; Adaptation, Physiological/physiology;
Animals; Cells, Cultured; Computer Simulation; Differential Threshold/physiology;
Membrane Potentials/physiology; *Models, Neurological; Nerve Net/*physiology;
Neuronal Plasticity/*physiology; Pyramidal Cells/*physiology; Rats;
Rats, Sprague-Dawley; Synaptic Transmission/*physiology},
mhda = {2006/09/27 09:00},
own = {NLM},
owner = {sprekeler},
pii = {26/36/9084},
pl = {United States},
pmid = {16957065},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {J Neurosci. 2006 Sep 6;26(36):9084-97. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Prinz03a,
author = {Prinz, Astrid A and Billimoria, Cyrus P and Marder, Eve},
title = {Alternative to hand-tuning conductance-based models: construction
and analysis of databases of model neurons.},
journal = {J Neurophysiol},
year = {2003},
volume = {90},
pages = {3998--4015},
number = {6},
abstract = {Conventionally, the parameters of neuronal models are hand-tuned using
trial-and-error searches to produce a desired behavior. Here, we
present an alternative approach. We have generated a database of
about 1.7 million single-compartment model neurons by independently
varying 8 maximal membrane conductances based on measurements from
lobster stomatogastric neurons. We classified the spontaneous electrical
activity of each model neuron and its responsiveness to inputs during
runtime with an adaptive algorithm and saved a reduced version of
each neuron's activity pattern. Our analysis of the distribution
of different activity types (silent, spiking, bursting, irregular)
in the 8-dimensional conductance space indicates that the coarse
grid of conductance values we chose is sufficient to capture the
salient features of the distribution. The database can be searched
for different combinations of neuron properties such as activity
type, spike or burst frequency, resting potential, frequency-current
relation, and phase-response curve. We demonstrate how the database
can be screened for models that reproduce the behavior of a specific
biological neuron and show that the contents of the database can
give insight into the way a neuron's membrane conductances determine
its activity pattern and response properties. Similar databases can
be constructed to explore parameter spaces in multicompartmental
models or small networks, or to examine the effects of changes in
the voltage dependence of currents. In all cases, database searches
can provide insight into how neuronal and network properties depend
on the values of the parameters in the models.},
address = {Volen Center and Biology Department, Brandeis University, Waltham,
Massachusetts 02454, USA. prinz@brandeis.edu},
au = {Prinz, AA and Billimoria, CP and Marder, E},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00641.2003},
da = {20031210},
date-added = {2007-12-12 19:55:04 +0100},
date-modified = {2007-12-12 19:55:21 +0100},
dcom = {20040211},
dep = {20030827},
doi = {10.1152/jn.00641.2003},
edat = {2003/08/29 05:00},
gr = {MH-46742/MH/United States NIMH},
issn = {0022-3077 (Print)},
jid = {0375404},
jt = {Journal of neurophysiology},
keywords = {Algorithms; Animals; Computer Simulation; *Databases, Factual; Electric
Stimulation; Electrophysiology; Membrane Potentials/physiology; Models,
Neurological; Nephropidae; Neurons/classification/*physiology},
language = {eng},
lr = {20071114},
mhda = {2004/02/12 05:00},
own = {NLM},
owner = {sprekeler},
phst = {2003/08/27 {$[$}aheadofprint{$]$}},
pii = {00641.2003},
pl = {United States},
pmid = {12944532},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.},
pubm = {Print-Electronic},
sb = {IM},
so = {J Neurophysiol. 2003 Dec;90(6):3998-4015. Epub 2003 Aug 27. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Prinz04b,
author = {Prinz, Astrid A and Bucher, Dirk and Marder, Eve},
title = {Similar network activity from disparate circuit parameters.},
journal = {Nat Neurosci},
year = {2004},
volume = {7},
pages = {1345--1352},
number = {12},
abstract = {It is often assumed that cellular and synaptic properties need to
be regulated to specific values to allow a neuronal network to function
properly. To determine how tightly neuronal properties and synaptic
strengths need to be tuned to produce a given network output, we
simulated more than 20 million versions of a three-cell model of
the pyloric network of the crustacean stomatogastric ganglion using
different combinations of synapse strengths and neuron properties.
We found that virtually indistinguishable network activity can arise
from widely disparate sets of underlying mechanisms, suggesting that
there could be considerable animal-to-animal variability in many
of the parameters that control network activity, and that many different
combinations of synaptic strengths and intrinsic membrane properties
can be consistent with appropriate network performance.},
address = {Volen Center and Biology Department, Brandeis University, Mail Stop
013, 415 South Street, Waltham, Massachusetts 02454-9110, USA. prinz@brandeis.edu
},
au = {Prinz, AA and Bucher, D and Marder, E},
bdsk-url-1 = {http://dx.doi.org/10.1038/nn1352},
cin = {Nat Neurosci. 2004 Dec;7(12):1287-8. PMID: 15643435},
da = {20050111},
date-added = {2008-03-28 11:55:00 +0100},
date-modified = {2008-03-28 11:55:06 +0100},
dcom = {20050215},
dep = {20041121},
doi = {10.1038/nn1352},
edat = {2004/11/24 09:00},
gr = {MH-46742/MH/United States NIMH},
issn = {1097-6256 (Print)},
jid = {9809671},
jt = {Nature neuroscience},
language = {eng},
lr = {20071114},
mh = {Action Potentials/*physiology; Animals; Crustacea; Nerve Net/*physiology;
Neurons/physiology; Synapses/physiology},
mhda = {2005/02/16 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2004/08/26 {$[$}received{$]$}; 2004/09/28 {$[$}accepted{$]$}; 2004/11/21
{$[$}aheadofprint{$]$}},
pii = {nn1352},
pl = {United States},
pmid = {15558066},
pst = {ppublish},
pt = {Comparative Study; Journal Article; Research Support, Non-U.S. Gov't;
Research Support, U.S. Gov't, P.H.S.},
pubm = {Print-Electronic},
sb = {IM},
so = {Nat Neurosci. 2004 Dec;7(12):1345-52. Epub 2004 Nov 21. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Prinz04,
author = {A.A. Prinz and L.F. Abbott and E. Marder},
title = {The dynamic clamp comes of age},
journal = {Trends in Neurosciences},
year = {2004},
volume = {27},
pages = {218-224}
}
@article{Prinz04a,
author = {Prinz, A.A. and Abbott, LF and Marder, E.},
title = {{The dynamic clamp comes of age}},
journal = {Trends Neurosci},
year = {2004},
volume = {27},
pages = {218--224},
number = {4},
keywords = {various-artists},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@article{Prinz03,
author = {A.A. Prinz and C.P. Billimoria and E. Marder},
title = {Alternative to hand-tuning conductance-based models: construction
and analysis of databases of model neurons},
journal = {J. Neurophysiology},
year = {2003},
volume = {90},
pages = {3998-4015}
}
@article{Perez95,
author = {P{\'e}rez, G. and Cerdeira, H.A.},
title = {{Extracting Messages Masked by Chaos}},
journal = {Physical Review Letters},
year = {1995},
volume = {74},
pages = {1970--1973},
number = {11},
publisher = {APS}
}
@article{Quiroga05,
author = {R. Quian Quiroga and L. Reddy and G Kreiman and C. Koch and I Fried},
title = {Invariant visual representation by single neurons in the human brain},
journal = {Nature},
year = {2005},
volume = {435},
pages = {1102--1107},
keywords = {Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Rabinovich06,
author = {Rabinovich, M.I. and Varona, P. and Selverston, A.I. and Abarbanel,
H.D.I.},
title = {{Dynamical principles in neuroscience}},
journal = {Reviews of Modern Physics},
year = {2006},
volume = {78},
pages = {1213--1265},
number = {4},
keywords = {various-artists},
owner = {sprekeler},
publisher = {APS},
timestamp = {2008.04.14}
}
@book{Rachevsky38,
title = {Mathematical Biophysics},
publisher = {University of Chicago Press; Reprinted by Dover, New York, 1960},
year = {1938},
author = {N. Rachevsky}
}
@inproceedings{Rahimi05,
author = {Rahimi, A. and Recht, B. and Darrell, T.},
title = {{Learning appearance manifolds from video}},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(CVPR)},
year = {2005},
volume = {1},
pages = {868--875},
keywords = {Various-Artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Raiguel06,
author = {S. Raiguel and R. Vogels and S. G. Mysore and G. A. Orban},
title = {Learning to see the difference specifically alters the most informative
V4 neurons},
journal = {The Journal of Neuroscience},
year = {2006},
volume = {26},
pages = {6589-6602},
number = {24},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Rall89,
author = {Wilfried Rall},
title = {Cable theory for dendritic neurons},
booktitle = {Methods in Neuronal Modeling},
year = {1989},
editor = {C. Koch and I. Segev},
pages = {9-62},
address = {Cambridge},
publisher = {MIT Press}
}
@inproceedings{Rall64,
author = {W. Rall},
title = {Theoretical significance of dendritic trees for neuronal input--output
relations.},
booktitle = {Neural theory and modeling},
year = {1964},
editor = {R. F. Reiss},
pages = {73--97},
address = {Stanford CA},
publisher = {Stanford University Press}
}
@book{Rall95,
title = {The theoretical foundation of dendritic function : selected papers
of Wilfrid Rall with commentaries },
publisher = {MIT Press},
year = {1995},
author = {Rall, Wilfrid and Segev, Idan and Rinzel, John and Shepherd, Gordon
M.},
address = {Cambridge, Mass. },
call-number = {QP361},
date-added = {2008-03-26 15:00:21 +0100},
date-modified = {2008-03-26 15:00:26 +0100},
dewey-call-number = {612.8},
genre = {Dendrites},
isbn = {0262193566},
library-id = {94013538},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Cajal09,
title = {Histologie du syst{\`e}me nerveux de l'homme et des vert{\'e}br{\'e}},
publisher = {A. Maloine},
year = {1909},
author = {S. {Ram{\`o}n y Cajal}},
address = {Paris}
}
@article{Ranck73,
author = {J.B. Ranck},
title = {Studies on single neurons in dorsal hippocampal formation and septum
in unrestrained rats. {I}. Behavioral correlates and firing repertoires.},
journal = {Experimental Neurology},
year = {1973},
volume = {41},
pages = {461--531},
number = {2},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Ranck84,
author = {J. B. Ranck},
title = {Head direction cells in the deep cell layer of dorsolateral pre-subiculum
in freely moving rats},
journal = {Society for Neuroscience Abstracts},
year = {1984},
volume = {10},
pages = {599},
note = {First article on head direction cells},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Rao98a,
author = {R. Rao and D. Ballard},
title = {Development of localized oriented receptive fields by learning a
translation-invariant code for natural images},
journal = {Network: Computation in Neural Systems},
year = {1998},
volume = {9},
pages = {219--234},
keywords = {Vision-Models, vision, invariance learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Rao98,
author = {R. Rao and D. Ruderman},
title = {Learning {Lie} groups for Invariant Visual Perception},
booktitle = {Neural Information Processing Systems},
year = {1998},
volume = {1998},
pages = {810--816},
keywords = {Vision-Models, vision, invariance learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@misc{Rao01b,
author = {Rao, R.P.N. and Sejnowski, T.J.},
title = {{Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference
Learning}},
year = {2001},
journal = {Neural Computation},
number = {10},
owner = {sprekeler},
pages = {2221--2237},
publisher = {MIT Press},
timestamp = {2008.04.14},
volume = {13}
}
@inproceedings{Rao00,
author = {Rao, R.P.N. and Sejnowski, T.J.},
title = {{Predictive sequence learning in recurrent neocortical circuits}},
booktitle = {Neural Information Processing Systems},
year = {2000},
volume = {2000},
pages = {164--170},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Rao99,
author = {Rao, R P and Ballard, D H},
title = {{P}redictive coding in the visual cortex: {A} functional interpretation
of some extra-classical receptive-field effects},
journal = {Nature Neuroscience},
year = {1999},
volume = {2},
pages = {79--87},
number = {1},
month = {Jan},
keywords = {Vision-Models},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Rao01a,
author = {Rajesh P. Rao and Terrence J. Sejnowski},
title = {Spike-Timing-Dependent {H}ebbian Plasticity as Temporal Difference
Learning},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {2221--2238},
number = {10},
keywords = {slowness, Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Rao01,
author = {R. P. N. Rao and T. J. Sejnowski},
title = {Spike-timing dependent {H}ebbian plasticity as temporal difference
learning},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {2221-2237}
}
@article{Rapp92,
author = {M. Rapp and Y. Yarom and I. Segev},
title = {The impact of parallel fiber background activity pn the cable properties
of cerebellar {\protect Purkinje} cells},
journal = {Neural Comput.},
year = {1992},
volume = {4},
pages = {518-533}
}
@article{Rauch03,
author = {A. Rauch and G. Camera and H. L{\"u}scher and W. Senn and S. Fusi},
title = {Neocortical pyramidal cells respond as integrate-and-fire neurons
to in-vivo-like input currents},
journal = {J. Neurophysiology},
year = {2003},
volume = {90},
pages = {1598-1612}
}
@article{Rauch03a,
author = {Rauch, Alexander and La Camera, Giancarlo and Luscher, Hans-Rudolf
and Senn, Walter and Fusi, Stefano},
title = {Neocortical pyramidal cells respond as integrate-and-fire neurons
to in vivo-like input currents.},
journal = {J Neurophysiol},
year = {2003},
volume = {90},
pages = {1598--1612},
number = {3},
abstract = {In the intact brain neurons are constantly exposed to intense synaptic
activity. This heavy barrage of excitatory and inhibitory inputs
was recreated in vitro by injecting a noisy current, generated as
an Ornstein-Uhlenbeck process, into the soma of rat neocortical pyramidal
cells. The response to such in vivo-like currents was studied systematically
by analyzing the time development of the instantaneous spike frequency,
and when possible, the stationary mean spike frequency as a function
of both the mean and the variance of the input current. All cells
responded with an in vivo-like action potential activity with stationary
statistics that could be sustained throughout long stimulation intervals
(tens of seconds), provided the frequencies were not too high. The
temporal evolution of the response revealed the presence of mechanisms
of fast and slow spike frequency adaptation, and a medium duration
mechanism of facilitation. For strong input currents, the slow adaptation
mechanism made the spike frequency response nonstationary. The minimal
frequencies that caused strong slow adaptation (a decrease in the
spike rate by more than 1 Hz/s), were in the range 30-80 Hz and depended
on the pipette solution used. The stationary response function has
been fitted by two simple models of integrate-and-fire neurons endowed
with a frequency-dependent modification of the input current. This
accounts for all the fast and slow mechanisms of adaptation and facilitation
that determine the stationary response, and proved necessary to fit
the model to the experimental data. The coefficient of variability
of the interspike interval was also in part captured by the model
neurons, by tuning the parameters of the model to match the mean
spike frequencies only. We conclude that the integrate-and-fire model
with spike-frequency-dependent adaptation/facilitation is an adequate
model reduction of cortical cells when the mean spike-frequency response
to in vivo-like currents with stationary statistics is considered.},
address = {Institute of Physiology, University of Bern, 3012 Bern, Switzerland.},
au = {Rauch, A and La Camera, G and Luscher, HR and Senn, W and Fusi, S},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00293.2003},
da = {20030910},
date-added = {2008-03-27 01:34:17 +0100},
date-modified = {2008-03-27 01:34:22 +0100},
dcom = {20031119},
dep = {20030515},
doi = {10.1152/jn.00293.2003},
edat = {2003/05/17 05:00},
issn = {0022-3077 (Print)},
jid = {0375404},
jt = {Journal of neurophysiology},
language = {eng},
lr = {20061115},
mh = {Action Potentials/*physiology; Animals; Female; Male; Models, Biological;
Neocortex/*physiology; Neurons/physiology; Pyramidal Cells/*physiology;
Rats; Rats, Wistar},
mhda = {2003/12/03 05:00},
own = {NLM},
owner = {sprekeler},
phst = {2003/05/15 {$[$}aheadofprint{$]$}},
pii = {00293.2003},
pl = {United States},
pmid = {12750422},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print-Electronic},
sb = {IM},
so = {J Neurophysiol. 2003 Sep;90(3):1598-612. Epub 2003 May 15. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Recanzone92,
author = {G. H. Recanzone and W. M. Jenkins and G. T. Hradek and M. M. Merzenich},
title = {Progressive improvement in discriminative abilities in adult owl
monkeys performing a tactive frequency discrimination task},
journal = {The Journal of Neurophysiology},
year = {1992},
volume = {67},
pages = {1015-1030},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Recanzone93,
author = {G. H. Recanzone and C. E. Schreiner and M. M. Merzenich},
title = {Plasticity in the frequency representation of primary auditory cortex
following discrimination training in adult owl monkeys},
journal = {The Journal of Neuroscience},
year = {1993},
volume = {13},
pages = {87-103},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Recce96,
author = {M. Recce and K.D. Harris},
title = {{Memory for places: a navigational model in support of Marr's theory
of hippocampal function}},
journal = {Hippocampus},
year = {1996},
volume = {6},
pages = {735--748}
}
@article{Reddy06,
author = {L. Reddy and N. Kanwisher},
title = {Coding of visual objects in the ventral stream},
journal = {Current Opinions in Neurobiololy},
year = {2006},
volume = {16},
pages = {408--14},
number = {4},
keywords = {Vision},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Redish04,
author = {AD Redish},
title = {Addiction as a computational process gone awry},
journal = {Science},
year = {2004},
volume = {306},
pages = {1944--1947}
}
@article{Redish01,
author = {A.D. Redish},
title = {The hippocampal debate: are we asking the right questions?},
journal = {Behavioral Brain Research},
year = {2001},
volume = {127},
pages = {81--98},
number = {1--2},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Redish99,
title = {{Beyond the Cognitive Map, From Place Cells to Episodic Memory}},
publisher = {MIT Press-Bradford Books},
year = {1999},
author = {A. D. Redish},
address = {London}
}
@book{Redish99a,
title = {Beyond the Cognitive Map - From Place Cells to Episodic Memory},
publisher = {MIT Press},
year = {1999},
author = {A. D. Redish},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Redish00,
author = {A. D. Redish and B. L. McNaughton and C. A. Barnes},
title = {Place cell firing shows an inertia-like process},
journal = {Neurocomputing},
year = {2000},
volume = {32--33},
pages = {235--241},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Reed72,
title = {{Methods of modern mathematical physics. 1. Functional analysis}},
publisher = {Academic Press},
year = {1972},
author = {Reed, M. and Simon, B.},
keywords = {various-artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Regehr90,
author = {Regehr, W G and Tank, D W},
title = {Postsynaptic NMDA receptor-mediated calcium accumulation in hippocampal
CA1 pyramidal cell dendrites.},
journal = {Nature},
year = {1990},
volume = {345},
pages = {807--810},
number = {6278},
abstract = {In the CA1 hippocampal region, intracellular calcium is a putative
second messenger for the induction of long-term potentiation (LTP),
a persistent increase of synaptic transmission produced by high frequency
afferent fibre stimulation. Because LTP in this region is blocked
by the NMDA (N-methyl-D-aspartate) receptor antagonist AP5 (DL-2-amino-5-phosphonovaleric
acid) and the calcium permeability of NMDA receptors is controlled
by a voltage-dependent magnesium block, a model has emerged that
suggests that the calcium permeability of NMDA receptor-coupled ion
channels is the biophysical basis for LTP induction. We have performed
microfluorometric measurements in individual CA1 pyramidal cells
during stimulus trains that induce LTP. In addition to a widespread
component of postsynaptic calcium accumulation previously described,
we now report that brief high frequency stimulus trains produce a
transient component spatially localized to dendritic areas near activated
afferents. This localized component is blocked by the NMDA receptor
antagonist AP5. The results directly confirm the calcium rise predicted
by NMDA receptor models of LTP induction.},
address = {Biophysics Research Department, AT\&T Bell Laboratories, Murray Hill,
New Jersey 07974.},
au = {Regehr, WG and Tank, DW},
bdsk-url-1 = {http://dx.doi.org/10.1038/345807a0},
da = {19900802},
date-added = {2008-03-28 23:35:04 +0100},
date-modified = {2008-03-28 23:35:20 +0100},
dcom = {19900802},
doi = {10.1038/345807a0},
edat = {1990/06/28},
issn = {0028-0836 (Print)},
jid = {0410462},
jt = {Nature},
language = {eng},
lr = {20061115},
mh = {2-Amino-5-phosphonovalerate/pharmacology; Animals; Calcium/*metabolism;
Electric Stimulation; Guinea Pigs; Hippocampus/cytology/*physiology;
Learning/physiology; Neural Pathways/physiology; Receptors, N-Methyl-D-Aspartate;
Receptors, Neurotransmitter/*physiology; Synaptic Transmission},
mhda = {1990/06/28 00:01},
own = {NLM},
owner = {sprekeler},
pl = {ENGLAND},
pmid = {1972782},
pst = {ppublish},
pt = {In Vitro; Journal Article},
pubm = {Print},
rn = {0 (Receptors, N-Methyl-D-Aspartate); 0 (Receptors, Neurotransmitter);
7440-70-2 (Calcium); 76726-92-6 (2-Amino-5-phosphonovalerate)},
sb = {IM},
so = {Nature. 1990 Jun 28;345(6278):807-10. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Reich98,
author = {D.S. Reich and J.D. Victor and B.W. Knight},
title = {The power ratio and the interval map: spiking models and extracellular
recordings},
journal = {J. of Neuroscience},
year = {1998},
volume = {18},
pages = {10090-10104},
number = {23},
annote = {introduces a modulated renewal process: modulated renewal processes
are further divided in (i)simple modulated renewal processes (that
can be mapped to standard renewal processes by a rescaling of time)
and (ii) a second class that contains the leaky IF neuron and the
Poisson neuron with absolute refractoriness. The conceps are tested
on experimental and model data.
