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@article{Albert08,
abstract = {Patterns of spontaneous activity in the developing retina, LGN, and
cortex are necessary for the proper development of visual cortex.
With these patterns intact, the primary visual cortices of many newborn
animals develop properties similar to those of the adult cortex but
without the training benefit of visual experience. Previous models
have demonstrated how V1 responses can be initialized through mechanisms
specific to development and prior to visual experience, such as using
axonal guidance cues or relying on simple, pairwise correlations
on spontaneous activity with additional developmental constraints.
We argue that these spontaneous patterns may be better understood
as part of an {\"}innate learning{\"} strategy, which learns similarly
on activity both before and during visual experience. With an abstraction
of spontaneous activity models, we show how the visual system may
be able to bootstrap an efficient code for its natural environment
prior to external visual experience, and we continue the same refinement
strategy upon natural experience. The patterns are generated through
simple, local interactions and contain the same relevant statistical
properties of retinal waves and hypothesized waves in the LGN and
V1. An efficient encoding of these patterns resembles a sparse coding
of natural images by producing neurons with localized, oriented,
bandpass structure-the same code found in early visual cortical cells.
We address the relevance of higher-order statistical properties of
spontaneous activity, how this relates to a system that may adapt
similarly on activity prior to and during natural experience, and
how these concepts ultimately relate to an efficient coding of our
natural world.},
author = {Mark V Albert and Adam Schnabel and David J Field},
doi = {10.1371/journal.pcbi.1000137},
institution = {Field of Computational Biology, Cornell University, Ithaca, New York, United States of America.},
journal = {PLoS Comput Biol},
keywords = {Action Potentials; Animals; Developmental Biology; Evoked Potentials, Visual; Fetal Organ Maturity; Geniculate Bodies; Humans; Information Theory; Learning; Models, Neurological; Nerve Net; Neuronal Plasticity; Neurons; Pattern Recognition, Visual; Photic Stimulation; Retinal Ganglion Cells; Stochastic Processes; Synaptic Transmission; Visual Cortex; Visual Fields; Visual Pathways},
number = {8},
owner = {sprekeler},
pages = {e1000137},
pmid = {18670593},
timestamp = {2009.07.07},
title = {Innate visual learning through spontaneous activity patterns.},
url = {http://dx.doi.org/10.1371/journal.pcbi.1000137},
volume = {4},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1371/journal.pcbi.1000137}
}
@article{Almeida05,
author = {Almeida, L.B.},
journal = {The Journal of Machine Learning Research},
keywords = {ICA},
pages = {1199--1229},
publisher = {MIT Press Cambridge, MA, USA},
title = {{Separating a Real-Life Nonlinear Image Mixture}},
volume = {6},
year = {2005}
}
@article{Almeida03,
author = {Almeida, L.B.},
journal = {The Journal of Machine Learning Research},
keywords = {ICA},
pages = {1297--1318},
publisher = {MIT Press Cambridge, MA, USA},
title = {{MISEP{\'o}linear and nonlinear ICA based on mutual information}},
volume = {4},
year = {2003}
}
@article{Aviel03,
abstract = {We investigate the formation of synfire waves in a balanced network
of integrate-and-fire neurons. The synaptic connectivity of this
network embodies synfire chains within a sparse random connectivity.
This network can exhibit global oscillations but can also operate
in an asynchronous activity mode. We analyze the correlations of
two neurons in a pool as convenient indicators for the state of the
network. We find, using different models, that these indicators depend
on a scaling variable. Beyond a critical point, strong correlations
and large network oscillations are obtained. We looked for the conditions
under which a synfire wave could be propagated on top of an otherwise
asynchronous state of the network. This condition was found to be
highly restrictive, requiring a large number of neurons for its implementation
in our network. The results are based on analytic derivations and
simulations.},
author = {Y. Aviel and C. Mehring and M. Abeles and D. Horn},
doi = {10.1162/089976603321780290},
institution = {Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel. aviel@cc.huji.ac.il},
journal = {Neural Comput},
keywords = {Computer Simulation; Models, Neurological; Neural Pathways; Neurons},
month = {Jun},
number = {6},
owner = {gerstner},
pages = {1321--1340},
pmid = {12816575},
timestamp = {2008.07.14},
title = {On embedding synfire chains in a balanced network.},
url = {http://dx.doi.org/10.1162/089976603321780290},
volume = {15},
year = {2003},
bdsk-url-1 = {http://dx.doi.org/10.1162/089976603321780290}
}
@article{Badoual06,
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.},
author = {Mathilde Badoual and Quan Zou and Andrew P Davison and Michael Rudolph and Thierry Bal and Yves Fr?gnac and Alain Destexhe},
institution = {Integrative and Computational Neuroscience Unit (UNIC), CNRS, Gif-sur-Yvette, France.},
journal = {Int J Neural Syst},
keywords = {Action Potentials; Animals; Biophysics; Computer Simulation; Models, Neurological; Neuronal Plasticity; Neurons; Synapses},
month = {Apr},
number = {2},
owner = {cmellier},
pages = {79--97},
pii = {S0129065706000524},
pmid = {16688849},
timestamp = {2008.05.08},
title = {Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity.},
volume = {16},
year = {2006}
}
@article{Bell97b,
author = {Anthony J. Bell and Terrence J. Sejnowski},
journal = {Vision Research},
keywords = {ICA, vision, Vision-Models, Optimal-Coding},
owner = {sprekeler},
pages = {3327--3338},
timestamp = {2008.04.14},
title = {The 'Independent Components' of Natural Scenes are Edge Filters},
volume = {37},
year = {1997}
}
@article{Bell95a,
author = {Anthony J. Bell and Terrence J. Sejnowski},
journal = {Neural Computation},
keywords = {ICA},
number = {6},
owner = {sprekeler},
pages = {1129--1159},
timestamp = {2008.04.14},
title = {An information maximisation approach to blind separation and blind deconvolution},
volume = {7},
year = {1995}
}
@article{Belouchrani97,
author = {Adel Belouchrani and Karim Abed-Meraim and Jean-Francois Cardoso and Eric Moulines},
journal = {IEEE Transactions on Signal Processing},
keywords = {ICA},
owner = {sprekeler},
pages = {434--444},
timestamp = {2008.04.14},
title = {A blind source separation technique using second order statistics},
volume = {45},
year = {1997}
}
@article{Birdwell07,
abstract = {Rats use active, rhythmic movements of their whiskers to acquire tactile
information about three-dimensional object features. There are no
receptors along the length of the whisker; therefore all tactile
information must be mechanically transduced back to receptors at
the whisker base. This raises the question: how might the rat determine
the radial contact position of an object along the whisker? We developed
two complementary biomechanical models that show that the rat could
determine radial object distance by monitoring the rate of change
of moment (or equivalently, the rate of change of curvature) at the
whisker base. The first model is used to explore the effects of taper
and inherent whisker curvature on whisker deformation and used to
predict the shapes of real rat whiskers during deflections at different
radial distances. Predicted shapes closely matched experimental measurements.