}
}
@article{Reich97,
author = {D.S. Reich and J.D. Victor and B.W. Knight and T. Ozaki and E. Kaplan},
title = {Response Variability and Timing precision of neuronal spike trains
in vivo},
journal = {J. Neurophysiology},
year = {1997},
volume = {77},
pages = {2836-2841}
}
@incollection{Reichardt69,
author = {W. Reichardt},
title = {Movement perception in insects},
booktitle = {Processing of optical data by organisms and by machines},
publisher = {Academic Press, New York},
year = {1969},
editor = {W. Reichardt}
}
@book{Reif87,
title = {{Statistische Physik und Theorie der W{\"a}rme}},
publisher = {de Gruyter},
year = {1987},
author = {Reif, F. and Muschik, W.},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Reinagel00,
author = {P. Reinagel and R. C. Reid},
title = {Temporal coding of visual information in the thalamus},
journal = {J. of Neuroscience},
year = {2002},
volume = {20},
pages = {5392-5400}
}
@article{Reinagel02,
author = {P. Reinagel and R. C. Reid},
title = {Precise firing events are conserved across neurons},
journal = {J. of Neuroscience},
year = {2002},
volume = {22},
pages = {6837-6841}
}
@book{Reiss64,
title = {Neural theory and modeling: proceedings of the 1962 Ojai symposium},
publisher = {Stanford University Press},
year = {1964},
author = {Reiss, Richard F.},
address = {Stanford, Calif.},
call-number = {QP361},
date-added = {2008-03-26 17:39:24 +0100},
date-modified = {2008-03-26 17:39:36 +0100},
dewey-call-number = {574.18},
genre = {Neurology},
library-id = {64013359},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@inproceedings{Reitboeck83,
author = {H. J. A. Reitboeck},
title = {A multi electrode matrix for studies of temporal signal correlations
within neural assemblies.},
booktitle = {Synergetics of the brain},
year = {1983},
editor = {Basar E. et al.},
pages = {174--182},
address = {Berlin Heidelberg New York},
publisher = {Springer--Verlag}
}
@article{Renart03,
author = {A. Renart and P. Song and X. J. Wang},
title = {Robust Spatial Working Memory through homeostatic synaptic scaling
in heterogeneous Cortical Networks},
journal = {Neuron},
year = {2003},
volume = {38},
pages = {473--485},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@incollection{Rescorla72,
author = {R.A. Rescorla and A.R. Wagner},
title = {A theory of Pavlovian conditioning: variations in the effectiveness
of reinforecement and nonreinforcement},
booktitle = {Classical Conditioning \protect{II}: current research and theory},
publisher = {Appleton Century Crofts},
year = {1972},
editor = {A. H. Black and W.F. Prokasy},
pages = {64-99},
address = {New York}
}
@article{Reutimann02,
author = {J. Reutimann and M. Giugliano and S. Fusi},
title = {Event-driven simulation of spiking neurons with stochastic dynamics},
journal = {Neural Computation},
year = {2003},
volume = {xx},
pages = {xx}
}
@article{Reyes96,
author = {A. D. Reyes and E. W. Rubel and W. J. Spain},
title = {In vitro analysis of optimal stimuli for phase-locking and time-delayed
modulation of firing in avian nucleus laminaris neuron},
journal = {J. Neurosci},
year = {1996},
volume = {16},
pages = {993-1007}
}
@article{Reyes94,
author = {A. D. Reyes and E. W. Rubel and W. J. Spain},
title = {Membrane properties Underlying the Firing of Neurons in the Avian
Cochlear Nucleus},
journal = {J. Neurosci.},
year = {1994},
volume = {14},
pages = {5352--5364},
number = {9}
}
@article{Reynolds01,
author = {Reynolds, John N. J. and Hyland, Brian I. and Wickens, Jeffery R.},
title = {A cellular mechanism of reward-related learning},
journal = {Nature},
year = {2001},
volume = {413},
pages = {67--70},
number = {6851},
month = sep,
comment = {10.1038/35092560},
issn = {0028-0836},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1038/35092560}
}
@article{Rhode86,
author = {William S. Rhode and Philip H. Smith},
title = {Encoding Timing and Intensity in the Ventral Cochlear Nucleus of
the Cat},
journal = {J. Neurophysiol.},
year = {1986},
volume = {56},
pages = {261--286},
number = {2}
}
@article{Rhode86a,
author = {William S. Rhode and Philip H. Smith},
title = {Physiological Studies on Neurons in the Dorsal Cochlear Nucleus of
the Cat},
journal = {J. Neurophysiol.},
year = {1986},
volume = {56},
pages = {287--307},
number = {2}
}
@article{Rhodes07,
author = {A. Rhodes-Morrison and A. Aertsen and M. Diesmann},
title = {Spike-timing dependent plasticity in balanced random networks},
journal = {Neural Computation},
year = {2007},
volume = {19},
pages = {1437-1467}
}
@book{Riccardi77,
title = {Diffusion Processes and related topics in biology},
publisher = {Springer-Verlag},
year = {1977},
author = {L. M. Ricciardi},
address = {Berlin}
}
@article{Rich07,
author = {Mark M. Rich and Peter Wenner},
title = {Sensing and expressing homeostatic synaptic plasticity},
journal = {Trends in Neurosciences},
year = {2007},
volume = {30},
pages = {119-125},
number = {3},
month = mar,
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.sciencedirect.com/science/article/B6T0V-4MY0TK9-1/2/72817062104ed62a6cea62a6d2a04a71}
}
@article{Kempter01b,
author = {Richard Kempter and Christian Leibold and Hermann Wagner and J. Leo van
Hemmen},
title = {Formation of temporal-feature maps by axonal propagation of synaptic
learning},
journal = {Proc. Natl. Academy of Sciences USA},
year = {2001},
volume = {98},
pages = {4166-4171}
}
@article{Richardson04,
author = {M.J.E. Richardson},
title = {The Effects of Synaptic Conductance on the Voltage Distribution and
Firing Rate of Spiking Neurons},
journal = {Physical Review E},
year = {2004},
volume = {69},
pages = {51918}
}
@article{Richardson03a,
author = {MJE Richardson and N. Brunel and V. Hakim},
title = {from Subthreshold to Firing-Rate Resonance},
journal = {J. Neurophysiology},
year = {2003},
volume = {89},
pages = {2538-2554}
}
@article{Richardson06a,
author = {M.J.E. Richardson and W. Gerstner},
title = { Statistics of subthreshold neuronal voltage fluctuations due to
conductance-based synaptic shot noise },
journal = {Chaos},
year = {2006},
volume = {16},
pages = {26106}
}
@article{Richardson05,
author = {M.J.E. Richardson and W. Gerstner},
title = {Synaptic Shot Noise and Conductance Fluctuations Affect the Membrane
Voltage with Equal Significance},
journal = {Neural Computation},
year = {2005},
volume = {17},
pages = {923-947}
}
@article{Richardson05b,
author = {M.J.E. Richardson and O. Melamed and G. Silberberg and W. Gerstner
and H. Markram},
title = {Short-term-plasticity orchestrates the response of pyramidal cells
and interneurons to population bursts },
journal = {J. Computational Neuroscience},
year = {2005},
volume = {18},
pages = {323-331}
}
@article{Richardson03,
author = {Richardson, Magnus J E and Brunel, Nicolas and Hakim, Vincent},
title = {From subthreshold to firing-rate resonance.},
journal = {J Neurophysiol},
year = {2003},
volume = {89},
pages = {2538--2554},
number = {5},
abstract = {Many types of neurons exhibit subthreshold resonance. However, little
is known about whether this frequency preference influences spike
emission. Here, the link between subthreshold resonance and firing
rate is examined in the framework of conductance-based models. A
classification of the subthreshold properties of a general class
of neurons is first provided. In particular, a class of neurons is
identified in which the input impedance exhibits a suppression at
a nonzero low frequency as well as a peak at higher frequency. The
analysis is then extended to the effect of subthreshold resonance
on the dynamics of the firing rate. The considered input current
comprises a background noise term, mimicking the massive synaptic
bombardment in vivo. Of interest is the modulatory effect an additional
weak oscillating current has on the instantaneous firing rate. When
the noise is weak and firing regular, the frequency most preferentially
modulated is the firing rate itself. Conversely, when the noise is
strong and firing irregular, the modulation is strongest at the subthreshold
resonance frequency. These results are demonstrated for two specific
conductance-based models and for a generalization of the integrate-and-fire
model that captures subthreshold resonance. They suggest that resonant
neurons are able to communicate their frequency preference to postsynaptic
targets when the level of noise is comparable to that prevailing
in vivo.},
address = {Laboratoire de Physique Statistique, Ecole Normale Superieure, 75231
Paris Cedex 05, France. Magnus.Richardson@epfl.ch},
au = {Richardson, MJ and Brunel, N and Hakim, V},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00955.2002},
da = {20030512},
date-added = {2008-03-14 17:10:13 +0100},
date-modified = {2008-03-14 17:10:18 +0100},
dcom = {20030702},
dep = {20021227},
doi = {10.1152/jn.00955.2002},
edat = {2003/03/04 04:00},
issn = {0022-3077 (Print)},
jid = {0375404},
jt = {Journal of neurophysiology},
language = {eng},
lr = {20061115},
mh = {Algorithms; Electrophysiology; Hippocampus/cytology/physiology; Interneurons/physiology;
Membrane Potentials/physiology; Models, Neurological; Neural Conduction/physiology;
Neurons/*physiology; Stochastic Processes; Synapses/physiology},
mhda = {2003/07/03 05:00},
own = {NLM},
owner = {sprekeler},
phst = {2002/12/27 {$[$}aheadofprint{$]$}},
pii = {00955.2002},
pl = {United States},
pmid = {12611957},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print-Electronic},
sb = {IM},
so = {J Neurophysiol. 2003 May;89(5):2538-54. Epub 2002 Dec 27. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Richmond90,
author = {Barry J. Richmond and Lance M. Optican and Hedva Spitzer},
title = {Temporal Encoding of Two-Dimensional Patterns by Single Units in
Primate Primary Visual Cortex. I. Stimulus-Response Relations},
journal = {{Journal of Neuroscience}},
year = {1990},
volume = {64},
pages = {351--369},
number = {2},
keywords = {Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Riedel88,
author = {U. Riedel and R. K\protect{\"u}hn and J. L. van~Hemmen},
title = {Temporal sequences and chaos in neural nets.},
journal = {Phys. Rev. A},
year = {1988},
volume = {38},
pages = {1105--1108}
}
@article{Riegle07,
author = {Riegle, Kenneth C. and Meyer, Ronald L.},
title = {Rapid Homeostatic Plasticity in the Intact Adult Visual System},
journal = {Journal of Neuroscience},
year = {2007},
volume = {27},
pages = {10556-10567},
number = {39},
abstract = {Neurons may possess activity-dependent homeostatic mechanisms that
permit them to globally alter synaptic strength as activity varies.
We used the retinotectal projection of goldfish to test this idea
in the intact adult CNS. We first altered tectal neuron activity
by selectively manipulating excitatory input. When excitatory synaptic
drive to tectal neurons was eliminated by blocking optic fibers,
current evoked at optic synapses increased by 183% within 90 min.
With partial activity blockade, the increase in synaptic strength
scaled with the magnitude of activity depression. This silence-induced
potentiation was also rapidly reversible. Conversely, an increase
in optic input was followed by a decrease in evoked synaptic current.
When optic drive was not altered and tectal neuronal activity was
instead increased or decreased pharmacologically via GABAA receptors,
synaptic strength again changed inversely with activity, indicating
that synaptic strength changed in response to neuronal activity and
not excitatory drive. Furthermore, altered synaptic strength tended
to return ongoing activity to baseline. Changes in synaptic strength
could also be detected in heterosynaptic pathways, indicating a global
response. Finally, changes in synaptic strength were associated with
corresponding changes in ongoing and evoked firing rates, indicating
that the responsivity of tectal neurons was altered. Thus, tectal
neurons exhibit archetypical homeostasis, one of the first robust
examples in the intact adult CNS.},
doi = {10.1523/JNEUROSCI.1631-07.2007},
eprint = {http://www.jneurosci.org/cgi/reprint/27/39/10556.pdf},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
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Despite the importance of this question, no definite answer has been
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address = {Unite de Neuroscience Integratives et Computationnelles, CNRS, UPR-2191,
Bat. 32-33, Avenue de la Terrasse, 91198 Gif-sur-Yvette, France.
Michael.Rudolph@iaf.cnrs-gif.fr},
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dcom = {20030711},
edat = {2003/05/27 05:00},
gr = {R01-NS37711/NS/United States NINDS},
issn = {0929-5313 (Print)},
jid = {9439510},
jt = {Journal of computational neuroscience},
language = {eng},
lr = {20071114},
mh = {Action Potentials/*physiology; Animals; Cats; Cell Compartmentation/physiology;
Cortical Synchronization; Dendrites/physiology/ultrastructure; Models,
Neurological; Neocortex/cytology/*physiology; Nerve Net/cytology/*physiology;
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mhda = {2003/07/12 05:00},
own = {NLM},
owner = {sprekeler},
pii = {5119742},
pl = {United States},
pmid = {12766426},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.},
pubm = {Print},
sb = {IM},
so = {J Comput Neurosci. 2003 May-Jun;14(3):239-51. },
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pages = {1005--1010}
}
@book{Sakmann95,
title = {Single-channel recording },
publisher = {Plenum Press},
year = {1995},
author = {Sakmann, Bert and Neher, Erwin},
address = {New York },
edition = {2nd ed},
bdsk-url-1 = {http://www.loc.gov/catdir/enhancements/fy0813/95003364-d.html},
call-number = {QH603.I54},
date-added = {2008-03-18 16:41:22 +0100},
date-modified = {2008-03-18 16:41:27 +0100},
dewey-call-number = {574.87/5},
genre = {Ion channels},
isbn = {030644870X},
library-id = {95003364},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.loc.gov/catdir/enhancements/fy0813/95003364-d.html}
}
@article{Salinas06,
author = {Salinas, Emilio},
title = {How Behavioral Constraints May Determine Optimal Sensory Representations},
journal = {PLoS Biology},
year = {2006},
volume = {4},
pages = {e387--},
number = {12},
month = dec,
abstract = {The sensory-triggered activity of a neuron is typically characterized
in terms of a tuning curve, which describes the neuron's average
response as a function of a parameter that characterizes a physical
stimulus. What determines the shapes of tuning curves in a neuronal
population? Previous theoretical studies and related experiments
suggest that many response characteristics of sensory neurons are
optimal for encoding stimulus-related information. This notion, however,
does not explain the two general types of tuning profiles that are
commonly observed: unimodal and monotonic. Here I quantify the efficacy
of a set of tuning curves according to the possible downstream motor
responses that can be constructed from them. Curves that are optimal
in this sense may have monotonic or nonmonotonic profiles, where
the proportion of monotonic curves and the optimal tuning-curve width
depend on the general properties of the target downstream functions.
This dependence explains intriguing features of visual cells that
are sensitive to binocular disparity and of neurons tuned to echo
delay in bats. The numerical results suggest that optimal sensory
tuning curves are shaped not only by stimulus statistics and signal-to-noise
properties but also according to their impact on downstream neural
circuits and, ultimately, on behavior.},
keywords = {Optimal-Coding},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://dx.doi.org/10.1371%2Fjournal.pbio.0040387}
}
@article{Salinas97,
author = {E. Salinas and L.F. Abbott},
title = {Invariant visual responses from attentional gain fields},
journal = {J. of Neurophysiology},
year = {1997},
volume = {77},
pages = {3267-3272}
}
@article{Salinas96,
author = {E. Salinas and L.F. Abbott},
title = {A model of multiplicative neural responses in parietal cortex},
journal = {Proc. Natl. Academy Sci. USA},
year = {1996},
volume = {93},
pages = {11956-11961}
}
@article{Salinas95,
author = {E. Salinas and L.F. Abbott},
title = {Transfer of coded information from sensory to motor networks},
journal = {J. of Neuroscience},
year = {1995},
volume = {15},
pages = {6461-6476}
}
@article{Salinas94,
author = {E. Salinas and L.F. Abbott},
title = {Vector reconstruction from firing rates},
journal = {J. Computat. Neurosci.},
year = {1994},
volume = {1},
pages = {89-107}
}
@article{Salinas02,
author = {E. Salinas and T. Sejnowski},
title = {Integrate-and-fire neurons driven by correlated stochastic input},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {2111-2155}
}
@article{Salinas00,
author = {E. Salinas and T.J. Sejnowski},
title = {Impact of correlated synaptic input on output firing rate and variability
in simple neuronal models},
journal = {J. of Neuroscience},
year = {2000},
volume = {20},
pages = {6193-6209}
}
@article{Samejima05,
author = {K Samejima and Y Ueda and K Doya and M Kimura},
title = {Representation of action-specific reward value in the striatum},
journal = {Science},
year = {2005},
volume = {310},
pages = {1337--1340}
}
@book{Sampath77,
title = {Stochastic models for spike trains of single neurons},
publisher = {Springer},
year = {1977},
author = {G. Sampath and S. K. Srinivasan},
address = {Berlin Heidelberg New York}
}
@article{Samsonowitch97,
author = {A. Samsonovich and B.L. McNaughton},
title = {{Path integration and cognitive mapping in a continuous attractor
neural network model}},
journal = {Journal of Neuroscience},
year = {1997},
volume = {17},
pages = {5900-5920}
}
@book{Sanders85,
title = {Averaging methods in nonlinear dynamics systems},
publisher = {Springer-Verlag},
year = {1985},
author = {J. A. Sanders and F. Verhulst},
address = {New York}
}
@article{Sandfuchs01,
author = {Sandfuchs, O. and Kaiser, F. and Beli{\'c}, MR},
title = {{Self-organization and Fourier selection of optical patterns in a
nonlinear photorefractive feedback system}},
journal = {Physical Review A},
year = {2001},
volume = {64},
pages = {63809},
number = {6},
keywords = {various-artists},
owner = {sprekeler},
publisher = {APS},
timestamp = {2008.04.14}
}
@inproceedings{Sanger94,
author = {Terence D. Sanger},
title = {Optimal Unsupervised Learning Predicts the Internal Representation
of Barn Owl Head Movements},
booktitle = {Neural Information Processing Systems},
year = {1994},
editor = {Jack D. Cowan and Gerald Tesauro and Joshua Alspector},
volume = {6},
pages = {614--621},
address = {San Francisco},
publisher = {Morgan Kaufmann}
}
@article{Sanger03,
author = {T. D. Sanger},
title = {Neural population codes},
journal = {Current Opinion in Neurobiology},
year = {2003},
volume = {13},
pages = {238-249},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Sanger89,
author = {T. D. Sanger},
title = {Optimal unsupervised learning in a single-layer linear feedforward
neural network},
journal = {Neural Networks},
year = {1989},
volume = {2},
pages = {459-473}
}
@article{Sargolini06,
author = {Sargolini, Francesca and Fyhn, Marianne and Hafting, Torkel and McNaughton,
Bruce L and Witter, Menno P and Moser, May-Britt and Moser, Edvard
I},
title = {Conjunctive representation of position, direction, and velocity in
entorhinal cortex},
journal = {Science},
year = {2006},
volume = {312},
pages = {758--762},
number = {5774},
month = {May},
keywords = {hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Sathian97,
author = {K. Sathian and A. Zangaladze},
title = {Tactile learning is task-specific but transfers between fingers},
journal = {Perceptual Psychophysics},
year = {1997},
volume = {59},
pages = {119-128},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Saudargiene03,
author = {A. Saudargiene and B. Porr and F. W{\"o}rg{\"o}tter},
title = {How the shape of pre- and postsynaptic signals can influence STDP:
A biophysical model},
journal = {Neural Computation},
year = {2003},
volume = {16},
pages = {595-626}
}
@article{Save00,
author = {E. Save and L. Nerad and B. Poucet},
title = {Contribution of multiple sensory information to place field stability
in hippocampal place cells},
journal = {Hippocampus},
year = {2000},
volume = {10},
pages = {64--76},
number = {1},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Scarpetta02,
author = {S. Scarpetta and L. Zhaoping and J. Hertz},
title = {Hebbian imprinting and retrieval in oscillatory neural networks},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {2371-2396}
}
@article{Schoelkopf95,
author = {B. Sch\"olkopf and H.A. Mallot},
title = {{View-based cognitive mapping and path planning}},
journal = {Adaptive Behavior},
year = {1995},
volume = {3},
pages = {311--348}
}
@book{Schoelkopf02,
title = {Learning with kernels: support vector machines, regularization, optimization,
and beyond,},
publisher = {MIT Press Cambridge},
year = {2002},
author = {B. Sch\"olkopf and A.J. Smola}
}
@article{Schaefer03,
author = {Schaefer, Andreas T and Larkum, Matthew E and Sakmann, Bert and Roth,
Arnd},
title = {Coincidence detection in pyramidal neurons is tuned by their dendritic
branching pattern.},
journal = {J Neurophysiol},
year = {2003},
volume = {89},
pages = {3143--3154},
number = {6},
abstract = {Neurons display a variety of complex dendritic morphologies even within
the same class. We examined the relationship between dendritic arborization
and the coupling between somatic and dendritic action potential (AP)
initiation sites in layer 5 (L5) neocortical pyramidal neurons. Coupling
was defined as the relative reduction in threshold for initiation
of a dendritic calcium AP due to a coincident back-propagating AP.
Simulations based on reconstructions of biocytin-filled cells showed
that addition of oblique branches of the main apical dendrite in
close proximity to the soma (d < 140 microm) increases the coupling
between the apical and axosomatic AP initiation zones, whereas incorporation
of distal branches decreases coupling. Experimental studies on L5
pyramids in acute brain slices revealed a highly significant (n =
28, r = 0.63, P < 0.0005) correlation: increasing the fraction of
proximal oblique dendrites (d < 140 microm), e.g., from 30 to 60%
resulted on average in an increase of the coupling from approximately
35% to almost 60%. We conclude that variation in dendritic arborization
may be a key determinant of variability in coupling (49 +/- 17%;
range 19-83%; n = 37) and is likely to outweigh the contribution
made by variations in active membrane properties. Thus coincidence
detection of inputs arriving from different cortical layers is strongly
regulated by differences in dendritic arborization.},
address = {Abteilung Zellphysiologie, Max-Planck-Institut fur medizinische Forschung,
D-69120 Heidelberg, Germany. schaefer@mpimf-heidelberg.mpg.de},
au = {Schaefer, AT and Larkum, ME and Sakmann, B and Roth, A},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00046.2003},
cin = {J Neurophysiol. 2003 Jun;89(6):2887-8. PMID: 12783945},
da = {20030604},
date-added = {2008-03-28 22:20:59 +0100},
date-modified = {2008-03-28 22:21:10 +0100},
dcom = {20030801},
dep = {20030226},
doi = {10.1152/jn.00046.2003},
edat = {2003/03/04 04:00},
issn = {0022-3077 (Print)},
jid = {0375404},
jt = {Journal of neurophysiology},
language = {eng},
lr = {20031114},
mh = {*Action Potentials; Animals; Dendrites/*physiology; Electrophysiology;
Membrane Potentials; *Models, Biological; Pyramidal Cells/*physiology;
Rats; Rats, Wistar; Somatosensory Cortex/*physiology},
mhda = {2003/08/02 05:00},
own = {NLM},
owner = {sprekeler},
phst = {2003/02/26 {$[$}aheadofprint{$]$}},
pii = {00046.2003},
pl = {United States},
pmid = {12612010},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print-Electronic},
sb = {IM},
so = {J Neurophysiol. 2003 Jun;89(6):3143-54. Epub 2003 Feb 26. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@incollection{Scheier98,
author = {C. Scheier and R. Pfeifer},
title = {Exploiting Embodiment for Category Learning},
booktitle = {From Animals to Animats 5},
publisher = {MIT-Press},
year = {1998},
editor = {R. Pfeifer, P. Blumberg, J.-A. Meyer and S.W. Wilson},
pages = {32-37}
}
@incollection{Scherf94,
author = {O. Scherf and K. Pawelzik and F. Wolf and T. Geisel},
title = {Unification of Complementary Feature Map Models},
booktitle = {ICANN 94},
publisher = {Springer},
year = {1994},
editor = {M. Marinaro and P. G. Morasso},
pages = {338-341}
}
@mastersthesis{Schiegg94a,
author = {A. Schiegg},
title = {Dynamik des intrazellul\protect{\"{a}}ren {C}alciums bei der {I}nduktion
von {L}ong-{T}erm-{P}otentiation.},
school = {Technische Universit\protect{\"{a}}t M\protect{\"{u}}nchen},
year = {1994},
type = {Diplomarbeit}
}
@article{Schiegg95,
author = {A. Schiegg and W. Gerstner and J.L. van Hemmen},
title = {Intracellular $\protect{C}a^{2+}$ stores can account for the time
course of \protect{LTP} induction: A model of $\protect{C}a^{2+}$
dynamics in dendritic spines.},
journal = {J. Neurophysiol.},
year = {1995},
volume = {74},
pages = {1046-1055}
}
@article{Schillen91,
author = {T. B. Schillen and P. K\protect{\"{o}}nig},
title = {Stimulus--dependent assembly formation of oscillatory responses:
II. Desynchronization.},
journal = {Neural Computation},
year = {1991},
volume = {3},
pages = {167--178}
}
@article{Schiller97,
author = {Schiller, J and Schiller, Y and Stuart, G and Sakmann, B},
title = {Calcium action potentials restricted to distal apical dendrites of
rat neocortical pyramidal neurons.},
journal = {J Physiol},
year = {1997},
volume = {505},
pages = {605--616},
number = {3},
abstract = {1. Simultaneous whole-cell voltage and Ca2+ fluorescence measurements
were made from the distal apical dendrites and the soma of thick
tufted pyramidal neurons in layer 5 of 4-week-old (P28-32) rat neocortex
slices to investigate whether activation of distal synaptic inputs
can initiate regenerative responses in dendrites. 2. Dual whole-cell
voltage recordings from the distal apical trunk and primary tuft
branches (540-940 microns distal to the soma) showed that distal
synaptic stimulation (upper layer 2) evoking a subthreshold depolarization
at the soma could initiate regenerative potentials in distal branches
of the apical tuft which were either graded or all-or-none. These
regenerative potentials did not propagate actively to the soma and
axon. 3. Calcium fluorescence measurements along the apical dendrites
indicated that the regenerative potentials were associated with a
transient increase in the concentration of intracellular free calcium
([Ca2+]i) restricted to distal dendrites. 4. Cadmium added to the
bath solution blocked both the all-or-more dendritic regenerative
potentials and local dendritic [Ca2+]i transients evoked by distal
dendritic current injection. Thus, the regenerative potentials in
distal dendrites represent local Ca2+ action potentials. 5. Initiation
of distal Ca2+ action potentials by a synaptic stimulus required
coactivation of AMPA- and NMDA-type glutamate receptor channels.
6. It is concluded that in neocortical layer 5 pyramidal neurons
of P28-32 animals glutamatergic synaptic inputs to the distal apical
dendrites can be amplified via local Ca2+ action potentials which
do not reach threshold for axonal AP initiation. As amplification
of distal excitatory synaptic input is associated with a localized
increase in [Ca2+]i these Ca2+ action potentials could control the
synaptic efficacy of the distal cortico-cortical inputs to layer
5 pyramidal neurons.},
address = {Abteilung Zellphysiologie, Max-Planck-Institut fur medizinische Forschung,
Heidelberg, Germany.},
au = {Schiller, J and Schiller, Y and Stuart, G and Sakmann, B},
da = {19980312},
date-added = {2008-03-28 23:37:21 +0100},
date-modified = {2008-03-28 23:38:49 +0100},
dcom = {19980312},
edat = {1998/02/11},
issn = {0022-3751 (Print)},
jid = {0266262},
jt = {The Journal of physiology},
language = {eng},
lr = {20061115},
mh = {Action Potentials/physiology; Animals; Axons/physiology; Calcium/*physiology;
Dendrites/*physiology; Electric Conductivity; Excitatory Amino Acid
Agonists/pharmacology; Excitatory Postsynaptic Potentials/physiology;
Fluorescence; Isoxazoles/pharmacology; Neocortex/*physiology/ultrastructure;
Patch-Clamp Techniques; Propionates/pharmacology; Pyramidal Cells/*physiology/ultrastructure;
Rats; Receptors, N-Methyl-D-Aspartate/physiology},
mhda = {1998/02/11 00:01},
own = {NLM},
owner = {sprekeler},
pl = {ENGLAND},
pmid = {9457639},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
rn = {0 (Excitatory Amino Acid Agonists); 0 (Isoxazoles); 0 (Propionates);
0 (Receptors, N-Methyl-D-Aspartate); 140158-50-5 (alpha-amino-3-hydroxy-5-tert-butyl-4-isoxazolepropionate);
7440-70-2 (Calcium)},
sb = {IM},
so = {J Physiol. 1997 Dec 15;505 ( Pt 3):605-16. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Schimming01,
author = {T. Schimming and M.Hasler},
title = {Optimal Detection of Differential Chaos Shift Keying},
journal = {IEEE Trans. Circuits and Systems I.},
year = {2001},
pages = {to appear},
annote = {Hasler - paper cited in grant proposal}
}
@article{Schindler04,
author = {M. Schindler and P. Talkner and P. Hanggi},
title = {Firing time statistics for driven neuron models: Analytic expressions
versus numerics},
journal = {Phys. Rev. Letters},
year = {2004},
volume = {93},
pages = {48102}
}
@incollection{Schmidhuber:05gmai,
author = {J. Schmidhuber},
title = {G\"{o}del machines: fully self-referential optimal universal problem
solvers},
booktitle = {Artificial General Intelligence},
publisher = {Springer Verlag},
year = {2006},
editor = {B. Goertzel and C. Pennachin},
pages = {199-226},
note = {Variant available as arXiv:cs.LO/0309048}
}
@incollection{Schmidhuber02,
author = {J. Schmidhuber},
title = {The {Speed Prior:} A New Simplicity Measure Yielding Near-Optimal
Computable Predictions},
booktitle = {Proceedings of the 15th Annual Conference on Computational Learning
Theory (COLT 2002)},
publisher = {Springer},
year = {2002},
editor = {J. Kivinen and R. H. Sloan},
series = {Lecture Notes in Artificial Intelligence},
pages = {216--228},
address = {Sydney, Australia}
}
@inproceedings{Schmidhuber07a,
author = {J. Schmidhuber},
title = {Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective
Attention, Curiosity \& Creativity},
booktitle = {Proc. 18th Intl. Conf. on Algorithmic Learning Theory (ALT 2007),
LNAI 4754},
year = {2007},
pages = {32-33},
publisher = {Springer},
note = {Joint invited lecture for {\em ALT 2007 and DS 2007}, Sendai, Japan,
2007},
editors = {M. Hutter, R. A. Servedio, E. Takimoto}
}
@misc{Schmidhuber:04subgoals,
author = {J. Schmidhuber},
title = {{Overview of work on hierarchical learning and automatic subgoal
generation, with a dozen publications}},
year = {2004},
note = {http://www.idsia.ch/\~{ }juergen/subgoals.html}
}
@inproceedings{Schmidhuber91,
author = {J. Schmidhuber},
title = {Curious Model-Building Control Systems},
booktitle = {Proceedings of the International Joint Conference on Neural Networks,
Singapore},
year = {1991},
volume = {2},
pages = {1458-1463},
publisher = {IEEE press}
}
@article{Schmidhuber96,
author = {J. Schmidhuber and M. Eldracher and B. Foltin },
title = {Semilinear predictability minimization produces well-known feature
detectors},
journal = {Neural Computation},
year = {1996},
volume = {8},
pages = {773--786},
number = {4},
citeulike-article-id = {2381574},
keywords = {juergen},
priority = {2}
}
@book{Schmidhuber08,
title = {Sequence Learning with Artificial Recurrent Neural Networks},
publisher = {Invited by Cambridge University Press},
year = {2007},
author = {J. Schmidhuber and A. Graves and F. Gomez and S. Fernandez and S.