The second model describes the relationship between radial object
distance and the rate of change of moment at the base of a tapered,
inherently curved whisker. Together, these models can account for
recent recordings showing that some trigeminal ganglion (Vg) neurons
encode closer radial distances with increased firing rates. The models
also suggest that four and only four physical variables at the whisker
base -- angular position, angular velocity, moment, and rate of change
of moment -- are needed to describe the dynamic state of a whisker.
We interpret these results in the context of our evolving hypothesis
that neural responses in Vg can be represented using a state-encoding
scheme that includes combinations of these four variables.},
author = {J. Alexander Birdwell and Joseph H Solomon and Montakan Thajchayapong and Michael A Taylor and Matthew Cheely and R. Blythe Towal and Jorg Conradt and Mitra J Z Hartmann},
doi = {10.1152/jn.00707.2006},
journal = {J Neurophysiol},
keywords = {Algorithms; Animals; Biomechanics; Computer Simulation; Elasticity; Female; Models, Neurological; Neural Pathways; Rats; Rats, Sprague-Dawley; Vibrissae},
month = {Oct},
number = {4},
owner = {tomm},
pages = {2439--2455},
pii = {00707.2006},
pmid = {17553946},
timestamp = {2008.06.20},
title = {Biomechanical models for radial distance determination by the rat vibrissal system.},
url = {http://dx.doi.org/10.1152/jn.00707.2006},
volume = {98},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00707.2006}
}
@article{Blaschke06,
author = {Blaschke, Tobias and Berkes, Pietro and Wiskott, Laurenz},
journal = {Neural Computation},
keywords = {ICA, slowness},
number = {10},
owner = {sprekeler},
pages = {2495--2508},
timestamp = {2008.04.14},
title = {What is the relation between Slow Feature Analysis and Independent Component Analysis?},
volume = {18},
year = {2006}
}
@article{Blaschke04,
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.},
author = {T. Blaschke and L. Wiskott},
journal = {IEEE Transactions on Signal Processing},
keywords = {ICA},
month = may,
number = {5},
owner = {sprekeler},
pages = {1250--1256},
timestamp = {2008.04.14},
title = {{CuBICA}: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization},
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},
volume = {52},
year = {2004}
}
@article{Blaschke07,
author = {Blaschke, Tobias and Zito, Tiziano and Wiskott, Laurenz},
journal = {Neural Computation},
keywords = {slowness,ICA},
number = {4},
owner = {sprekeler},
pages = {994-1021},
timestamp = {2008.04.14},
title = {Independent Slow Feature Analysis and Nonlinear Blind Source Separation},
volume = {19},
year = {2007}
}
@article{Boucheny05,
abstract = {Motivated by experimental observations of the head direction system,
we study a three population network model that operates as a continuous
attractor network. This network is able to store in a short-term
memory an angular variable (the head direction) as a spatial profile
of activity across neurons in the absence of selective external inputs,
and to accurately update this variable on the basis of angular velocity
inputs. The network is composed of one excitatory population and
two inhibitory populations, with inter-connections between populations
but no connections within the neurons of a same population. In particular,
there are no excitatory-to-excitatory connections. Angular velocity
signals are represented as inputs in one inhibitory population (clockwise
turns) or the other (counterclockwise turns). The system is studied
using a combination of analytical and numerical methods. Analysis
of a simplified model composed of threshold-linear neurons gives
the conditions on the connectivity for (i) the emergence of the spatially
selective profile, (ii) reliable integration of angular velocity
inputs, and (iii) the range of angular velocities that can be accurately
integrated by the model. Numerical simulations allow us to study
the proposed scenario in a large network of spiking neurons and compare
their dynamics with that of head direction cells recorded in the
rat limbic system. In particular, we show that the directional representation
encoded by the attractor network can be rapidly updated by external
cues, consistent with the very short update latencies observed experimentally
by Zugaro et al. (2003) in thalamic head direction cells.},
author = {Christian Boucheny and Nicolas Brunel and Angelo Arleo},
doi = {10827-005-6559-y},
institution = {Laboratory of Physiology of Perception and Action, CNRS-Coll?ge de France, 11 pl. M. Berthelot, 75005, Paris, France.},
journal = {J Comput Neurosci},
keywords = {Action Potentials; Animals; Head Movements; Models, Neurological; Motion Percep; Nerve Net; Neural Networks (Computer); Neural Pathways; Space Perception; Systems Theory; Thalamus; Time Factors; tion},
number = {2},
owner = {gerstner},
pages = {205--227},
pmid = {15714270},
timestamp = {2008.07.07},
title = {A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system.},
url = {http://dx.doi.org/10827-005-6559-y},
volume = {18},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10827-005-6559-y}
}
@article{Brumberg99,
abstract = {Controlled whisker stimulation and single-unit recordings were used
to elucidate response transformations that occur during the processing
of tactile information from ventral posterior medial thalamus (VPM)
through cortical columns in the rat whisker/barrel cortex. Whiskers
were either deflected alone, using punctate ramp-and-hold stimuli,
or in combination with a random noise vibration applied simultaneously
to two or more neighboring whiskers. Quantitative data were obtained
from five anatomically defined groups of neurons based on their being
located in: VPM, layer IV barrels, layer IV septa, supragranular
laminae, and infragranular laminae. Neurons in each of these populations
displayed characteristic properties related to their response latency
and time course, relative magnitudes of responses evoked by stimulus
onset versus offset, strength of excitatory responses evoked by the
noise stimulus, and/or the degree to which the noise stimulus, when
applied to neighboring whiskers, suppressed or facilitated responses
evoked by the columnar whisker. Results indicate that within layer
IV itself there are at least two anatomically distinct networks,
barrel and septum, that independently process afferent information,
transforming thalamic input in similar but quantitatively distinguishable
ways. Transformed signals are passed on to circuits in supragranular
and infragranular laminae. In the case of supragranular neurons,
evidence suggests that circuits there function in a qualitatively
different fashion from those in layer IV, diminishing response differentials
between weak and strong inputs, rather than enhancing them. Compared
to layer IV, the greater heterogeneity of receptive field properties