Hochreiter},
note = {In preparation (aiming to become {\em the} definitive textbook on
{RNN})}
}
@article{Schneidman98,
author = {E. Schneidman and B. Freedman and I. Segev},
title = {Ion channel stochasticity may be critical in determining the reliability
and precision of spike timing},
journal = {Neural Computation},
year = {1998},
volume = {10},
pages = {1679-1703}
}
@article{Schneidman06,
author = {E. Schneidman and M.J.Berry and R. Segav and W. Bialek},
title = {Weak pairwise correlations imply strongly correlated network states
in a neural population },
journal = {Nature},
year = {2006},
volume = {440},
pages = {1007-1012}
}
@article{Schoups98,
author = {A. Schoups and R. Vogels and G. A. Orban},
title = {Effects of perceptual learning in orientation discrimination on orientation
coding in V1},
journal = {Invest Ophtalmol Vis Sci Suppl},
year = {1998},
volume = {39},
pages = {684},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Schoups95,
author = {A. Schoups and R. Vogels and G. A. Orban},
title = {Human perceptual learning in identifying the oblique orientation:
retinotopy, orientation specificity, and monocularity},
journal = {The Journal of Physiology},
year = {1995},
volume = {483},
pages = {797-810},
number = {3},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Schoups01,
author = {A. Schoups and R. Vogels and N. Quian and G. A. Orban},
title = {Practising orientation identification improves orientation coding
in V1 neurons},
journal = {Nature},
year = {2001},
volume = {412},
pages = {549-553},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Schroedinger15,
author = {E. Schr\protect{\"o}dinger},
title = {{Z}ur {T}heorie der {F}all- und {S}teigversuche and {T}eilchen mit
\protect{B}rownscher {B}ewegung},
journal = {Physikalische Zeitschrift},
year = {1915},
volume = {16},
pages = {289-295}
}
@article{Schraudolph99,
author = {N. N. Schraudolph and M. Eldracher and J. Schmidhuber},
title = {Processing Images by Semi-Linear Predictability Minimization},
journal = {Network: Computation in Neural Systems},
year = {1999},
volume = {10},
pages = {133--169},
number = {2}
}
@article{Schultz01a,
author = {S.R. Schultz and S. Panzeri},
title = {Temporal correlations and neural spike train entropy},
journal = {Phys. Rev. Letters},
year = {2001},
volume = {xx},
pages = {xx}
}
@incollection{Schultz97b,
author = {W. Schultz},
title = {Reward responses of dopamine neurons: A biological reinforcement
signal},
booktitle = {Artificial Neural Networks - ICANN'97},
publisher = {Springer},
year = {1997},
editor = {W. Gerstner and A. Germond and M. Hasler and J.-D. Nicoud},
series = {Lecture Notes in Computer Science 1327},
pages = {3-12}
}
@article{Schultz07,
author = {Schultz, Wolfram},
title = {Behavioral dopamine signals},
journal = {Trends in Neurosciences},
year = {2007},
volume = {30},
pages = {203--210},
number = {5},
month = may,
booktitle = {Fifty years of dopamine research},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.sciencedirect.com/science/article/B6T0V-4ND7091-2/2/171b8fe8539fe2e6f47f085bed15546d}
}
@article{Schultz97c,
author = {W. Schultz},
title = {Dopamine neurons and their role in reward mechanisms},
journal = {Curr. Op. Neurobiol.},
year = {1997},
volume = {7},
pages = {191-197}
}
@article{Schultz93,
author = {W. Schultz and P. Apicella and E. Scarnati and T. Ljungberg},
title = {Responses of monkey dopamine neurons to reward and conditioned stimuli
during successive steps of learning a delayed response task},
journal = {J. Neuroscience},
year = {1993},
volume = {13:900-913}
}
@article{Schultz92,
author = {W. Schultz and P. Apicella and E. Scarnati and T. Ljungberg},
title = {Neuronal activity in monkey ventral striatum related to the expectation
of reward},
journal = {J. Neuroscience},
year = {1992},
volume = {12},
pages = {4595--4610}
}
@article{Schultz97,
author = {W. Schultz and P. Dayan and R.R. Montague},
title = {A neural substrate for prediction and reward},
journal = {Science},
year = {1997},
volume = {275},
pages = {1593-1599}
}
@article{Schultz00,
author = {Wolfram Schultz and Anthony Dickinson},
title = {Neuronal Coding of Prediction Errors},
journal = {Annual Reviews of Neuroscience},
year = {2000},
volume = {23},
pages = {472--500},
keywords = {Plasticity, Various-Artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Schultz90,
author = {W. Schultz and R. Romo},
title = {Dopamine neurons of the monkey midbrain: Contingencies of responses
to stimuli eliciting immediate behavioral reaction},
journal = {J. Neurophysiol.},
year = {1990},
volume = {63},
pages = {607-624}
}
@article{Schulz06,
author = {Schulz, David J and Goaillard, Jean-Marc and Marder, Eve},
title = {Variable channel expression in identified single and electrically
coupled neurons in different animals.},
journal = {Nat Neurosci},
year = {2006},
volume = {9},
pages = {356--362},
number = {3},
abstract = {It is often assumed that all neurons of the same cell type have identical
intrinsic properties, both within an animal and between animals.
We exploited the large size and small number of unambiguously identifiable
neurons in the crab stomatogastric ganglion to test this assumption
at the level of channel mRNA expression and membrane currents (measured
in voltage-clamp experiments). In lateral pyloric (LP) neurons, we
saw strong correlations between measured current and the abundance
of Shal and BK-KCa mRNAs (encoding the Shal-family voltage-gated
potassium channel and large-conductance calcium-activated potassium
channel, respectively). We also saw two- to fourfold interanimal
variability for three potassium currents and their mRNA expression.
Measurements of channel expression in the two electrically coupled
pyloric dilator (PD) neurons showed significant interanimal variability,
but copy numbers for IH (encoding the hyperpolarization-activated,
inward-current channel) and Shal mRNA in the two PD neurons from
the same crab were similar, suggesting that the regulation of some
currents may be shared in electrically coupled neurons.},
address = {Volen Center and Biology Department, Brandeis University, Waltham,
Massachusetts 02454, USA. SchulzD@missouri.edu},
au = {Schulz, DJ and Goaillard, JM and Marder, E},
bdsk-url-1 = {http://dx.doi.org/10.1038/nn1639},
da = {20060224},
date-added = {2007-12-12 20:08:59 +0100},
date-modified = {2007-12-12 20:13:34 +0100},
dcom = {20060414},
dep = {20060129},
doi = {10.1038/nn1639},
edat = {2006/01/31 09:00},
gr = {MH46742/MH/United States NIMH; MH70292/MH/United States NIMH; NS17813/NS/United
States NINDS},
issn = {1097-6256 (Print)},
jid = {9809671},
jt = {Nature neuroscience},
keywords = {Action Potentials/genetics; Animals; Biological Clocks/genetics; Brachyura/cytology/*physiology;
Cell Communication/genetics; Ganglia, Invertebrate/cytology/*metabolism;
Gap Junctions/genetics; Gene Expression Regulation/physiology; Membrane
Potentials/genetics; Molecular Sequence Data; Nervous System/cytology/*metabolism;
Neural Conduction/genetics; Neural Inhibition/genetics; Neurons/cytology/*metabolism;
Patch-Clamp Techniques; Potassium/metabolism; RNA, Messenger/metabolism;
Shal Potassium Channels/genetics/*metabolism},
language = {eng},
lr = {20071114},
mhda = {2006/04/15 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2005/08/29 {$[$}received{$]$}; 2005/12/23 {$[$}accepted{$]$}; 2006/01/29
{$[$}aheadofprint{$]$}},
pii = {nn1639},
pl = {United States},
pmid = {16444270},
pst = {ppublish},
pt = {Journal Article; Research Support, N.I.H., Extramural; Research Support,
Non-U.S. Gov't},
pubm = {Print-Electronic},
rn = {0 (RNA, Messenger); 0 (Shal Potassium Channels); 7440-09-7 (Potassium)},
sb = {IM},
si = {GENBANK/DQ103254; GENBANK/DQ103255; GENBANK/DQ103256; GENBANK/DQ103257},
so = {Nat Neurosci. 2006 Mar;9(3):356-62. Epub 2006 Jan 29. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@book{Schuster92,
title = {Applications of Neuronal Networks},
publisher = {VCH Weinheim},
year = {1992},
author = {Schuster, H. G.},
series = {Nonlinear Systems~3}
}
@book{Schuster91,
title = {Nonlinear Dynamics and Neuronal Networks},
publisher = {VCH Weinheim},
year = {1991},
author = {Schuster, H. G.},
series = {Nonlinear Systems~2}
}
@article{Schuster90a,
author = {H. G. Schuster and P. Wagner},
title = {A model for neuronal oscillations in the visual cortex 1. Mean-field
theory and derivation of the phase equations.},
journal = {Biol. Cybern.},
year = {1990},
volume = {64},
pages = {77--82}
}
@article{Schuster90b,
author = {H. G. Schuster and P. Wagner},
title = {A model for neuronal oscillations in the visual cortex 2. Phase description
and feature dependent synchronization.},
journal = {Biol. Cybern.},
year = {1990},
volume = {64},
pages = {83--85}
}
@article{DeSchutter95,
author = {Erik De Schutter},
title = {Cerebellar long term depression may normalize excitation of purkinje
cells: a hypothesis},
journal = {Trends in Neurosciences},
year = {1995},
volume = {18},
pages = {291-295}
}
@article{Schwabe05,
author = {L. Schwabe and K. Obermayer},
title = {Adaptivity of tuning functions in a generic recurrent network model
of a cortical hypercolumn},
journal = {The Journal of Neuroscience},
year = {2005},
volume = {25},
pages = {3323-3332},
number = {13},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Schwartz77,
author = {E. Schwartz},
title = {Spatial mapping in the primate sensory projection: {A}nalytic structure
and relevance to perception.},
journal = {Biol. Cybern.},
year = {1977},
volume = {25},
pages = {181--194}
}
@book{Schwartz90,
title = {Computational Neuroscience},
publisher = {MIT Press},
year = {1990},
author = {Eric L. Schwartz},
address = {Cambridge}
}
@article{Schwartz07,
author = {Schwartz, G. and Harris, R. and Shrom, D. and II, M.J.B.},
title = {{Detection and prediction of periodic patterns by the retina}},
journal = {Nature Neuroscience},
year = {2007},
volume = {10},
pages = {552--554},
keywords = {vision,vision-physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Schwartz06,
author = {Schwartz, O. and Sejnowski, T. J. and Dayan, P.},
title = {Soft mixer assignment in a hierarchical generative model of natural
scene statistics},
journal = {Neural Computation},
year = {2006},
volume = {18},
pages = {2680-2718},
number = {11},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Schwartz01,
author = {O. Schwartz and E. P. Simoncelli},
title = {Natural signal statistics and sensory gain control},
journal = {Nature Neuroscience},
year = {2001},
volume = {4},
pages = {819-825}
}
@book{Scott92,
title = {Multivariate Density Estimation},
publisher = {Wiley},
year = {1992},
author = {D. W. Scott},
address = {New York}
}
@article{Seelen68,
author = {W. von Seelen},
title = {Informationverarbeitung in homogenen Netzen von Neuronen},
journal = {Kybernetik},
year = {1968},
volume = {5},
pages = {133-148}
}
@article{Segev92,
author = {Idan Segev},
title = {Single neurone models: oversimple, complex and reduced},
journal = {Trends in Neurosciences},
year = {1992},
volume = {15},
pages = {414--421},
number = {11}
}
@inproceedings{Segev89,
author = {Idan Segev and James W. Fleshman and Robert E. Burke},
title = {Compartmental model of complex neurons},
booktitle = {Methods in Neuronal modeling},
year = {1989},
editor = {C. Koch and I. Segev},
pages = {63-96},
address = {Cambridge},
publisher = {MIT Press}
}
@article{Segundo63,
author = {J. P. Segundo and G. P. Moore and L. J. Stensaas and T. H. Bullock},
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impulses},
journal = {J. Exp. Biol.},
year = {1963},
volume = {40},
pages = {643-667}
}
@article{Seitz05,
author = {A. R. Seitz and J. E. Nanez and S. R. Holloway and S. Koyama and
T. Watanabe},
title = {Seeing what is not there shows the cost of perceptual learning},
journal = {PNAS},
year = {2005},
volume = {102},
pages = {9080-9085},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Sejnowski77,
author = {T. Sejnowski},
title = {Storing covariance with nonlinearly interacting neurons},
journal = {J. Mathematical Biology},
year = {1977},
volume = {4},
pages = {303-321}
}
@article{Sejnowski76,
author = {T. Sejnowski},
title = {On the stochastic dynamics of neuronal interaction},
journal = {Biological Cybernetics},
year = {1976},
volume = {22},
pages = {203-211}
}
@article{Sejnowski06,
author = {Sejnowski, T.J. and Paulsen, O.},
title = {{Network Oscillations: Emerging Computational Principles}},
journal = {Journal of Neuroscience},
year = {2006},
volume = {26},
pages = {1673--1676},
number = {6},
keywords = {various-artists},
owner = {sprekeler},
publisher = {Soc Neuroscience},
timestamp = {2008.04.14}
}
@article{Sejnowski99,
author = {T. J. Sejnowski},
title = {The book of Hebb},
journal = {Neuron},
year = {1999},
volume = {24},
pages = {773-776}
}
@article{Sejnowski95a,
author = {T. J. Sejnowski},
title = {Pattern recognition. Time for a new neural code?},
journal = {Nature},
year = {1995},
volume = {376},
pages = {21--22},
number = {6535},
month = {Jul},
doi = {10.1038/376021a0},
keywords = {Neuronal-Processing},
owner = {sprekeler},
pmid = {7596425},
timestamp = {2008.05.08},
url = {http://dx.doi.org/10.1038/376021a0}
}
@incollection{Sejnowski89,
author = {T. J. Sejnowski and G. Tesauro},
title = {The {H}ebb rule for synaptic plasticity: algorithms and implementations},
booktitle = {Neural Models of Plasticity},
publisher = {Academic Press},
year = {1989},
editor = {J. H. Byrne and W. O. Berry},
chapter = {6},
pages = {94-103}
}
@article{Senn02b,
author = {W. Senn},
title = {Beyond spike timing: the role of non-linear plasticity and unreliable
synapses},
journal = {Biological Cybernetics},
year = {2002},
volume = {87},
pages = {344-355}
}
@article{Senn02,
author = {W. Senn and M. Schneider and B. Ruf},
title = {Activity-dependent development of axonal and dendritic delays or,
why synaptic transmission should be unreliable},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {583-619}
}
@incollection{Senn97,
author = {W. Senn and M. Tsodyks and H. Markram},
title = {An algorithm for synaptic modification based on exact timing of pre-and
postsynaptic action potentials},
booktitle = {Artificial Neural Networks - ICANN'97},
publisher = {Springer},
year = {1997},
editor = {W. Gerstner and A. Germond and M. Hasler and J.-D. Nicoud},
series = {Lecture Notes in Computer Science 1327},
pages = {121-126}
}
@article{Senn00,
author = {W. Senn and M. Tsodyks and H. Markram},
title = {An algorithm for modifying neurotransmitter release probability based
on pre- and postsynaptic spike timing},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {35-67}
}
@article{Senn01,
author = {W. Senn and M. Tsodyks and H. Markram},
title = {An algorithm for modifying neurotransmitter release probability based
on pre- and postsynaptic spike timing},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {35-67}
}
@article{Senn96,
author = {W. Senn and K. Wyler and J. Streit and M. Larkum and H.-R. L\protect{\"u}scher
and H, Mey and L. M\protect{\"u}ller and D. Steinhauser and K. Vogt
and T. Wannier},
title = {Dynamics of random neural network with synaptic depression},
journal = {Neural Networks},
year = {1996},
volume = {9},
pages = {575-588}
}
@article{Seri`es04,
author = {P. Seri\`es and P. Latham and A. Pouget},
title = {Tuning curve sharpening for orientation selectivity: coding efficiency
and the impact of correlations},
journal = {Nature Neuroscience},
year = {2004},
volume = {7},
pages = {1129-1135},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Seung03,
author = {H.S. Seung},
title = {Learning in spiking neural networks by reinforcement of stochastic
synaptic transmission},
journal = {Neuron},
year = {2003},
volume = {40},
pages = {1063-1073}
}
@article{Seung03a,
author = {Seung, H.S.},
title = {{Learning in Spiking Neural Networks by Reinforcement of Stochastic
Synaptic Transmission}},
journal = {Neuron},
year = {2003},
volume = {40},
pages = {1063--1073},
number = {6},
owner = {sprekeler},
publisher = {Elsevier},
timestamp = {2008.04.14}
}
@article{Seung93,
author = {H. S. Seung and H. Sompolinski},
title = {Simple models for reading neuronal population codes},
journal = {PNAS},
year = {1993},
volume = {90},
pages = {10749-10753},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Seymour04,
author = {B. Seymour and J.P. \protect{O'Doherty} and P. Dayan and M. Koltzenburg
and A.K. Jones and R. J. Dolan and K.J. Friston and R. Frackowiak},
title = { Temporal difference models describe higher order learning in humans.},
journal = {Nature},
year = {2004},
volume = {429},
pages = {664-667}
}
@article{Shadlen98,
author = {M.N. Shadlen and W. T. Newsome},
title = {The variable discharge of cortical neurons: implications for connectivity,
computation, and information coding},
journal = {J. of Neuroscience},
year = {1998},
volume = {18},
pages = {3870-3896}
}
@article{Shadlen99,
author = {Shadlen, M N and Movshon, J A},
title = {Synchrony unbound: a critical evaluation of the temporal binding
hypothesis.},
journal = {Neuron},
year = {1999},
volume = {24},
pages = {67--77},
number = {1},
address = {Department of Physiology and Biophysics, University of Washington,
Seattle 98195, USA.},
au = {Shadlen, MN and Movshon, JA},
da = {20000225},
date-added = {2008-03-29 12:53:29 +0100},
date-modified = {2008-03-29 12:53:37 +0100},
dcom = {20000225},
edat = {2000/02/17 09:00},
gr = {EY02017/EY/United States NEI; EY11378/EY/United States NEI; RR00166/RR/United
States NCRR},
issn = {0896-6273 (Print)},
jid = {8809320},
jt = {Neuron},
language = {eng},
lr = {20071114},
mh = {Animals; Cerebral Cortex/physiology; Humans; *Models, Biological;
Neurons/*physiology; Time Factors; Visual Cortex/physiology; Visual
Perception/*physiology},
mhda = {2000/03/04 09:00},
own = {NLM},
owner = {sprekeler},
pii = {S0896-6273(00)80822-3},
pl = {UNITED STATES},
pmid = {10677027},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.; Review},
pubm = {Print},
rf = {715},
sb = {IM; S},
so = {Neuron. 1999 Sep;24(1):67-77, 111-25. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Shadlen98a,
author = {Shadlen, M N and Newsome, W T},
title = {The variable discharge of cortical neurons: implications for connectivity,
computation, and information coding.},
journal = {J Neurosci},
year = {1998},
volume = {18},
pages = {3870--3896},
number = {10},
abstract = {Cortical neurons exhibit tremendous variability in the number and
temporal distribution of spikes in their discharge patterns. Furthermore,
this variability appears to be conserved over large regions of the
cerebral cortex, suggesting that it is neither reduced nor expanded
from stage to stage within a processing pathway. To investigate the
principles underlying such statistical homogeneity, we have analyzed
a model of synaptic integration incorporating a highly simplified
integrate and fire mechanism with decay. We analyzed a "high-input
regime" in which neurons receive hundreds of excitatory synaptic
inputs during each interspike interval. To produce a graded response
in this regime, the neuron must balance excitation with inhibition.
We find that a simple integrate and fire mechanism with balanced
excitation and inhibition produces a highly variable interspike interval,
consistent with experimental data. Detailed information about the
temporal pattern of synaptic inputs cannot be recovered from the
pattern of output spikes, and we infer that cortical neurons are
unlikely to transmit information in the temporal pattern of spike
discharge. Rather, we suggest that quantities are represented as
rate codes in ensembles of 50-100 neurons. These column-like ensembles
tolerate large fractions of common synaptic input and yet covary
only weakly in their spike discharge. We find that an ensemble of
100 neurons provides a reliable estimate of rate in just one interspike
interval (10-50 msec). Finally, we derived an expression for the
variance of the neural spike count that leads to a stable propagation
of signal and noise in networks of neurons-that is, conditions that
do not impose an accumulation or diminution of noise. The solution
implies that single neurons perform simple algebra resembling averaging,
and that more sophisticated computations arise by virtue of the anatomical
convergence of novel combinations of inputs to the cortical column
from external sources.},
address = {Department of Physiology and Biophysics and Regional Primate Research
Center, University of Washington, Seattle, Washington 98195-7290,
USA.},
au = {Shadlen, MN and Newsome, WT},
da = {19980601},
date-added = {2008-03-27 14:11:16 +0100},
date-modified = {2008-03-27 14:13:37 +0100},
dcom = {19980601},
edat = {1998/06/06},
gr = {EY05603/EY/United States NEI; EY11378/EY/United States NEI; RR00166/RR/United
States NCRR},
issn = {0270-6474 (Print)},
jid = {8102140},
jt = {The Journal of neuroscience : the official journal of the Society
for Neuroscience},
language = {eng},
lr = {20071114},
mh = {Action Potentials/physiology; Animals; Cerebral Cortex/*cytology;
Data Interpretation, Statistical; Electrophysiology; Higher Nervous
Activity/*physiology; *Information Theory; Interneurons/*physiology;
Macaca mulatta; Mental Processes/physiology; *Models, Neurological;
Neural Pathways/physiology; Reaction Time/physiology},
mhda = {1998/06/06 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {9570816},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't; Research Support,
U.S. Gov't, P.H.S.},
pubm = {Print},
sb = {IM},
so = {J Neurosci. 1998 May 15;18(10):3870-96. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Shadlen94,
author = {M. N. Shadlen and W. T. Newsome},
title = {Noise, neural codes and cortical organization},
journal = {Current Opininon in Neurobiology},
year = {1994},
volume = {4},
pages = {569-579}
}
@article{Shamma89,
author = {Shihab A. Shamma},
title = {Stereausis: Binaural Processing without neuronal delays},
journal = {J. Acoust. Soc. Am.},
year = {1989},
volume = {86},
pages = {989--1006},
number = {3}
}
@article{Sharp01,
author = {E. P. Sharp and H. T. Blair and J. Cho},
title = {The anatomical and computational basis of the rat head-direction
cell signal},
journal = {Trends in Neurosciences},
year = {2001},
volume = {24},
pages = {289--294},
number = {5},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Sharp96,
author = {P.E. Sharp},
title = {Multiple Spatial/Behavioral Correlates for Cells in the Rat Postsubiculum:
Multiple Regression Analysis and Comparison to Other Hippocampal
Areas},
journal = {Cerebral Cortex},
year = {1996},
volume = {6},
pages = {238--259},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Sharp94,
author = {P.E. Sharp and C. Green},
title = {Spatial correlates of firing patterns of single cells in the subiculum
of the freely moving rat},
journal = {Journal of Neuroscience},
year = {1994},
volume = {14},
pages = {2339--2356},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Sharp90,
author = {P. Sharp and J. Kubie and R. Muller},
title = {Firing properties of hippocampal neurons in a visually symmetrical
environment: Contributions of multiple sensory cues and mnemonic
properties},
journal = {Journal of Neuroscience},
year = {1990},
volume = {10},
pages = {3093--3105},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@incollection{Sharp05,
author = {P. E. Sharp},
title = {Regional Distribution and Variation in the Firing Properties of Head
Direction Cells},
booktitle = {Head direction cells and the neural mechanisms of spatial orientation},
publisher = {MIT Press},
year = {2005},
editor = {S. I. Wiener and S. Taube},
chapter = {1},
pages = {3--15},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Sharp91,
author = {Sharp, P. E .},
title = {{Computer simulation of hippocampal place cells}},
journal = {Psychobiology},
year = {1991},
volume = {19},
pages = {103--115},
number = {2},
keywords = {hippocampus},
owner = {sprekeler},
publisher = {Psychonomic Society},
timestamp = {2008.04.14}
}
@phdthesis{Shaw06,
author = {Shaw, J.},
title = {Unifying Perception and Curiosity},
school = {University of Rochester},
year = {2006},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@techreport{Shaw03,
author = {Jonathan Shaw},
title = {Predictive Coding with Temporal Invariance},
institution = {University of Rochester},
year = {2003},
keywords = {slowness},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://hdl.handle.net/1802/1318}
}
@article{Shawn03,
author = {C. Shawn and D. Bavelier},
title = {Action video game modifies visual selective attention},
journal = {Nature},
year = {2003},
volume = {423},
pages = {534-537},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Shema07,
author = {Reut Shema and Todd Charlton Sacktor and Yadin Dudai},
title = {Rapid erasure of long-term memory associations in the cortex by an
inhibitor of PKM zeta.},
journal = {Science},
year = {2007},
volume = {317},
pages = {951--953},
number = {5840},
month = {Aug},
abstract = {Little is known about the neuronal mechanisms that subserve long-term
memory persistence in the brain. The components of the remodeled
synaptic machinery, and how they sustain the new synaptic or cellwide
configuration over time, are yet to be elucidated. In the rat cortex,
long-term associative memories vanished rapidly after local application
of an inhibitor of the protein kinase C isoform, protein kinase M
zeta (PKMzeta). The effect was observed for at least several weeks
after encoding and may be irreversible. In the neocortex, which is
assumed to be the repository of multiple types of long-term memory,
persistence of memory is thus dependent on ongoing activity of a
protein kinase long after that memory is considered to have consolidated
into a long-term stable form.},
doi = {10.1126/science.1144334},
keywords = {Animals; Conditioning (Psychology); Enzyme Inhibitors; Hippocampus;
Male; Memory; Neocortex; Oligopeptides; Protein Kinase C; Rats; Rats,
Wistar; Taste; Time Factors},
owner = {sprekeler},
pii = {317/5840/951},
pmid = {17702943},
timestamp = {2008.06.03},
url = {http://dx.doi.org/10.1126/science.1144334}
}
@book{Shepherd90,
title = {The synaptic organization of the brain},
publisher = {Oxford University Press},
year = {1990},
author = {Gordon M. Shepherd},
address = {Oxford}
}
@book{Shepherd88,
title = {Neurobiology},
publisher = {Oxford University Press},
year = {1988},
author = {Gordon M. Shepherd},
address = {Oxford},
edition = {2nd}
}
@article{Shepherd07,
author = {Jason D Shepherd and Richard L Huganir},
title = {The cell biology of synaptic plasticity: AMPA receptor trafficking.},
journal = {Annu Rev Cell Dev Biol},
year = {2007},
volume = {23},
pages = {613--643},
abstract = {The cellular processes that govern neuronal function are highly complex,
with many basic cell biological pathways uniquely adapted to perform
the elaborate information processing achieved by the brain. This
is particularly evident in the trafficking and regulation of membrane
proteins to and from synapses, which can be a long distance away
from the cell body and number in the thousands. The regulation of
neurotransmitter receptors, such as the AMPA-type glutamate receptors
(AMPARs), the major excitatory neurotransmitter receptors in the
brain, is a crucial mechanism for the modulation of synaptic transmission.
The levels of AMPARs at synapses are very dynamic, and it is these
plastic changes in synaptic function that are thought to underlie
information storage in the brain. Thus, understanding the cellular
machinery that controls AMPAR trafficking will be critical for understanding
the cellular basis of behavior as well as many neurological diseases.
Here we describe the life cycle of AMPARs, from their biogenesis,
through their journey to the synapse, and ultimately through their
demise, and discuss how the modulation of this process is essential
for brain function.},
doi = {10.1146/annurev.cellbio.23.090506.123516},
keywords = {Plasticity},
owner = {sprekeler},
pmid = {17506699},
timestamp = {2008.04.17},
url = {http://dx.doi.org/10.1146/annurev.cellbio.23.090506.123516}
}
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title = {{The Integrative Action of the Nervous System}},
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timestamp = {2008.04.14}
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@inproceedings{Sheynikhovich05,
author = {D. Sheynikhovich and R. Chavarriaga and T. Str\"osslin and W. Gerstner},
title = {Spatial Representation and Navigation in a Bio-inspired Robot },
booktitle = { Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems,
Cognitive Robotics, and Neuroscience},
year = {2005},
editor = {Stefan Wermter, Günther Palm, Mark Elshaw},
pages = {245-264}
}
@other{Sheynikhovich05a,
abstract = {A biologically inspired computational model of rodent repre-sentation–based
(locale) navigation is presented. The model combines visual input
in the form of realistic two dimensional grey-scale images and odometer
signals to drive the firing of simulated place and head direction
cells via Hebbian synapses. The space representation is built incrementally
and on-line without any prior information about the environment and
consists of a large population of location-sensitive units (place
cells) with overlapping receptive fields. Goal navigation is performed
using reinforcement learning in continuous state and action spaces,
where the state space is represented by population activity of the
place cells. The model is able to reproduce a number of behavioral
and neuro-physiological data on rodents. Performance of the model
was tested on both simulated and real mobile Khepera robots in a
set of behavioral tasks and is comparable to the performance of animals
in similar tasks.},
author = {Sheynikhovich, Denis and Chavarriaga, Ricardo and Strösslin, Thomas
and Gerstner, Wulfram},
journal = {Biomimetic Neural Learning for Intelligent Robots},
owner = {sprekeler},
pages = {245--264},
timestamp = {2008.04.14},
title = {Spatial Representation and Navigation in a Bio-inspired Robot},
url = {http://dx.doi.org/10.1007/11521082_15},
year = {2005}
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abstract = {Cortical neurons of behaving animals generate irregular spike sequences.
Recently, there has been a heated discussion about the origin of
this irregularity. Softky and Koch (1993) pointed out the inability
of standard single-neuron models to reproduce the irregularity of
the observed spike sequences when the model parameters are chosen
within a certain range that they consider to be plausible. Shadlen
and Newsome (1994), on the other hand, demonstrated that a standard
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from the monkey prefrontal cortex to calculate the higher-order statistics
of the interspike intervals. Consistency of the data with the model
is examined on the basis of the coefficient of variation and the
skewness coefficient, which are, respectively, a measure of the spiking
irregularity and a measure of the asymmetry of the interval distribution.
It is found that the biological data are not consistent with the
model if the model time constant assumes a value within a certain
range believed to cover all reasonable values. This fact suggests
that the leaky integrate-and-fire model with the assumption of uncorrelated
inputs is not adequate to account for the spiking in at least some
cortical neurons.},
address = {Department of Physics, Graduate School of Sciences, Kyoto University,
Sakyo-ku, Kyoto, 606-8502 Japan. shino@ton.scphys.kyoto-u.ac.jp},
au = {Shinomoto, S and Sakai, Y and Funahashi, S},
da = {19990615},
date-added = {2008-03-28 11:58:48 +0100},
date-modified = {2008-03-28 11:58:49 +0100},
dcom = {19990615},
edat = {1999/05/05},
issn = {0899-7667 (Print)},
jid = {9426182},
jt = {Neural computation},
language = {eng},
lr = {20061115},
mh = {Action Potentials/physiology; Animals; *Models, Statistical; Neurons/*physiology;
Prefrontal Cortex/*physiology; Reproducibility of Results},
mhda = {1999/05/05 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {10226190},
pst = {ppublish},
pt = {Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {Neural Comput. 1999 May 15;11(4):935-51. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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keywords = {Vision, Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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keywords = {plasticity},
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publisher = {National Acad Sciences},
timestamp = {2008.04.14}
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type = {Ph.D. thesis},
number = {AI95-237},
address = {Austin, TX},
owner = {sprekeler},
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timestamp = {2008.04.14}
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keywords = {Plasticity},
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timestamp = {2008.04.14}
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abstract = {1. Initiation and propagation of action potentials evoked by extracellular
synaptic stimulation was studied using simultaneous dual and triple
patch pipette recordings from different locations on neocortical
layer 5 pyramidal neurons in brain slices from 4-week-old rats (P26-30)
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regenerative potentials prior to somatic action potentials following
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manner which was frequency dependent. The half-width of back propagating
action potentials increased and their maximum rate of rise decreased
with distance from the soma, with the peak of these action potentials
propagating with a conduction velocity of approximately 0.5 m s-1.
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was associated with dendritic calcium electrogenesis, which was particularly
prominent during bursts of somatic action potentials. 5. When dendritic
regenerative potentials were evoked prior to somatic action potentials,
the more distal the dendritic recording was made from the soma the
longer the time between the onset of the dendritic regenerative potential
relative to somatic action potential. This suggested that dendritic
regenerative potentials were initiated in the distal apical dendrites,
possibly in the apical tuft. 6. At any one stimulus intensity, the
initiation of dendritic regenerative potentials prior to somatic
action potentials could fluctuate, and was modulated by depolarizing
somatic or hyperpolarizing dendritic current injection. 7. Dendritic
regenerative potentials could be initiated prior to somatic action
potentials by dendritic current injections used to simulate the membrane
voltage change that occurs during an EPSP. Initiation of these dendritic
potentials was not affected by cadmium (200 microM), but was blocked
by TTX (1 microM). 8. Dendritic regenerative potentials in some experiments
were initiated in isolated from somatic action potentials. The voltage
change at the soma in response to these dendritic regenerative events
was small and subthreshold, showing that dendritic regenerative events
are strongly attenuated as they spread to the soma. 9. Simultaneous
whole-cell recordings from the axon initial segment and the soma
indicated that synaptic stimulation always initiated action potentials
first in the axon. The further the axonal recording was made from
the soma the greater the time delay between axonal and somatic action
potentials, indicating a site of action potential initiation in the
axon at least 30 microns distal to the soma. 10. Simultaneous whole-cell
recordings from the apical dendrite, soma and axon initial segment
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neocortical layer 5 pyramidal neurons in P26-30 animals are capable
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to the soma and axon. As a consequence, action potentials are always
initiated in the axon before the soma, even when synaptic activation
is intense enough to initiate dendritic regenerative potentials.