in nongranular layers suggests the existence of multiple, operationally
distinct local circuits in the output layers of the cortical column.},
author = {J. C. Brumberg and D. J. Pinto and D. J. Simons},
journal = {J Neurophysiol},
keywords = {Animals; Electric Stimulation; Evoked Potentials, Somatosensory; Female; Neural Pathways; Neurons; Physical Stimulation; Rats; Rats, Sprague-Dawley; Somatosensory Cortex; Thalamus; Touch; Vibrissae},
month = {Oct},
number = {4},
owner = {tomm},
pages = {1808--1817},
pmid = {10515970},
timestamp = {2008.12.31},
title = {Cortical columnar processing in the rat whisker-to-barrel system.},
volume = {82},
year = {1999}
}
@article{Buia06,
abstract = {The response of a neuron in the visual cortex to stimuli of different
contrast placed in its receptive field is commonly characterized
using the contrast response curve. When attention is directed into
the receptive field of a V4 neuron, its contrast response curve is
shifted to lower contrast values (Reynolds et al., 2000). The neuron
will thus be able to respond to weaker stimuli than it responded
to without attention. Attention also increases the coherence between
neurons responding to the same stimulus (Fries et al., 2001). We
studied how the firing rate and synchrony of a densely interconnected
cortical network varied with contrast and how they were modulated
by attention. The changes in contrast and attention were modeled
as changes in driving current to the network neurons.We found that
an increased driving current to the excitatory neurons increased
the overall firing rate of the network, whereas variation of the
driving current to inhibitory neurons modulated the synchrony of
the network. We explain the synchrony modulation in terms of a locking
phenomenon during which the ratio of excitatory to inhibitory firing
rates is approximately constant for a range of driving current values.We
explored the hypothesis that contrast is represented primarily as
a drive to the excitatory neurons, whereas attention corresponds
to a reduction in driving current to the inhibitory neurons. Using
this hypothesis, the model reproduces the following experimental
observations: (1) the firing rate of the excitatory neurons increases
with contrast; (2) for high contrast stimuli, the firing rate saturates
and the network synchronizes; (3) attention shifts the contrast response
curve to lower contrast values; (4) attention leads to stronger synchronization
that starts at a lower value of the contrast compared with the attend-away
condition. In addition, it predicts that attention increases the
delay between the inhibitory and excitatory synchronous volleys produced
by the network, allowing the stimulus to recruit more downstream
neurons.},
author = {Calin Buia and Paul Tiesinga},
doi = {10.1007/s10827-006-6358-0},
institution = {Astronomy, University of North Carolina at Chapel Hill, Campus Box 3255, Chapel Hill, North Carolina 27599, USA. buia@physics.unc.edu},
journal = {J Comput Neurosci},
keywords = {Action Potentials; Animals; Attention; Cats; Contrast Sensitivity; Cortical Synchronization; Excitatory Postsynaptic Potentials; Humans; Models, Neuro; Nerve Net; Neural Inhibition; Neural Pathways; Neurons; Rats; Reaction Time; Synaptic Transmission; Time Factors; Visual Cortex; logical},
month = {Jun},
number = {3},
owner = {gerstner},
pages = {247--264},
pmid = {16683206},
timestamp = {2008.07.07},
title = {Attentional modulation of firing rate and synchrony in a model cortical network.},
url = {http://dx.doi.org/10.1007/s10827-006-6358-0},
volume = {20},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1007/s10827-006-6358-0}
}
@article{Burguiere05,
abstract = {L7-PKCI transgenic mice, which lack parallel fiber-Purkinje cell long-term
depression (LTD), were tested with two different mazes to dissociate
the relative importance of declarative and procedural components
of spatial navigation. We show that L7-PKCI mice are deficient in
acquisition of an adapted goal-oriented behavior, part of the procedural
component of the task. This supports the hypothesis that cerebellar
LTD may subserve a general sensorimotor adaptation process shared
by motor and spatial learning functions.},
author = {Eric Burgui?re and Angelo Arleo and Mohammad reza Hojjati and Ype Elgersma and Chris I De Zeeuw and Alain Berthoz and Laure Rondi-Reig},
doi = {10.1038/nn1532},
institution = {Laboratoire de Physiologie de la Perception et de l'Action, UMR CNRS 7152, 11 place Marcelin Berthelot, Coll?ge de France, 75005 Paris, France.},
journal = {Nat Neurosci},
keywords = {Adaptation, Physiological; Analysis of Variance; Animals; Behavior, Animal; Cerebellum; Escape Reaction; Long-Term Synaptic Depression; Maze Learning; Mice; Mice, Inbred C57BL; Mice, Transgenic; Perceptual Disorders; Protein Kinase C; Psychomotor Performance; Purkinje Cells; Reaction Time; Spatial Behavior; Time Factors},
month = {Oct},
number = {10},
owner = {gerstner},
pages = {1292--1294},
pii = {nn1532},
pmid = {16136042},
timestamp = {2008.07.09},
title = {Spatial navigation impairment in mice lacking cerebellar LTD: a motor adaptation deficit?},
url = {http://dx.doi.org/10.1038/nn1532},
volume = {8},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1038/nn1532}
}
@article{Burkitt07,
abstract = {The dynamics of the learning equation, which describes the evolution
of the synaptic weights, is derived in the situation where the network
contains recurrent connections. The derivation is carried out for
the Poisson neuron model. The spiking-rates of the recurrently connected
neurons and their cross-correlations are determined self- consistently
as a function of the external synaptic inputs. The solution of the
learning equation is illustrated by the analysis of the particular
case in which there is no external synaptic input. The general learning
equation and the fixed-point structure of its solutions is discussed.},
author = {A. N. Burkitt and M. Gilson and J. L. van Hemmen},
doi = {10.1007/s00422-007-0148-2},
institution = {ia. aburkitt@bionicear.org},
journal = {Biol Cybern},
keywords = {Cell Communication; Humans; Learning; Mathematics; Models, Neurological; Neuronal Plasticity; Neurons; Poisson Distribution; Synapses},
month = {May},
number = {5},
owner = {gerstner},
pages = {533--546},
pmid = {17415586},
timestamp = {2008.07.09},
title = {Spike-timing-dependent plasticity for neurons with recurrent connections.},
url = {http://dx.doi.org/10.1007/s00422-007-0148-2},
volume = {96},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1007/s00422-007-0148-2}
}
@article{Burkitt03c,
abstract = {Spike-timing-dependent synaptic plasticity has recently provided an
account of both the acuity of sound localization and the development
of temporal-feature maps in the avian auditory system. The dynamics
of the resulting learning equation, which describes the evolution
of the synaptic weights, is governed by an unstable fixed point.
We outline the derivation of the learning equation for both the Poisson
neuron model and the leaky integrate-and-fire neuron with conductance
synapses. The asymptotic solutions of the learning equation can be
described by a spectral representation based on a biorthogonal expansion.},
author = {A. N. Burkitt and J. L. van Hemmen},
doi = {10.1007/s00422-003-0437-3},
institution = {The Bionic Ear Institute, 384-388 Albert Street, Vic 3002, East Melbourne, Australia. aburkitt@bionicear.org},
journal = {Biol Cybern},
keywords = {Action Potentials; Animals; Auditory Perception; Evoked Potentials, Auditory; Excitatory Postsynaptic Potentials; Humans; Models, Neurological; Neuronal Plasticity; Neurons; Strigiformes; Synapses; Synaptic Transmission},
month = {Nov},
number = {5},
owner = {gerstner},
pages = {318--332},
pmid = {14669012},
timestamp = {2008.07.09},
title = {How synapses in the auditory system wax and wane: theoretical perspectives.},
url = {http://dx.doi.org/10.1007/s00422-003-0437-3},
volume = {89},
year = {2003},
bdsk-url-1 = {http://dx.doi.org/10.1007/s00422-003-0437-3}
}
@article{Cateau06,
abstract = {To simplify theoretical analyses of neural networks, individual neurons
are often modeled as Poisson processes. An implicit assumption is
that even if the spiking activity of each neuron is non-Poissonian,
the composite activity obtained by summing many spike trains limits
to a Poisson process. Here, we show analytically and through simulations
that this assumption is invalid. Moreover, we show with Fokker-Planck
equations that the behavior of feedforward networks is reproduced
accurately only if the tendency of neurons to fire periodically is
incorporated by using colored noise whose autocorrelation has a negative
component.},
author = {Hideyuki C\^ateau and Alex D Reyes},
institution = {Center for Neural Science, New York University, 4 Washington Place, New York, New York 10003, USA.},
journal = {Phys Rev Lett},
keywords = {Action Potentials; Algorithms; Animals; Computer Simulation; Humans; Models, Neurological; Nerve Net; Neurons},
month = {Feb},
number = {5},
owner = {gerstner},
pages = {058101},
pmid = {16486995},
timestamp = {2008.07.09},
title = {Relation between single neuron and population spiking statistics and effects on network activity.},
volume = {96},
year = {2006}
}
@article{Carandini97b,
abstract = {Simple cells in the primary visual cortex often appear to compute
a weighted sum of the light intensity distribution of the visual
stimuli that fall on their receptive fields. A linear model of these
cells has the advantage of simplicity and captures a number of basic
aspects of cell function. It, however, fails to account for important
response nonlinearities, such as the decrease in response gain and
latency observed at high contrasts and the effects of masking by
stimuli that fail to elicit responses when presented alone. To account
for these nonlinearities we have proposed a normalization model,
which extends the linear model to include mutual shunting inhibition
among a large number of cortical cells. Shunting inhibition is divisive,
and its effect in the model is to normalize the linear responses
by a measure of stimulus energy. To test this model we performed
extracellular recordings of simple cells in the primary visual cortex
of anesthetized macaques. We presented large stimulus sets consisting
of (1) drifting gratings of various orientations and spatiotemporal
frequencies; (2) plaids composed of two drifting gratings; and (3)
gratings masked by full-screen spatiotemporal white noise. We derived
expressions for the model predictions and fitted them to the physiological
data. Our results support the normalization model, which accounts
for both the linear and the nonlinear properties of the cells. An
alternative model, in which the linear responses are subject to a
compressive nonlinearity, did not perform nearly as well.},
author = {M. Carandini and D. J. Heeger and J. A. Movshon},
institution = {Howard Hughes Medical Institute and Center for Neural Science, New York University, New York, New York 10003, USA.},
journal = {J Neurosci},
keywords = {Animals; Macaca fascicularis; Macaca nemestrina; Models, Neurological; Nonlinear Dynamics; Photic Stimulation; Reaction Time; Visual Cortex; Visual Perception},
month = {Nov},
number = {21},
owner = {gerstner},
pages = {8621--8644},
pmid = {9334433},
timestamp = {2008.07.09},
title = {Linearity and normalization in simple cells of the macaque primary visual cortex.},
volume = {17},
year = {1997}
}
@article{Chacron05,
abstract = {Feedback circuitry with conduction and synaptic delays is ubiquitous
in the nervous system. Yet the effects of delayed feedback on sensory
processing of natural signals are poorly understood. This study explores
the consequences of delayed excitatory and inhibitory feedback inputs
on the processing of sensory information. We show, through numerical
simulations and theory, that excitatory and inhibitory feedback can
alter the firing frequency response of stochastic neurons in opposite
ways by creating dynamical resonances, which in turn lead to information
resonances (i.e., increased information transfer for specific ranges
of input frequencies). The resonances are created at the expense
of decreased information transfer in other frequency ranges. Using
linear response theory for stochastically firing neurons, we explain
how feedback signals shape the neural transfer function for a single
neuron as a function of network size. We also find that balanced
excitatory and inhibitory feedback can further enhance information
tuning while maintaining a constant mean firing rate. Finally, we
apply this theory to in vivo experimental data from weakly electric
fish in which the feedback loop can be opened. We show that it qualitatively
predicts the observed effects of inhibitory feedback. Our study of
feedback excitation and inhibition reveals a possible mechanism by
which optimal processing may be achieved over selected frequency
ranges.},
author = {Maurice J Chacron and Andr? Longtin and Leonard Maler},
institution = {Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, Canada K1N 6N5.},
journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
keywords = {Action Potentials; Animals; Biological Clocks; Computer Simulation; Elect; Electric Organ; Excitatory Postsynaptic Potentials; Feedback; Information Storage and Retrieval; Models, Neurological; Nerve Net; Neural Inhibition; Neurons, Afferent; Sense Organs; Time Factors; ric Fish},
month = {Nov},
number = {5 Pt 1},
owner = {gerstner},
pages = {051917},
pmid = {16383655},
timestamp = {2008.07.09},
title = {Delayed excitatory and inhibitory feedback shape neural information transmission.},
volume = {72},
year = {2005}
}
@article{Coombes01,
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.},
author = {S. Coombes and M. R. Owen and G. D. Smith},
institution = {Department of Mathematical Sciences, Loughborough University, Leicestershire LE11 3TU, United Kingdom.},
journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
keywords = {Action Potentials; Animals; Calcium; Humans; Models, Neurological; Models, Statistical; Models, Theoretical; Neurons; Oscillometry; Synaptic Transmission; Time Factors},
month = {Oct},
number = {4 Pt 1},
owner = {gerstner},
pages = {041914},
pmid = {11690059},
timestamp = {2008.05.28},
title = {Mode locking in a periodically forced integrate-and-fire-or-burst neuron model.},
volume = {64},
year = {2001}
}
@article{Corchs02,
abstract = {A computational neuroscience framework is proposed to better understand
the role and the neuronal correlate of spatial attention modulation
in visual perception. The model consists of several interconnected
modules that can be related to the different areas of the dorsal
and ventral paths of the visual cortex. Competitive neural interactions
are implemented at both microscopic and interareal levels, according
to the biased competition hypothesis. This hypothesis has been experimentally
confirmed in studies in humans using functional magnetic resonance
imaging (fMRI) techniques and also in single-cell recording studies
in monkeys. Within this neuro-dynamical approach, numerical simulations
are carried out that describe both the fMRI and the electrophysiological
data. The proposed model draws together data of different spatial
and temporal resolution, as are the above-mentioned imaging and single-cell
results.},
author = {Silvia Corchs and Gustavo Deco},
institution = {Siemens AG, Corporate Technology, Information and Communications, CT IC 4, Otto-Hahn-Ring 6, 81739 Munich, Germany.},
journal = {Cereb Cortex},
keywords = {Algorithms; Attention; Cognition; Computer Simulation; Electrophysiology; Humans; Learning; Magnetic Resonance Imaging; Models, Neurological; Neurons; Prefrontal Cortex; Vision; Visual Cortex; Visual Pathways; Visual Perception},
month = {Apr},
number = {4},
owner = {gerstner},
pages = {339--348},
pmid = {11884349},
timestamp = {2008.07.09},
title = {Large-scale neural model for visual attention: integration of experimental single-cell and fMRI data.},
volume = {12},
year = {2002}
}
@article{Davison06,
abstract = {A common problem in tasks involving the integration of spatial information
from multiple senses, or in sensorimotor coordination, is that different
modalities represent space in different frames of reference. Coordinate
transformations between different reference frames are therefore
required. One way to achieve this relies on the encoding of spatial
information with population codes. The set of network responses to
stimuli in different locations (tuning curves) constitutes a set
of basis functions that can be combined linearly through weighted
synaptic connections to approximate nonlinear transformations of
the input variables. The question then arises: how is the appropriate
synaptic connectivity obtained? Here we show that a network of spiking
neurons can learn the coordinate transformation from one frame of
reference to another, with connectivity that develops continuously
in an unsupervised manner, based only on the correlations available
in the environment and with a biologically realistic plasticity mechanism
(spike timing-dependent plasticity).},
author = {Andrew P Davison and Yves Fr?gnac},
doi = {10.1523/JNEUROSCI.5263-05.2006},
institution = {Unit? de Neurosciences Int?gratives et Computationnelles, Centre National de la Recherche Scientifique, 91198 Gif sur Yvette, France. davison@iaf.cnrs-gif.fr},
journal = {J Neurosci},
keywords = {Action Potentials; Animals; Computer Simulation; Humans; Learning; Models, Neurological; Nerve Net; Neuronal Plasticity; Space Perception; Synapses; Synaptic Transmission; Time Factors},
month = {May},
number = {21},
owner = {cmellier},
pages = {5604--5615},
pii = {26/21/5604},
pmid = {16723517},
timestamp = {2008.07.09},
title = {Learning cross-modal spatial transformations through spike timing-dependent plasticity.},
url = {http://dx.doi.org/10.1523/JNEUROSCI.5263-05.2006},
volume = {26},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.5263-05.2006}
}
@article{Diamond08,
author = {Mathew E Diamond and Moritz von Heimendahl and Ehsan Arabzadeh},
doi = {10.1371/journal.pbio.0060220},
journal = {PLoS Biol},
keywords = {Afferent Pathways; Animals; Mechanoreceptors; Physical Stimulation; Rats; Sensory Thresholds; Touch; Vibration; Vibrissae},
month = {Aug},
number = {8},
owner = {tomm},
pages = {e220},
pii = {08-PLBI-P-2146},
pmid = {18752356},
timestamp = {2009.01.08},
title = {Whisker-mediated texture discrimination.},
url = {http://dx.doi.org/10.1371/journal.pbio.0060220},
volume = {6},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1371/journal.pbio.0060220}
}
@article{Dudman07,
abstract = {Synaptic potentials originating at distal dendritic locations are
severely attenuated when they reach the soma and, thus, are poor
at driving somatic spikes. Nonetheless, distal inputs convey essential
information, suggesting that such inputs may be important for compartmentalized
dendritic signaling. Here we report a new plasticity rule in which
stimulation of distal perforant path inputs to hippocampal CA1 pyramidal
neurons induces long-term potentiation at the CA1 proximal Schaffer
collateral synapses when the two inputs are paired at a precise interval.