Once initiated, the axonal action potentials are conducted orthogradely
into the axonal arbor and retrogradely into the dendritic tree.},
address = {Abteilung Zellphysiologie, Max-Planck-Institut fur medizinische Forschung,
Heidelberg, Germany. Greg.Stuart@anu.edu.au},
au = {Stuart, G and Schiller, J and Sakmann, B},
da = {19980312},
date-added = {2008-03-28 17:06:36 +0100},
date-modified = {2008-03-28 17:06:39 +0100},
dcom = {19980312},
edat = {1998/02/11},
issn = {0022-3751 (Print)},
jid = {0266262},
jt = {The Journal of physiology},
language = {eng},
lr = {20061115},
mh = {Action Potentials/physiology; Animals; Axons/physiology; Dendrites/physiology;
Electric Conductivity; Excitatory Postsynaptic Potentials/physiology;
Neocortex/cytology/*physiology/ultrastructure; Patch-Clamp Techniques;
Pyramidal Cells/*physiology/ultrastructure; Rats; Rats, Wistar; Synapses/physiology},
mhda = {1998/02/11 00:01},
own = {NLM},
owner = {sprekeler},
pl = {ENGLAND},
pmid = {9457640},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print},
sb = {IM},
so = {J Physiol. 1997 Dec 15;505 ( Pt 3):617-32. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
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title = {Dendrites },
publisher = {Oxford University Press},
year = {2007},
author = {Stuart, Greg and Spruston, Nelson and H{\"a}usser, Michael},
address = {Oxford },
edition = {2nd ed},
bdsk-url-1 = {http://www.loc.gov/catdir/toc/ecip0712/2007010138.html},
call-number = {QP363},
date-added = {2008-03-26 14:54:18 +0100},
date-modified = {2008-03-26 14:54:21 +0100},
dewey-call-number = {573.8/5},
genre = {Dendrites},
isbn = {9780198566564 (alk. paper)},
library-id = {2007010138},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.loc.gov/catdir/toc/ecip0712/2007010138.html}
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author = {K. Svoboda and W. Denk and D. Kleinfeld and D.W. Tank},
title = {In vivo dendritic calcium dynamics in neocortical pyramidal neurons},
journal = {Nature},
year = {1997},
volume = {185},
pages = {161-165 }
}
@article{Swindale82,
author = {N. V. Swindale},
title = {A model for the formation of orientation columns},
journal = {Proc. Royal Soc. London B},
year = {1982},
volume = {215},
pages = {211-230}
}
@article{Takacs06,
author = {Balint Takacs and Andras Lorincz},
title = {Independent component analysis forms place cells in realistic robot
simulations},
journal = {Neurocomputing},
year = {2006},
volume = {69},
pages = {1249--1252},
number = {10-12},
keywords = { hippocampus , ICA},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Tanabe98,
author = {S. Tanabe and K. Pakdaman and T. Nomura and S. Sato},
title = {Dynamics of an ensemble of leaky integrate-and-fire neuron models
and its response to a pulse input},
journal = {Technical Report of IEICE},
year = {1998},
pages = {NLP98-14}
}
@article{Tanaka86,
author = {M. Tanaka and H. Weber and O. D. Creutzfeldt},
title = {{Visual properties and spatial distribution of neurones in the visual
association area on the prelunate gyrus of the awake monkey}},
journal = {Experimental Brain Research},
year = {1986},
volume = {65},
pages = {11--37},
number = {1},
keywords = {Vision, Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Tanaka04,
author = {S Tanaka and K Doya and G Okada and K Ueda and Y Okamoto and S Yamawaki},
title = {Prediction of immediate and future rewards differentially recruits
cortico-basal ganglia loops},
journal = {Nature Neuroscience},
year = {2004},
volume = {7},
pages = {887--893},
number = {8}
}
@article{Tanila97a,
author = {H. Tanila and M. Shapiro and H. Eichenbaum},
title = {Discordance of Spatial Representations in Ensembles of Hippocampal
Place Cells},
journal = {Hippocampus},
year = {1997},
volume = {7},
pages = {613--623},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Tanila97,
author = {H. Tanila and P. Sipila and M. Shapiro and H. Eichenbaum},
title = {Brain Aging: Impaired Coding of Novel Environmental Cues},
journal = {Journal of Neuroscience},
year = {1997},
volume = {17},
pages = {5167--5174},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Tank94,
author = {D. W. Tank and A. Gelperin and D. Kleinfeld},
title = {Odors, oscillations, and waves: {D}oes it all compute?},
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volume = {265},
pages = {1819--1820}
}
@article{Tateno04,
author = {T. Tateno and A. Harsch and H.P.C. Robinson},
title = {Threshold firing frequency - Current relationships of neurons in
rat somatosensory cortex: Type 1 and type 2 dynamics },
journal = {J. Neurophysiology},
year = {2004},
volume = {92},
pages = {2283-2294 }
}
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author = {J.S. Taube},
title = {Place cells recorded in the parasubiculum of freely moving rats},
journal = {Hippocampus},
year = {1995},
volume = {6},
pages = {569--583},
number = {5},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {J. S. Taube and J. P. Bassett},
title = {Persistent Neural Activity in Head Direction Cells},
journal = {Cerebral Cortex},
year = {2003},
volume = {13},
pages = {1162--1172},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Taube98,
author = {J. S. Taube and R. U. Muller},
title = {Comparisons of Head Direction Cell Activity in the Postsubiculum
and Anterior Thalamus of Freely Moving Rats},
journal = {Hippocampus},
year = {1998},
volume = {8},
pages = {87--108},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Taube90,
author = {J. S. Taube and R. U. Muller and J. B. Jr. Ranck},
title = {Head Direction Cells Recorded from the Postsubiculum in Freely Moving
Rats. {II. Effects} of Environmental Manipulations.},
journal = {Journal of Neuroscience},
year = {1990},
volume = {2},
pages = {436--447},
number = {10},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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author = {Tegner, J. and Kepecs, A.},
title = {{An adaptive spike-timing-dependent plasticity rule}},
journal = {Neurocomputing},
year = {2002},
volume = {44},
pages = {189--194},
number = {46},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Teich03,
author = {A. F. Teich and N. Qian},
title = {Learning and adaptation in a recurrent model of V1 orientation selectivity},
journal = {The Journal of Neurophysiology},
year = {2003},
volume = {89},
pages = {2086-2100},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Tenenbaum00,
author = {Tenenbaum, J.B. and Silva, V. and Langford, J.C.},
title = {{A Global Geometric Framework for Nonlinear Dimensionality Reduction}},
journal = {Science},
year = {2000},
volume = {290},
pages = {2319--2323},
number = {5500},
keywords = {Slowness},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Terashima08,
author = {Akira Terashima and Kenneth A Pelkey and Jong-Cheol Rah and Young
Ho Suh and Katherine W Roche and Graham L Collingridge and Chris
J McBain and John T R Isaac},
title = {An essential role for PICK1 in NMDA receptor-dependent bidirectional
synaptic plasticity.},
journal = {Neuron},
year = {2008},
volume = {57},
pages = {872--882},
number = {6},
month = {Mar},
abstract = {PICK1 is a calcium-sensing, PDZ domain-containing protein that interacts
with GluR2 and GluR3 AMPA receptor (AMPAR) subunits and regulates
their trafficking. Although PICK1 has been principally implicated
in long-term depression (LTD), PICK1 overexpression in CA1 pyramidal
neurons causes a CaMK- and PKC-dependent potentiation of AMPAR-mediated
transmission and an increase in synaptic GluR2-lacking AMPARs, mechanisms
associated with NMDA receptor (NMDAR)-dependent long-term potentiation
(LTP). Here, we directly tested whether PICK1 participates in both
hippocampal NMDAR-dependent LTP and LTD. We show that the PICK1 potentiation
of AMPAR-mediated transmission is NMDAR dependent and fully occludes
LTP. Conversely, blockade of PICK1 PDZ interactions or lack of PICK1
prevents LTP. These observations demonstrate an important role for
PICK1 in LTP. In addition, deletion of PICK1 or blockade of PICK1
PDZ binding prevented NMDAR-dependent LTD. Thus, PICK1 plays a critical
role in bidirectional NMDAR-dependent long-term synaptic plasticity
in the hippocampus.},
doi = {10.1016/j.neuron.2008.01.028},
keywords = {plasticity},
owner = {sprekeler},
pii = {S0896-6273(08)00115-3},
pmid = {18367088},
timestamp = {2008.04.15},
url = {http://dx.doi.org/10.1016/j.neuron.2008.01.028}
}
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author = {D. Terman and N. Kopell and A. Bose},
title = {Dynamics of mutually coupled slow inhibitory neurons},
journal = {Physica D},
year = {1998},
volume = {117},
pages = {241-275}
}
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author = {D. Terman and D. Wang},
title = {Global competition and local cooperation in a network of neural oscillators},
journal = {Physica D},
year = {1995},
volume = {81},
pages = {148-176}
}
@article{Tesauro94,
author = {G.J. Tesauro},
title = {TD-Gammon, a self-teaching backgammon program, achieves master-level
play},
journal = {Neural Computation},
year = {1994},
volume = {6},
pages = {215-219}
}
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author = {F.E. Theunissen and S.V. David and N.C. Sing and A. Hsu and W.E.
Vinje and J.L. Gallant},
title = {Estimating spatio-temporal receptive fiels of auditory and visual
neurons form their responses to natural stimuli},
journal = {Network},
year = {2001},
volume = {12},
pages = {289-316}
}
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author = {F. Theunissen and J.P. Miller},
title = {Temporal encoding in nervous systems: a rigorous definition},
journal = {J. Comput. Neurosci,},
year = {1995},
volume = {2},
pages = {149-162}
}
@book{Thompson93,
title = {The brain},
publisher = {W. H. Freeman and Company},
year = {1993},
author = {R. F. Thompson},
address = {New York},
edition = {2nd}
}
@book{Thompson90,
title = {Das {G}ehirn.},
publisher = {Spektrum der Wissenschaft},
year = {1990},
author = {R. F. Thompson},
address = {Heidelberg}
}
@article{Thomson07,
author = {A.M. Thomson and C. Lamy},
title = {Functional maps of neocortical local circuitry},
journal = {Frontiers in Neuroscience},
year = {2007},
volume = {1},
pages = {19-42}
}
@article{Thomson03,
author = {A. M. Thomson and D.C. West},
title = {Presynaptic Frequency Filtering in the Gamma Frequency Band; Dual
Intracellular Recordings in Slices of Adult Rat and Cat Neocortex},
journal = {Cerebral Cortex},
year = {2003},
volume = {13},
pages = {136-143}
}
@book{Thorndike11,
title = {Animal Intelligence},
publisher = {Hafner},
year = {1911},
author = {E.L. Thorndike},
address = {Darien, CT}
}
@article{Thorpe01,
author = {S. Thorpe and A. Delorme and R. \protect{Van Rullen}},
title = {Spike-based strategies for rapid processing},
journal = {Neural Networks},
year = {2001},
volume = {14},
pages = {715-725}
}
@article{Thorpe96,
author = {S. Thorpe and D. Fize and C. Marlot},
title = {Speed of processing in the human visual system},
journal = {Nature},
year = {1996},
volume = {381},
pages = {520-522}
}
@article{Thrun98,
author = {S. Thrun},
title = {Learning metric-topological maps for indoor mobile robot navigation},
journal = {Artificial Intelligence},
year = {1998},
volume = {99},
pages = {21-71}
}
@article{Tiesinga08,
author = {Paul Tiesinga and Jean-Marc Fellous and Terrence J. Sejnowski},
title = {Regulation of spike timing in visual cortical circuits},
journal = {Nature Reviews Neuroscience},
year = {2008},
volume = {9},
pages = {97-107},
keywords = {various-artists, neuronal-processing},
owner = {sprekeler},
pdf = {/home/sprekeler/literatur/paper/Fellous08.pdf},
timestamp = {2008.04.14}
}
@inproceedings{Tishby99,
author = {N. Tishby and F. Pereira and W. Bialek},
title = {The information bottleneck method},
booktitle = {Proceedings of the 37-th Annual Allerton Conference on Communication,
Control and Computing},
year = {1999},
pages = {368--377},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {citeseer.ist.psu.edu/tishby99information.html}
}
@article{Toledo-Rodriguez04,
author = {Toledo-Rodriguez, Maria and Blumenfeld, Barak and Wu, Caizhi and
Luo, Junyi and Attali, Bernard and Goodman, Philip and Markram, Henry},
title = {Correlation maps allow neuronal electrical properties to be predicted
from single-cell gene expression profiles in rat neocortex.},
journal = {Cereb Cortex},
year = {2004},
volume = {14},
pages = {1310--1327},
number = {12},
abstract = {The computational power of the neocortex arises from interactions
of multiple neurons, which display a wide range of electrical properties.
The gene expression profiles underlying this phenotypic diversity
are unknown. To explore this relationship, we combined whole-cell
electrical recordings with single-cell multiplex RT-PCR of rat (p13-16)
neocortical neurons to obtain cDNA libraries of 26 ion channels (including
voltage activated potassium channels, Kv1.1/2/4/6, Kvbeta1/2, Kv2.1/2,
Kv3.1/2/3/4, Kv4.2/3; sodium/potassium permeable hyperpolarization
activated channels, HCN1/2/3/4; the calcium activated potassium channel,
SK2; voltage activated calcium channels, Caalpha1A/B/G/I, Cabeta1/3/4),
three calcium binding proteins (calbindin, parvalbumin and calretinin)
and GAPDH. We found a previously unreported clustering of ion channel
genes around the three calcium-binding proteins. We further determined
that cells similar in their expression patterns were also similar
in their electrical properties. Subsequent regression modeling with
statistical resampling yielded a set of coefficients that reliably
predicted electrical properties from the expression profile of individual
neurons. This is the first report of a consistent relationship between
the co-expression of a large profile of ion channel and calcium binding
protein genes and the electrical phenotype of individual neocortical
neurons.},
address = {Brain and Mind Institute, EPFL, Lausanne 1015, Switzerland.},
au = {Toledo-Rodriguez, M and Blumenfeld, B and Wu, C and Luo, J and Attali,
B and Goodman, P and Markram, H},
bdsk-url-1 = {http://dx.doi.org/10.1093/cercor/bhh092},
da = {20041111},
date-added = {2008-03-29 17:52:45 +0100},
date-modified = {2008-03-29 17:55:28 +0100},
dcom = {20050125},
dep = {20040610},
doi = {10.1093/cercor/bhh092},
edat = {2004/06/12 05:00},
issn = {1047-3211 (Print)},
jid = {9110718},
jt = {Cerebral cortex (New York, N.Y. : 1991)},
language = {eng},
lr = {20061115},
mh = {Action Potentials/*physiology; Animals; Gene Expression Profiling/*methods;
Neocortex/cytology/*physiology; Neurons/cytology/*physiology; Predictive
Value of Tests; Rats; Rats, Wistar},
mhda = {2005/01/26 09:00},
own = {NLM},
owner = {sprekeler},
phst = {2004/06/10 {$[$}aheadofprint{$]$}},
pii = {bhh092},
pl = {United States},
pmid = {15192011},
pst = {ppublish},
pt = {In Vitro; Journal Article; Research Support, Non-U.S. Gov't},
pubm = {Print-Electronic},
sb = {IM},
so = {Cereb Cortex. 2004 Dec;14(12):1310-27. Epub 2004 Jun 10. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@article{Toledo04,
author = {M. Toledo-Rodriguez and B. Blumenfeld and C. Wu and J. Luo and B.