This subthreshold form of heterosynaptic plasticity occurs in the
absence of somatic spiking but requires activation of both NMDA receptors
and IP(3) receptor-dependent release of Ca(2+) from internal stores.
Our results suggest that direct sensory information arriving at distal
CA1 synapses through the perforant path provide compartmentalized,
instructive signals that assess the saliency of mnemonic information
propagated through the hippocampal circuit to proximal synapses.},
author = {Joshua T Dudman and David Tsay and Steven A Siegelbaum},
doi = {10.1016/j.neuron.2007.10.020},
institution = {Department of Neuroscience, Columbia University, 1051 Riverside Drive, New York, NY 10032, USA.},
journal = {Neuron},
keywords = {Absorptiometry, Photon; Animals; Calcium; Computer Simulation; Dendrites; Dendritic Spines; Electrophysiology; Excitatory Postsynaptic Potentials; Hippocampus; Inositol 1,4,5-Trisphosphate Receptors; Memory; Mice; Models, Neurological; Neuronal Plasticity; Patch-Clamp Techniques; Pyramidal Cells; Receptors, AMPA; Receptors, N-Methyl-D-Aspartate; Recruitment, Neurophysiological; Signal Transduction; Synapses; Synaptic Transmission},
month = {Dec},
number = {5},
owner = {cmellier},
pages = {866--879},
pii = {S0896-6273(07)00815-X},
pmid = {18054862},
timestamp = {2008.07.09},
title = {A role for synaptic inputs at distal dendrites: instructive signals for hippocampal long-term plasticity.},
url = {http://dx.doi.org/10.1016/j.neuron.2007.10.020},
volume = {56},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.neuron.2007.10.020}
}
@article{Ermentrout07,
abstract = {We demonstrate that two key theoretical objects used widely in computational
neuroscience, the phase-resetting curve (PRC) from dynamics and the
spike triggered average (STA) from statistical analysis, are closely
related when neurons fire in a nearly regular manner and the stimulus
is sufficiently small. We prove that the STA due to injected noisy
current is proportional to the derivative of the PRC. We compare
these analytic results with numerical calculations for the Hodgkin-Huxley
neuron and we apply the method to neurons in the olfactory bulb of
mice. This observation allows us to relate the stimulus-response
properties of a neuron to its dynamics, bridging the gap between
dynamical and information theoretic approaches to understanding brain
computations and facilitating the interpretation of changes in channels
and other cellular properties as influencing the representation of
stimuli.},
author = {G. Bard Ermentrout and Roberto F Gal?n and Nathaniel N Urban},
institution = {University of Pittsburgh, Department of Mathematics, Thackeray Hall, Pittsburgh, Pennsylvania 15260, USA.},
journal = {Phys Rev Lett},
keywords = {Action Potentials; Animals; Models, Neurological; Neurons},
month = {Dec},
number = {24},
owner = {gerstner},
pages = {248103},
pmid = {18233494},
timestamp = {2008.07.09},
title = {Relating neural dynamics to neural coding.},
volume = {99},
year = {2007}
}
@article{Falconbridge06,
author = {M. Falconbridge and R. Stamps and D. Badcock},
journal = {Neural Computation},
keywords = {vision, Vision-Models, sparse coding, ICA},
owner = {sprekeler},
pages = {415-429},
timestamp = {2008.04.14},
title = {A Simple Hebbian/Anti-Hebbian Network Learns the Sparse, Independent Components of Natural Images},
volume = {18},
year = {2006}
}
@article{Fusi07a,
abstract = {Volitional behavior relies on the brain's ability to remap sensory
flow to motor programs whenever demanded by a changed behavioral
context. To investigate the circuit basis of such flexible behavior,
we have developed a biophysically based decision-making network model
of spiking neurons for arbitrary sensorimotor mapping. The model
quantitatively reproduces behavioral and prefrontal single-cell data
from an experiment in which monkeys learn visuomotor associations
that are reversed unpredictably from time to time. We show that when
synaptic modifications occur on multiple timescales, the model behavior
becomes flexible only when needed: slow components of learning usually
dominate the decision process. However, if behavioral contexts change
frequently enough, fast components of plasticity take over, and the
behavior exhibits a quick forget-and-learn pattern. This model prediction
is confirmed by monkey data. Therefore, our work reveals a scenario
for conditional associative learning that is distinct from instant
switching between sets of well-established sensorimotor associations.},
author = {Stefano Fusi and Wael F Asaad and Earl K Miller and Xiao-Jing Wang},
doi = {10.1016/j.neuron.2007.03.017},
institution = {Center for Neurobiology and Behavior, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA.},
journal = {Neuron},
keywords = {Algorithms; Animals; Association Learning; Cues; Decision Making; Haplorhini; Learning; Memory; Mental Recall; Models, Neurological; Neural Networks (Computer); Neurons; Prefrontal Cortex; Psychomotor Performance; Synapses},
month = {Apr},
number = {2},
owner = {gerstner},
pages = {319--333},
pii = {S0896-6273(07)00212-7},
pmid = {17442251},
timestamp = {2008.07.10},
title = {A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales.},
url = {http://dx.doi.org/10.1016/j.neuron.2007.03.017},
volume = {54},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.neuron.2007.03.017}
}
@article{Galan08,
abstract = {Use of spike timing to encode information requires that neurons respond
with high temporal precision and with high reliability. Fast fluctuating
stimuli are known to result in highly reproducible spike times across
trials, whereas constant stimuli result in variable spike times.
Here, we first studied mathematically how spike-time reliability
depends on the rapidness of aperiodic stimuli. Then, we tested our
theoretical predictions in computer simulations of neuron models
(Hodgkin-Huxley and modified quadratic integrate-and-fire), as well
as in patch-clamp experiments with real neurons (mitral cells in
the olfactory bulb and pyramidal cells in the neocortex). As predicted
by our theory, we found that for firing frequencies in the beta/gamma
range, spike-time reliability is maximal when the time scale of the
input fluctuations (autocorrelation time) is in the range of a few
milliseconds (2-5 ms), coinciding with the time scale of fast synapses,
and decreases substantially for faster and slower inputs. Finally,
we comment how these findings relate to mechanisms causing neuronal
synchronization.},
author = {Roberto F Gal?n and G. Bard Ermentrout and Nathaniel N Urban},
doi = {10.1152/jn.00563.2007},
institution = {Department of Biological Sciences, Carnegie Mellon University, Mellon Institute, 4400 Fifth Ave., Pittsburgh, Pennsylvania 15213, USA. galan@cnbc.cmu.edu},
journal = {J Neurophysiol},
keywords = {Action Potentials; Algorithms; Animals; Computer Simulation; Cortical Synchronization; Mice; Models, Neurological; Neocortex; Neural Pathways; Olf; Organ Culture Techniques; Pyramidal Cells; Reaction Time; Synapses; Synaptic Transmission; Time Factors; actory Bulb},
month = {Jan},
number = {1},
owner = {cmellier},
pages = {277--283},
pii = {00563.2007},
pmid = {17928562},
timestamp = {2008.07.10},
title = {Optimal time scale for spike-time reliability: theory, simulations, and experiments.},
url = {http://dx.doi.org/10.1152/jn.00563.2007},
volume = {99},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00563.2007}
}
@article{Gavornik09,
abstract = {The ability to represent time is an essential component of cognition
but its neural basis is unknown. Although extensively studied both
behaviorally and electrophysiologically, a general theoretical framework
describing the elementary neural mechanisms used by the brain to
learn temporal representations is lacking. It is commonly believed
that the underlying cellular mechanisms reside in high order cortical
regions but recent studies show sustained neural activity in primary
sensory cortices that can represent the timing of expected reward.
Here, we show that local cortical networks can learn temporal representations
through a simple framework predicated on reward dependent expression
of synaptic plasticity. We assert that temporal representations are
stored in the lateral synaptic connections between neurons and demonstrate
that reward-modulated plasticity is sufficient to learn these representations.
We implement our model numerically to explain reward-time learning
in the primary visual cortex (V1), demonstrate experimental support,
and suggest additional experimentally verifiable predictions.},
author = {Jeffrey P Gavornik and Marshall G Hussain Shuler and Yonatan Loewenstein and Mark F Bear and Harel Z Shouval},
doi = {10.1073/pnas.0901835106},
institution = {Department of Neurobiology and Anatomy, University of Texas Medical School, Houston, TX 77030, USA.},
journal = {Proc Natl Acad Sci U S A},
keywords = {Action Potentials; Cerebral Cortex; Humans; Learning; Models, Neurological; Nerve Net; Neuronal Plasticity; Neurons; Reward; Stochastic Processes; Time Factors},
month = {Apr},
number = {16},
owner = {sprekeler},
pages = {6826--6831},
pii = {0901835106},
pmid = {19346478},
timestamp = {2009.08.04},
title = {Learning reward timing in cortex through reward dependent expression of synaptic plasticity.},
url = {http://dx.doi.org/10.1073/pnas.0901835106},
volume = {106},
year = {2009},
bdsk-url-1 = {http://dx.doi.org/10.1073/pnas.0901835106}
}
@article{Giugliano04,
author = {M. Giugliano and P. Darbon and M. Arsiero and H-R. L?scher and J. Streit},
doi = {10.1152/jn.00067.2004},
journal = {J Neurophysiol},
keywords = {Action Potentials; Animals; Animals, Newborn; Artifacts; Cell Aging; Cells, Cultured; Computer Simulation; Differential Threshold; Electrophysiology; Microelectrodes; Models, Neurological; Neocortex; Nerve Net; Neurons; Patch-Clamp Techniques; Rats; Rats, Wistar; Reaction Time},
month = {Aug},
number = {2},
owner = {gerstner},
pages = {977--996},
pii = {00067.2004},
pmid = {15044515},
timestamp = {2008.07.10},
title = {Single-neuron discharge properties and network activity in dissociated cultures of neocortex.},
url = {http://dx.doi.org/10.1152/jn.00067.2004},
volume = {92},
year = {2004},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00067.2004}
}
@article{Gold07,
abstract = {We investigate the use of extracellular action potential (EAP) recordings
for biophysically faithful compartmental models. We ask whether constraining
a model to fit the EAP is superior to matching the intracellular
action potential (IAP). In agreement with previous studies, we find
that the IAP method under-constrains the parameters. As a result,
significantly different sets of parameters can have virtually identical
IAP's. In contrast, the EAP method results in a much tighter constraint.