Attali and P. Goodman and Henry Markram},
title = {Correlation Maps Allow Neuronal Electrical Properties to be Predicted
from Single-cell Gene Expression Profiles in Rat Neocortex},
journal = {Cerebral Cortex},
year = {2004},
volume = {14},
pages = {1310-1327}
}
@article{Tolhurst83,
author = {D.J. Tolhurst and J.A. Movshon and A.F. Dean},
title = {The statistical reliability of signals in single neurons in cat and
monkey visual-cortex},
journal = {Vision Research},
year = {1983},
volume = {23},
pages = {775-785}
}
@article{Tolman48,
author = {E. C. Tolman},
title = {Cognitive Maps in Rats and Man},
journal = {Psychological Review},
year = {1948},
volume = {55},
pages = {189--208},
note = {Definition of the cognitive map},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Tonnelier02,
author = {A. Tonnelier},
title = {The piecewise linear FitzHugh-Nagumo model. {I} the space clamped
system},
journal = {Technical Report, University of Grenoble},
year = {2002},
volume = {xx},
pages = {xx}
}
@article{Tonnelier03,
author = {A. Tonnelier and W. Gerstner},
title = {Piecewise linear differential equations and integrate-and-fire neurons
: insights from two-dimensional membrane models},
journal = {Phys. Rev. E},
year = {2003},
volume = {67},
pages = {21908}
}
@article{Torborg05,
author = {Torborg, CL and Feller, MB},
title = {{Spontaneous patterned retinal activity and the refinement of retinal
projections.}},
journal = {Progress in Neurobiology},
year = {2005},
volume = {76},
pages = {312--235},
number = {4},
keywords = {Vision,Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Touboul08,
author = {Touboul, Jonathan},
title = {Bifurcation Analysis of a General Class of Nonlinear Integrate-and-Fire
Neurons},
journal = {SIAM Journal on Applied Mathematics},
year = {2008},
volume = {68},
pages = {1045-1079},
number = {4},
date-added = {2008-03-31 11:49:45 +0200},
date-modified = {2008-03-31 11:52:07 +0200},
keywords = {neuron models; dynamical system analysis; nonlinear dynamics; Hopf
bifurcation; saddle-node bifurcation; BogdanovTakens bifurcation},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{NIPS90,
title = {Advances in Neural Information Processing Systems},
publisher = {Morgan Kaufmann Publishers},
year = {1990},
author = {D. S. Touretzky },
volume = {2},
address = {San Mateo}
}
@article{Touryan05,
author = {Touryan, Jon and Felsen, Gidon and Dan, Yang},
title = {{{S}patial structure of complex cell receptive fields measured with
natural images}},
journal = {Neuron},
year = {2005},
volume = {45},
pages = {781--791},
number = {5},
month = {Mar},
keywords = {Plasticity, Vision, Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Tovee95,
author = {M. J. Tovee and E. T. Rolls},
title = {Information encoding in short firing rate epochs by single neurons
in the primate temporal visual cortex},
journal = {Visual Cognition},
year = {1995},
volume = {2},
pages = {35-58},
number = {1}
}
@article{Tovee93,
author = {M. J. Tovee and E. T. Rolls and A. Treves and R. P. Belles},
title = {Information encoding and the responses of single neurons in the primate
visual cortex},
journal = {J. Neurophysiol.},
year = {1993},
volume = {70},
pages = {640-654}
}
@article{Toyoizumi07,
author = {T. Toyoizumi and J.-P. Pfister and K. Aihara and W. Gerstner},
title = {Optimality Model of Unsupervised Spike-Timing Dependent Plasticity:
Synaptic Memory and Weight Distribution},
journal = {Neural comutation},
year = {2007},
volume = {19},
pages = {639-671}
}
@article{Toyoizumi07a,
author = {Toyoizumi, T. and Pfister, J.P. and Aihara, K. and Gerstner, W.},
title = {{Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity:
Synaptic Memory and Weight Distribution}},
journal = {Neural Computation},
year = {2007},
volume = {19},
pages = {639},
number = {3},
keywords = {plasticity},
owner = {sprekeler},
publisher = {MIT Press},
timestamp = {2008.04.14}
}
@article{Toyoizumi05,
author = {T. Toyoizumi and J.-P. Pfister and K. Aihara and W. Gerstner},
title = {Generalized Bienenstock-Cooper-Munro rule for spiking neurons that
maximizes information transmission},
journal = {Proc. National Academy Sciences (USA)},
year = {2005},
volume = {102},
pages = {5239-5244}
}
@article{Toyoizumi05b,
author = {Toyoizumi, T. and Pfister, J.P. and Aihara, K. and Gerstner, W.},
title = {{Generalized Bienenstock-Cooper-Munro rule for spiking neurons that
maximizes information transmission}},
journal = {Proceedings of the National Academy of Sciences},
year = {2005},
volume = {102},
pages = {5239--5244},
number = {14},
keywords = {plasticity,optimal-coding},
owner = {sprekeler},
publisher = {National Acad Sciences},
timestamp = {2008.04.14}
}
@incollection{Toyoizumi05a,
author = {Taro {Toyoizumi} and Jean-Pascal {Pfister} and Kazuyuki {Aihara}
and Wulfram {Gerstner}},
title = {Spike-timing Dependent Plasticity and Mutual Information Maximization
for a Spiking Neuron Model},
booktitle = {Advances in Neural Information Processing Systems 17},
publisher = {MIT Press},
year = {2005},
editor = {Lawrence K. Saul and Yair Weiss and {L\'{e}on} Bottou},
pages = {1409-1416},
address = {Cambridge, MA}
}
@article{Traub04,
author = {R.D. Traub and A. Bibbig and F.E. LeBeau and E.H. Buhl and M.A. Whittington},
title = {Cellular mechanisms of neuronal population oscillations in the hippocampus
in vitro},
journal = {Annu.~{R}ev.~{N}eurosci.},
year = {2004},
volume = {27},
pages = {247--278}
}
@book{Traub91b,
title = {Neural Networks of the Hippocampus},
publisher = {Cambridge University Press},
year = {1991},
author = {R. D. Traub and R. Miles},
address = {Cambridge}
}
@article{Traub91,
author = {R. D. Traub and R. K. S. Wong and R. Miles and H. Michelson},
title = {A model of a \protect{CA3} hippocampal pyramidal neuron incorporating
voltage-clamp data on intrinsic conductances},
journal = {J. Neurophysiol.},
year = {1991},
volume = {66},
pages = {635--650}
}
@mastersthesis{Trefz91,
author = {T. Trefz},
title = {Oszillationen im Cortex.},
school = {Technische Universit\protect{\"a}t M\protect{\"u}nchen},
year = {1991},
type = {Diplomarbeit}
}
@article{Treves93,
author = {A. Treves},
title = {Mean-field analysis of neuronal spike dynamics},
journal = {Network },
year = {1993},
volume = {4},
pages = {259-284}
}
@article{Treves92,
author = {A. Treves},
title = {Local neocortical processing: a time for recognition},
journal = {Int. J. of Neural Systems},
year = {1992},
volume = {3 (Supp)},
pages = {115-119}
}
@article{Treves90,
author = {A. Treves},
title = {Graded-response neurons and information encoding in autoassociative
memory},
journal = {Phys.~Rev. A},
year = {1990},
volume = {42},
pages = {2418-2430}
}
@article{Treves92a,
author = {A. Treves and O. Miglino and D. Parisi},
title = {Rats, nets, maps and the emergence of place cells},
journal = {Psychobiology},
year = {1992},
volume = {20},
pages = {1--8},
number = {1},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Treves94,
author = {A. Treves and E. T. Rolls},
title = {A computational analysis of the role of the hippocampus in learning
and memory},
journal = {Hippocampus},
year = {1994},
volume = {4},
pages = {373--391},
number = {3},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Treves97,
author = {A. Treves and E. T. Rolls and M. Simmen},
title = {Time for retrieval in recurrent associative memories},
journal = {Physica D},
year = {1997},
volume = {107},
pages = {392-400}
}
@incollection{Triesch05,
author = {J. Triesch},
title = {A gradient rule for the plasticity of a neuron's intrinsic excitability},
booktitle = {ICANN 2005},
publisher = {Springer-Verlag},
year = {2005},
editor = {W. Duch and al.},
volume = {3696},
series = {LNCS},
pages = {65-70},
address = {Berlin Heidelberg},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Triesch07,
author = {J. Triesch},
title = {Synergies between intrinsic and synaptic plasticity mechanisms},
journal = {Neural computation},
year = {2007},
volume = {19},
pages = {885 -909}
}
@article{Troyer98,
author = {T. W. Troyer and A.E. Krukowski and N.J. Priebe and K.D. Miller},
title = {Contrast-invariant orientation tuning in cat visual cortex: thalamocortical
input tuning and correlation-based intracortical connectivity},
journal = {J. Neuroscience},
year = {1998},
volume = {18},
pages = {5908-5927}
}
@article{Troyer97,
author = {T. W. Troyer and K.D. Miller},
title = {Physiological gain leads to high \protect{ISI} variability in a simple
model of a cortical regular spiking cell},
journal = {Neural Computation},
year = {1997},
volume = {9},
pages = {971-983}
}
@article{Trullier97,
author = {O. Trullier and S.I. Wiener and A. Berthoz and J.-A. Meyer},
title = {Biologically based artificial navigation systems: review and prospects},
journal = {Progress in Neurobiology},
year = {1997},
volume = {51},
pages = {483-544}
}
@article{Tsien88,
author = {R.W. Tsien and D. Lipscombe and D. V Madison and K.R. Bley and A.
P. Fox},
title = {Multiple types of calcium channels and their selective modulations},
journal = {Trends in Neuroscience},
year = {1988},
volume = {11},
pages = {431-438}
}
@article{Tsodyks04,
author = {Misha Tsodyks and Charles Gilbert},
title = {Neural networks and perceptual learning},
journal = {Nature},
year = {2004},
volume = {431},
pages = {775-781}
}
@article{Tsodyks99,
author = {Tsodyks, M. and Kenet, T. and Grinvald, A. and Arieli, A.},
title = {{Linking Spontaneous Activity of Single Cortical Neurons and the
Underlying Functional Architecture}},
journal = {Science},
year = {1999},
volume = {286},
pages = {1943},
number = {5446},
keywords = {neuronal-processing},
owner = {sprekeler},
publisher = {AAAS},
timestamp = {2008.04.14}
}
@article{Tsodyks97,
author = {M. Tsodyks and H. Markram},
title = {The neural code between neocortical pyramidal neurons depends on
neurotransmitter release probability},
journal = {Proc. Natl. Academy of Sci., USA},
year = {1997},
volume = {94},
pages = {719-723}
}
@article{Tsodyks93,
author = {M. Tsodyks and I. Mitkov and H. Sompolinsky},
title = {Patterns of synchrony in inhomogeneous networks of oscillators with
pulse interaction},
journal = {Phys.~Rev.~Lett.},
year = {1993},
volume = {71},
pages = {1281-1283}
}
@article{Tsodyks95,
author = {M. V. Tsodyks and T. Sejnowski},
title = {Rapid state switching in balanced cortical networks},
journal = {Network},
year = {1995},
volume = {6},
pages = {111-124}
}
@book{Tuckwell89,
title = {Stochastic Processes in the Neurosciences},
publisher = {SIAM},
year = {1989},
author = {H.~C.~Tuckwell},
address = {Philadelphia}
}
@book{Tuckwell-all,
title = {Introduction to theoretic neurobiology},
publisher = {Cambridge Univ. Press},
year = {1988},
author = {H. C. Tuckwell},
address = {Cambridge}
}
@book{Tuckwell88a,
title = {Introduction to theoretic neurobiology},
publisher = {Cambridge Univ. Press},
year = {1988},
author = {H. C. Tuckwell},
volume = {1},
address = {Cambridge}
}
@book{Tuckwell88b,
title = {Introduction to theoretic neurobiology},
publisher = {Cambridge Univ. Press},
year = {1988},
author = {H. C. Tuckwell},
volume = {2},
address = {Cambridge}
}
@article{Tuckwell79,
author = {H. C. Tuckwell},
title = {Synaptic transmission in a model for stochastic neural activity},
journal = {J. Theor. Biology},
year = {1979},
volume = {71},
pages = {167-183}
}
@article{Tuckwell79b,
author = {H. C. Tuckwell},
title = {The response of a spatially distributed neuron to white noise current
injection},
journal = {Biol. Cybern.},
year = {1979},
volume = {33},
pages = {39-55}
}
@article{Turner07,
author = {Turner, R. and Sahani, M.},
title = {{A Maximum-Likelihood Interpretation for Slow Feature Analysis}},
journal = {Neural Computation},
year = {2007},
volume = {19},
pages = {1022},
number = {4},
keywords = {slowness, optimal-coding},
owner = {sprekeler},
publisher = {MIT Press},
timestamp = {2008.04.14}
}
@article{Turrigiano04,
author = {G.G. Turrigiano and S.B. Nelson},
title = {Homeostatic plasticity in the developing nervous system},
journal = {Nature Reviews Neuroscience},
year = {2004},
volume = {5},
pages = {97-107}
}
@article{Turrigiano07,
author = {Turrigiano, G. G.},
title = {{Homeostatic signaling: the positive side of negative feedback.}},
journal = {Current Opinion in Neurobiology},
year = {2007},
volume = {17},
pages = {318--324},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Turrigiano98,
author = {Gina G. Turrigiano and Kenneth R. Leslie and Niraj S. Desai and Lana
C. Rutherford and Sacha B. Nelson},
title = {Activity-dependent scaling of quantal amplitude in neocortical neurons},
journal = {{Nature}},
year = {1998},
volume = {391},
pages = {892--895},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Turrigiano00,
author = {Gina G. Turrigiano and Sacha B. Nelson},
title = {Hebb and Homeostasis in Neuronal Plasticity},
journal = {{Current Opinion in Neurobiology}},
year = {2000},
volume = {10},
pages = {358--364},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Tyson88,
author = {J.J. Tyson and J. P. Keener},
title = {Singular perturbation theory of travelling waves in excitable media
(a review).},
journal = {Physica D},
year = {1988},
volume = {32},
pages = {327--361}
}
@article{Tzounopoulos04,
author = {Thanos Tzounopoulos and Yuil Kim and Donata Oertel and Laurence O
Trussell},
title = {Cell-specific, spike timing−dependent plasticities in the dorsal
cochlear nucleus},
journal = {Nature Neuroscience},
year = {2004},
volume = {7},
pages = {719-125},
annote = {STDP}
}
@article{Tzounopoulos04a,
author = {Thanos Tzounopoulos and Yuil Kim and Donata Oertel and Laurenze O.
Trussell},
title = {Cell-specific, spike timing-dependent plasticity in the dorsal cochlear
nucleus},
journal = {Nature Neuroscience},
year = {2004},
volume = {7},
pages = {719--725},
number = {7},
keywords = {Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Turker05,
author = {K.S. T{\"u}rker and R.K. Powers},
title = {Black box revisited: a technique for estimating postsynaptic potentials
in neurons},
journal = {Trends in Neurosciences},
year = {2005},
volume = {28},
pages = {379-386}
}
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author = {G. E. Uhlenbeck and L. S. Ornstein},
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pages = {823-841}
}
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author = {M. Usher and H. G. Schuster and E. Niebur},
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and memory},
journal = {Neural Comp.},
year = {1993},
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pages = {570-586}
}
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author = {M. Usher and M. Stemmler and Z. Olami},
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pages = {326-329}
}
@article{Afraimovich86,
author = {V.Afraimovich and N.Veritchev and M.Rabinovich},
title = {Stochastically synchronized oscillators in dissipative systems},
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year = {1986},
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pages = {795},
note = {in Russian},
annote = {Hasler - paper cited in grant proposal}
}
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author = {Vanier, M C and Bower, J M},
title = {A comparative survey of automated parameter-search methods for compartmental
neural models.},
journal = {J Comput Neurosci},
year = {1999},
volume = {7},
pages = {149--171},
number = {2},
abstract = {One of the most difficult and time-consuming aspects of building compartmental
models of single neurons is assigning values to free parameters to
make models match experimental data. Automated parameter-search methods
potentially represent a more rapid and less labor-intensive alternative
to choosing parameters manually. Here we compare the performance
of four different parameter-search methods on several single-neuron
models. The methods compared are conjugate-gradient descent, genetic
algorithms, simulated annealing, and stochastic search. Each method
has been tested on five different neuronal models ranging from simple
models with between 3 and 15 parameters to a realistic pyramidal
cell model with 23 parameters. The results demonstrate that genetic
algorithms and simulated annealing are generally the most effective
methods. Simulated annealing was overwhelmingly the most effective
method for simple models with small numbers of parameters, but the
genetic algorithm method was equally effective for more complex models
with larger numbers of parameters. The discussion considers possible
explanations for these results and makes several specific recommendations
for the use of parameter searches on neuronal models.},
address = {Department of Computation and Neural Systems, California Institute
of Technology, Pasadena 91125, USA. mvanier@bbb.caltech.edu},
au = {Vanier, MC and Bower, JM},
da = {19991124},
date-added = {2008-03-28 11:51:31 +0100},
date-modified = {2008-03-28 11:51:37 +0100},
dcom = {19991124},
edat = {1999/10/09},
issn = {0929-5313 (Print)},
jid = {9439510},
jt = {Journal of computational neuroscience},
language = {eng},
lr = {20001218},
mh = {Action Potentials/physiology; Algorithms; Calcium Channels/physiology;
Cell Compartmentation/physiology; Computer Simulation; Dendrites/*physiology;
*Models, Neurological; Olfactory Pathways/physiology; Potassium Channels/physiology;
Pyramidal Cells/*physiology; Sodium Channels/physiology},
mhda = {1999/10/09 00:01},
own = {NLM},
owner = {sprekeler},
pl = {UNITED STATES},
pmid = {10515252},
pst = {ppublish},
pt = {Journal Article},
pubm = {Print},
rn = {0 (Calcium Channels); 0 (Potassium Channels); 0 (Sodium Channels)},
sb = {IM},
so = {J Comput Neurosci. 1999 Sep-Oct;7(2):149-71. },
stat = {MEDLINE},
timestamp = {2008.04.14}
}
@book{Vapnik00,
title = {{The Nature of Statistical Learning Theory}},
publisher = {Springer},
year = {2000},
author = {Vapnik, V.N.},
keywords = {Various-Artists},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Vapnik95,
title = {The Nature of Statistical Learning Theory},
publisher = {Springer-Verlag, New York},
year = {1995},
author = {V. Vapnik}
}
@incollection{Veksler07,
author = {Veksler, V. D. and Gray, W. D. and Schoelles M. J},
title = {Categorization and Reinforcement Learning: State Identification in
Reinforcement Learning and Network Reinforcement Learning.},
booktitle = {Twenty-Ninth Annual Meeting of the Cognitive Science Society}
}
@book{Verhulst96,
title = {Nonlinear differential equations and dynamical systems},
publisher = {Springer},
year = {1996},
author = {F. Verhulst},
address = {Berlin}
}
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author = {L. Viana and A.J. Bray},
title = {Phase-diagrams for dilute spin-glasses },
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year = {1985},
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pages = {3037-3051 }
}
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author = {R. Vogels and G. A. Orban},
title = {The effect of practice on the oblique effect in line orientation
judgments},
journal = {Vision Research},
year = {1985},
volume = {25},
pages = {1679-1687},
number = {11},
keywords = {Perceptual-Learning},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@book{Volterra59,
title = {Theory of functionals and of integral and integro-differential equations},
publisher = {Dover Publications Inc.},
year = {1959},
author = {V. Volterra}
}
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author = {C. van Vreeswijk},
title = {Stability of the Asynchronous State in Networks of Non-linear Oscillators},
journal = {Physical Review Letters},
year = {2000},
volume = {84},
pages = {5110-5113}
}
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author = {C. van Vreeswijk},
title = {Partially synchronized states in networks of pulse-coupled neurons},
journal = {Physical Review E},
year = {1996},
volume = {54},
pages = {5522-5537}
}
@article{Vreeswijk93,
author = {Carl van Vreeswijk and L. F. Abbott},
title = {Self-sustained firing in populations of integrate-and-fire neurons},
journal = {SIAM J. Appl. Math.},
year = {1993},
volume = {53},
pages = {253-264},
number = {1}
}
@article{Vreeswijk94,
author = {Carl van Vreeswijk and L. F. Abbott and G. Bard Ermentrout},
title = {Inhibition not excitation synchronizes neural firing},
journal = {Journal of Computational Neuroscience},
year = {1994},
volume = {1},
pages = {303-313}
}
@article{Vreeswijk01,
author = {C. van Vreeswijk and D. Hansel},
title = {Patterns of synchrony in neural networks with spike adaptation},
journal = {Neural Computation},
year = {2001},
volume = {13},
pages = {959-992}
}
@incollection{Vreeswijk97,
author = {Carl van Vreeswijk and H. Sompolinsky},
title = {Irregular firing in cortical circuits with inhibition/excitation
balance},
booktitle = {Computational Neuroscience: Trends in Reserach, 1997},
publisher = {Plenum Press, New York},
year = {1997},
editor = {J. Bower},
pages = {209-213}
}
@article{Vreeswijk98,
author = {C. van Vreeswijk and H. Sompolinsky},
title = {Chaotic Balanced state in a model of cortical circuits},
journal = {Neural Computation},
year = {1998},
volume = {10},
pages = {1321-1371}
}
@article{Vreeswijk96,
author = {C. van Vreeswijk and H. Sompolinsky},
title = {Chaos in neuronal networks with balanced excitatory and inhibitory
activity},
journal = {Science},
year = {1996},
volume = {274},
pages = {1724-1726}
}
@article{Yao90,
author = {Y.Yaoand W.Freeman},
title = {Model of biological pattern recognition with spatially chaotic dynamics},
journal = {Neural Networks},
year = {1990},
volume = {3},
pages = {153-170},
annote = {hasler citation}
}
@article{Wagner93,
author = {Hermann Wagner and Barrle Frost},
title = {Disparity-sensitive cells in the owl have a characteristic disparity},
journal = {Nature},
year = {1993},
volume = {364},
pages = {796--798},
number = {6440}
}
@article{Wallis97a,
author = {Guy Wallis and Roland Baddeley},
title = {Optimal, Unsupervised Learning in Invariant Object Recognition},
journal = {Neural Computation},
year = {1997},
volume = {9},
pages = {883-894},
keywords = {vision-models, slowness},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Wallis01,
author = {Wallis, G. and B{\"u}lthoff, H.H.},
title = {Effects of temporal association on recognition memory},
journal = {Proceedings of the National Academy of Sciences},
year = {2001},
pages = {71028598},
keywords = {slowness, vision, vision-models, vision-physiology},
owner = {sprekeler},
publisher = {National Acad Sciences},
timestamp = {2008.04.14}
}
@article{Wallis97,
author = {Wallis, Guy and Rolls, Edmund T.},
title = {Invariant Face and Object Recognition in the Visual System.},
journal = {Progress in Neurobiology},
year = {1997},
volume = {51},
pages = {167--194},
number = {2},
month = feb,
keywords = {slowness, vision-models},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://www.uq.edu.au/~uqgwalli/abstracts.html#pnb}
}
@inproceedings{Wan94,
author = {H.S. Wan and David S Touretzky and A.D. Redish},
title = {Towards a computational theory of rat navigation},
booktitle = {Proceedings of the 1993 Connectionist Models Summer School},
year = {1994},
pages = {11-19},
publisher = {Lawrence Earlbaum Associates},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Wang95,
author = {D. Wang},
title = {Emergent synchrony in locally coupled neural oscillators},
journal = {IEEE Transactions on Neural Networks},
year = {1995},
volume = {6},
pages = {941-948}
}
@article{Wang90,
author = {D. Wang and J. Buhmann and C. von~der~Malsburg},
title = {Pattern segmentation in associative memory.},
journal = {Neural Computation},
year = {1990},
volume = {2},
pages = {94--106}
}
@article{Wang05,
author = {H.-X. Wang and R.C. Gerkin and D.W. Nauen and G.-Q. Wang},
title = {Coactivation and timing-dependent integration of synaptic potentiation
and depression},
journal = {Nature Neuroscience},
year = {2005},
volume = {8},
pages = {187-193}
}
@article{Wang02b,
author = {X.-J. Wang},
title = {Probabilistic decision making by slow reverrberation in cortical
circuits},
journal = {Neuron},
year = {2002},
volume = {36},
pages = {955-968}
}
@article{Wang02,
author = {Y. Wang and A. Gupta and M. Toledo-Rodriguez and C.Z. Wu and H. Markram},
title = {Anatomical, physiological, molecular and circuit properties of nest
basket cells in the developing somatosensory cortex},
journal = {Cerebral Cortex},
year = {2002},
volume = {12},
pages = {395-410}
}
@article{Warland06,
author = {Warland, D.K. and Huberman, A.D. and Chalupa, L.M.},
title = {{Dynamics of Spontaneous Activity in the Fetal Macaque Retina during
Development of Retinogeniculate Pathways}},
journal = {Journal of Neuroscience},
year = {2006},
volume = {26},
pages = {5190},
number = {19},
keywords = {vision,vision-physiology,plasticity},
owner = {sprekeler},
publisher = {Soc Neuroscience},
timestamp = {2008.04.14}
}
@book{Watkins89,
title = {Learning from delayed rewards},
publisher = {PhD-thesis, Cambridge University},
year = {1989},
author = {C.J.C.H. Watkings},
address = {Cambridge}
}
@article{Watkins92,
author = {C.J.C.H. Watkins and P. Dayan},
title = {Q-learning},
journal = {Machine Learning},
year = {1992},
volume = {8},
pages = {279-292}
}
@book{Wechsler,
title = {Neural networks for perception, vol. 1 + 2},
publisher = {Academic Press},
year = {199x},
author = {H. Wechsler},
address = {Boston}
}
@incollection{Wehmeier89,
author = {U. Wehmeier and D. Dong and C. Koch and D. van Essen},
title = {Modeling the Mammalian Visual System},
booktitle = {Methods in Neuronal Modeling},
publisher = {MIT Press},
year = {1989},
pages = {335-359},
address = {Cambridge}
}
@article{Wehner96,
author = {R. Wehner and B. Michel and P. Antonsen},
title = {{Visual navigation in insects: Coupling of egocentric and geocentric
information}},
journal = {Journal of Experimental Biology},
year = {1996},
volume = {199},
pages = {129--140}
}
@article{Wehr03,
author = {M. Wehr and A.M. Zador},
title = {Balanced inhibition underlies tuning and sharpens spike timing in
auditory cortex},
journal = {Nature},
year = {2003},
volume = {426},
pages = {442-446}
}
@article{Weiss66,
author = {T.F. Weiss},
title = {A model of the peripheral auditory system},
journal = {Kybernetik},
year = {1966},
volume = {3},
pages = {153-175}
}
@article{Westheimer94,
author = {G. Westheimer},
title = {Illusory figures and real neurons.},
journal = {Nature},
year = {1994},
volume = {371},
pages = {745--746}
}
@article{White00,
author = {J. A. White and J. T. Rubinstein and A. R. Kay},
title = {Channle noise in neurons},
journal = {Trends in Neurosciences},
year = {2000},
volume = {23},
pages = {131-137}
}
@article{White04,
author = {O. L. White and D. D. Lee and H. Sompolinsky},
title = {Short-Term Memory in Orthogonal Neural Networks},
journal = {Phys. Rev. Lett},
year = {2004},
volume = {92},
number = {148102}
}
@article{Wickens97,
author = {J. Wickens},
title = {Basal ganglia: structure and computations},
journal = {Network: Computation in Neural Systems},
year = {1997},
volume = {8},
pages = {R77-R109}
}
@article{Wickens90,
author = {J.R. Wickens},
title = {Stiatal dopamine in motor activiation and reward-mediated learning:
steps towards a unifying model},
journal = {J. Neural Transmism.},
year = {1990},
volume = {80},
pages = {9-31}
}
@incollection{Wickens95,
author = {J. Wickens and R. Kotter},
title = {Cellular models of reinforcement},
booktitle = {Models of information processing in basal ganglia},
publisher = {MIT-Press},
year = {1995},
editor = {J.C. Houk and J.L. Davis and D. G. Beiser},
pages = {187-214},
address = {Cambridge}
}
@article{Widrow73,
author = {B. Widrow and N.K. Gupta and S. Maitra},
title = {Punish/Reward: learning with a critic in adaptive threshold systems},
journal = {IEEE transactions on systems, man, cybernetics},
year = {1973},
volume = {3},
pages = {455-465}
}
@inproceedings{Widrow60,
author = {B. Widrow and M. E. Hoff},
title = {Adaptive switching circuits},
booktitle = {1960 IRE WESCON Convention Record},
year = {1960},
pages = {96--104},
address = {New York: IRE}
}
@article{Wiener03,
author = {M.C. Wiener and B.J. Richmond},
title = {Decoding spike trains instant by instant using order statistics and
the mixture-of-Poissons model.
},
journal = {Neuroscience},
year = {2003},
volume = {23},
pages = {2394-2406}
}
@article{Wiener89,
author = {S. I. Wiener and C. A. Paul and H. Eichenbaum},
title = {Spatial and Behavioral Correlates of Hippocampal Neuronal Activity},
journal = {Journal of Neuroscience},
year = {1989},
volume = {9},
pages = {2736--2763},
number = {8},
keywords = {Hippocampus},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Wierenga05,
author = {Wierenga, Corette J. and Ibata, Keiji and Turrigiano, Gina G.},
title = {Postsynaptic Expression of Homeostatic Plasticity at Neocortical
Synapses},
journal = {J. Neurosci.},
year = {2005},
volume = {25},
pages = {2895--2905},
number = {11},
month = mar,
abstract = {Synaptic scaling is a form of homeostatic plasticity that scales synaptic
strengths up or down to compensate for prolonged changes in activity.
It has been controversial whether this plasticity is expressed presynaptically,
postsynaptically, or both. Here we describe in detail the homeostatic
changes that take place at excitatory synapses in visual cortical
cultures after 1 or 2 d of activity blockade. After 7-10 d in vitro,
activity blockade significantly increased postsynaptic accumulation
of synaptic AMPA receptors via proportional increases in glutamate
receptor 1 (GluR1) and GluR2. Time-lapse imaging of enhanced green
fluorescent protein-tagged AMPA receptors revealed that receptor
accumulation increased progressively over 2 d of activity blockade
and affected the entire population of imaged synapses. The strength
of synaptic connections between pyramidal neurons was more than doubled
after activity blockade without affecting short-term depression or
the coefficient of variation of the postsynaptic responses. Furthermore,
uptake of the fluorescent styryl dye FM1-43 (N-(3-triethylammoniumpropyl)-4-[4-(dibutylamino)styryl]
pyridinium dibromide) by presynaptic terminals was not different
at control and activity-blocked synapses. In addition to the increased
accumulation of postsynaptic AMPA receptors, boosting of dendritic
AMPA currents by sodium channels was increased by activity blockade.
These data indicate that, at young neocortical synapses, synaptic
scaling has a predominantly postsynaptic locus and functions as a
gain control mechanism to regulate neuronal activity without affecting
the dynamics of synaptic transmission.},
comment = {10.1523/JNEUROSCI.5217-04.2005},
keywords = {plasticity},
owner = {sprekeler},
timestamp = {2008.04.14},
url = {http://www.jneurosci.org/cgi/content/abstract/25/11/2895}
}
@article{Wiering97,
author = {Wiering, M. and Schmidhuber, J. },
title = {{HQ}-Learning},
journal = {Adaptive Behavior},
year = {1998},
volume = {6},
pages = {219--246},
number = {2},
citeulike-article-id = {2381248},
keywords = {juergen},
priority = {2}
}
@article{Wiesel82,
author = {T. N. Wiesel},
title = {Postnatal development of the visual cortex and the influence of environment.},
journal = {Nature},
year = {1982},
volume = {299},
pages = {583--591},
number = {5884},
month = {Oct},
keywords = {Age Factors; Animals; Brain Mapping; Cats; Environment; Geniculate
Bodies; Haplorhini; Neuronal Plasticity; Visual Cortex; Visual Perception},
owner = {sprekeler},
pmid = {6811951},
timestamp = {2008.04.14}
}
@article{Wiesenfeld98,
author = {K. Wiesenfeld and F. Jaramillo},
title = {Minireview of stochastic resonance},
journal = {Chaos},
year = {1998},
volume = {8},
pages = {539-548}
}
@article{Wiesenfeld95,
author = {K. Wiesenfeld and F. Moss},
title = {Stochastic resonance and the benefits of noise: from ice ages to
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booktitle = {ICANN'95},
publisher = {Springer},
year = {1995},
editor = {xx},
volume = {xx},
pages = {(preprint)},
keywords = {Vision}
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year = {1997},
volume = {77},
pages = {463--477},
annote = {visual cortex, V1, development, spatiotemporal receptive fields,
simple cells},
keywords = {Vision}
}
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author = {S. Wimbauer and O. G. Wenisch and K. D. Miller and J. L. {van Hemmen}},
title = {Development of spatiotemporal receptive fields of simple cells: I.
Model formulation},
journal = {Biol. Cybern.},
year = {1997},
volume = {77},
pages = {453--461},
annote = {visual cortex, V1, development, spatiotemporal receptive fields,
simple cells},
keywords = {Vision}
}
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author = {Laurenz Wiskott},
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booktitle = {23 Problems in Systems Neuroscience},
publisher = {Oxford University Press},
year = {2005},
editor = {J. L. {van Hemmen} and T. J. Sejnowski},
keywords = {vision, Vision-Models, invariance learning, slowness},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://itb.biologie.hu-berlin.de/~wiskott/Abstracts/Wisk2005a.html},
urlpaper = {http://itb.biologie.hu-berlin.de/~wiskott/Publications/Wisk2003a-Invariances-CogPrints.pdf},
urlpaper2 = {http://itb.biologie.hu-berlin.de/~wiskott/Publications/Wisk2003a-Invariances-CogPrints.ps.gz},
urlpaper3 = {http://cogprints.org/3321/01/Wiskott2003.pdf},
urlpaper4 = {http://cogprints.org/3321/02/Wiskott2003.ps}
}
@inproceedings{Wiskott98a,
author = {L. Wiskott},
title = {Learning Invariance Manifolds},
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Networks, ICANN'98, Sk{\"o}vde},
year = {1998},
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pages = {555--560},
address = {London},
month = sep,
publisher = {Springer},
isbn = {3-540-76263-9},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@misc{Wiskott03,
author = {L. Wiskott},
title = {{Estimating Driving Forces of Nonstationary Time Series with Slow
Feature Analysis}},
howpublished = {arXiv.org e-Print archive, \url{http://arxiv.org/abs/cond-mat/0312317/}},
month = dec,
year = {2003},
abstract = {Slow feature analysis (SFA) is a new technique for extracting slowly
varying features from a quickly varying signal. It is shown here
that SFA can be applied to nonstationary time series to estimate
a single underlying driving force with high accuracy up to a constant
offset and a factor. Examples with a tent map and a logistic map
illustrate the performance.},
keywords = {driving force, nonlinear time series analysis, nonstationary time
series, slow feature analysis},
owner = {sprekeler},
timestamp = {2008.04.14}
}
@article{Wiskott03a,
author = {Laurenz Wiskott},
title = {Slow Feature Analysis: {A} Theoretical Analysis of Optimal Free Responses},
journal = {Neural Computation},
year = {2003},
volume = {15},
pages = {2147--2177},
number = {9},
month = sep,
abstract = {Temporal slowness is a learning principle that allows learning of
invariant representations by extracting slowly varying features from
quickly varying input signals. Slow feature analysis (SFA) is an
efficient algorithm based on this principle, which has been applied
to the learning of translation, scale, and other invariances in a
simple model of the visual system. Here a theoretical analysis of
the optimization problem solved by SFA is presented, which provides
a deeper understanding of the simulation results obtained in previous
studies.},
keywords = {invariance learning, SFA, slowness},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://itb.biologie.hu-berlin.de/~wiskott/Abstracts/Wisk2003b.html},
urlpaper = {http://itb.biologie.hu-berlin.de/~wiskott/Publications/Wisk2003b-SFATheoryFree-NC.pdf},
urlpaper2 = {http://itb.biologie.hu-berlin.de/~wiskott/Publications/Wisk2003b-SFATheoryFree-NC.ps.gz}
}
@article{Wiskott02,
author = {L. Wiskott and T.J. Sejnowski},
title = {Slow feature analysis: unsupervised learning of invariances},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {715-770}
}
@article{Wiskott02a,
author = {Laurenz Wiskott and Terrence Sejnowski},
title = {Slow Feature Analysis: Unsupervised Learning of Invariances},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {715--770},
number = {4},
abstract = {Invariant features of temporally varying signals are useful for analysis
and classification. Slow feature analysis (SFA) is a new method for
learning invariant or slowly varying features from a vectorial input
signal. SFA is based on a non-linear expansion of the input signal
and application of principal component analysis to this expanded
signal and its time derivative. It is guaranteed to find the optimal
solution within a family of functions directly and can learn to extract
a large number of decorrelated features, which are ordered by their
degree of invariance. SFA can be applied hierarchically to process
high dimensional input signals and to extract complex features. Slow
feature analysis is applied first to complex cell tuning properties
based on simple cell output including disparity and motion. Then,
more complicated input-output functions are learned by repeated application
of SFA. Finally, a hierarchical network of SFA-modules is presented
as a simple model of the visual system. The same unstructured network
can learn translation, size, rotation, contrast, or, to a lesser
degree, illumination invariance for one-dimensional objects, depending
only on the training stimulus. Surprisingly, only a few training
objects sufficed to achieve good generalization to new objects. The
generated representation is suitable for object recognition. Performance
degrades, if the network is trained to learn multiple invariances
simultaneously.},
keywords = {invariance learning, SFA, slowness, Vision-Models, vision},
owner = {sprekeler},
timestamp = {2008.04.14},
urlabstract = {http://itb.biologie.hu-berlin.de/~wiskott/Abstracts/WisSej2002.html},
urlpaper = {http://itb.biologie.hu-berlin.de/~wiskott/Publications/WisSej2002-LearningInvariances-NC.ps.gz}
}
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timestamp = {2008.04.14}
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timestamp = {2008.04.14}
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owner = {sprekeler},
timestamp = {2008.04.14}
}
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abstract = {Stable expression of long-term synaptic plasticity is critical for
the developmental refinement of neural circuits and for some forms
of learning and memory. Although structural remodeling of dendritic
spines is associated with the stable expression of long-term potentiation
(LTP), the relationship between structural and physiological plasticity
remains unclear. To define whether these two processes are related
or distinct, we simultaneously monitored EPSPs and dendritic spines,
using combined patch-clamp recording and two-photon time-lapse imaging
in the same CA1 pyramidal neurons in acute hippocampal slices. We
found that theta burst stimulation paired with postsynaptic spiking,
which reliably induced LTP, also induced a rapid and persistent expansion
of dendritic spines. Like LTP, this expansion was NMDA receptor dependent.
Spine expansion occurred even when LTP was inhibited by postsynaptic
inhibition of exocytosis or PKA (protein kinase A); however, under
these conditions, the spine expansion was unstable and collapsed
spontaneously. Furthermore, similar changes in LTP and spine expansion
were observed when hippocampal neurons were treated with protein
synthesis inhibitors. Like LTP, spine expansion was reversed by low-frequency
stimulation (LFS) via a phosphatase-dependent mechanism, but only
if the LFS was applied in a critical time window after induction.
These results indicate that the initial expression of LTP and spine
expansion is dissociable, but there is a high degree of mechanistic
overlap between the stabilization of structural plasticity and LTP.},
doi = {10.1523/JNEUROSCI.3998-07.2008},
owner = {sprekeler},
pii = {28/22/5740},
pmid = {18509035},
timestamp = {2008.06.03},
url = {http://dx.doi.org/10.1523/JNEUROSCI.3998-07.2008}
}
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keywords = {Plasticity, Vision, Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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in visual cortical orientation tuning}},
journal = {PNAS},
year = {2004},
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pages = {5081--5086},
number = {14},
keywords = {Vision, Vision-Physiology,Plasticity},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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year = {2007},
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pages = {772--778},
keywords = {Vision,Vision-Physiology},
owner = {sprekeler},
timestamp = {2008.04.14}
}
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owner = {sprekeler},
timestamp = {2008.04.14}
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keywords = {vision, vision-physiology, vision-models},
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