We find that the distinguishing characteristics of the waveform--but
not its amplitude-resulting from the distribution of active conductances
are fairly invariant to changes of electrode position and detailed
cellular morphology. Based on these results, we conclude that EAP
recordings are an excellent source of data for the purpose of constraining
compartmental models.},
author = {Carl Gold and Darrell A Henze and Christof Koch},
doi = {10.1007/s10827-006-0018-2},
institution = {Computation and Neural Systems, California Institute of Technology, Pasadena, CA, USA. carlg@caltech.edu},
journal = {J Comput Neurosci},
keywords = {Action Potentials; Animals; Electric Conductivity; Electric Stimulation; Hippocampus; Models, Neurological; Pyramidal Cells; Time Factors},
month = {Aug},
number = {1},
owner = {gerstner},
pages = {39--58},
pmid = {17273940},
timestamp = {2008.07.14},
title = {Using extracellular action potential recordings to constrain compartmental models.},
url = {http://dx.doi.org/10.1007/s10827-006-0018-2},
volume = {23},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1007/s10827-006-0018-2}
}
@article{Gollisch08,
abstract = {Natural vision is a highly dynamic process. Frequent body, head, and
eye movements constantly bring new images onto the retina for brief
periods, challenging our understanding of the neural code for vision.
We report that certain retinal ganglion cells encode the spatial
structure of a briefly presented image in the relative timing of
their first spikes. This code is found to be largely invariant to
stimulus contrast and robust to noisy fluctuations in response latencies.
Mechanistically, the observed response characteristics result from
different kinetics in two retinal pathways ({\"}ON{\"} and {\"}OFF{\"}) that
converge onto ganglion cells. This mechanism allows the retina to
rapidly and reliably transmit new spatial information with the very
first spikes emitted by a neural population.},
author = {T. Gollisch and M. Meister},
doi = {10.1126/science.1149639},
institution = {Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA.},
journal = {Science},
keywords = {Action Potentials; Ambystoma; Animals; Kinetics; Models, Neurological; Photic Stimulation; Reaction Time; Retinal Bipolar Cells; Retinal Ganglion Cells; Saccades; Synapses; Vision; Visual Pathways},
month = {Feb},
number = {5866},
owner = {cmellier},
pages = {1108--1111},
pii = {319/5866/1108},
pmid = {18292344},
timestamp = {2008.07.30},
title = {Rapid neural coding in the retina with relative spike latencies.},
url = {http://dx.doi.org/10.1126/science.1149639},
volume = {319},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1126/science.1149639}
}
@article{Goulet07,
abstract = {Fish acquire information about their aquatic environment by means
of their mechanosensory lateral-line system. This system consists
of superficial and canal neuromasts that sense perturbations in the
water surrounding them. Based on a hydrodynamic model presented here,
we propose a mechanism through which fish can localize the source
of these perturbations. In doing so we include the curvature of the
fish body, a realistic lateral line canal inter-pore distance for
the lateral-line canals, and the surface boundary layer. Using our
model to explore receptor behavior based on experimental data of
responses to dipole stimuli we suggest that superficial and canal
neuromasts employ the same mechanism, hence provide the same type
of input to the central nervous system. The analytical predictions
agree well with spiking responses recorded experimentally from primary
lateral-line nerve fibers. From this, and taking into account the
central organization of the lateral-line system, we present a simple
biophysical model for determining the distance to a source.},
author = {Julie Goulet and Jacob Engelmann and Boris P Chagnaud and Jan-Moritz P Franosch and Maria D Suttner and J. Leo van Hemmen},
doi = {10.1007/s00359-007-0275-1},
institution = {Physik Department T35, TU M?nchen and Bernstein Center for Computational Neuroscience, 85747 Garching bei M?nchen, Germany. julie@ph.tum.de},
journal = {J Comp Physiol A Neuroethol Sens Neural Behav Physiol},
keywords = {Algorithms; Animals; Computer Simulation; Evoked Potentials; Fishes; Lateral Line System; Models, Biological; Receptors, Sensory; Space Perception; Water Movements},
month = {Jan},
number = {1},
owner = {gerstner},
pages = {1--17},
pmid = {18060550},
timestamp = {2008.07.14},
title = {Object localization through the lateral line system of fish: theory and experiment.},
url = {http://dx.doi.org/10.1007/s00359-007-0275-1},
volume = {194},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1007/s00359-007-0275-1}
}
@article{GrossbergConsc99,
abstract = {The processes whereby our brains continue to learn about a changing
world in a stable fashion throughout life are proposed to lead to
conscious experiences. These processes include the learning of top-down
expectations, the matching of these expectations against bottom-up
data, the focusing of attention upon the expected clusters of information,
and the development of resonant states between bottom-up and top-down
processes as they reach an attentive consensus between what is expected
and what is there in the outside world. It is suggested that all
conscious states in the brain are resonant states and that these
resonant states trigger learning of sensory and cognitive representations.
The models which summarize these concepts are therefore called Adaptive
Resonance Theory, or ART, models. Psychophysical and neurobiological
data in support of ART are presented from early vision, visual object
recognition, auditory streaming, variable-rate speech perception,
somatosensory perception, and cognitive-emotional interactions, among
others. It is noted that ART mechanisms seem to be operative at all
levels of the visual system, and it is proposed how these mechanisms
are realized by known laminar circuits of visual cortex. It is predicted
that the same circuit realization of ART mechanisms will be found
in the laminar circuits of all sensory and cognitive neocortex. Concepts
and data are summarized concerning how some visual percepts may be
visibly, or modally, perceived, whereas amodal percepts may be consciously
recognized even though they are perceptually invisible. It is also
suggested that sensory and cognitive processing in the What processing
stream of the brain obey top-down matching and learning laws that
are often complementary to those used for spatial and motor processing
in the brain's Where processing stream. This enables our sensory
and cognitive representations to maintain their stability as we learn
more about the world, while allowing spatial and motor representations
to forget learned maps and gains that are no longer appropriate as
our bodies develop and grow from infanthood to adulthood. Procedural
memories are proposed to be unconscious because the inhibitory matching
process that supports these spatial and motor processes cannot lead
to resonance.},
author = {S. Grossberg},
doi = {10.1006/ccog.1998.0372},
institution = {Department of Cognitive and Neural Systems, Boston University, MA 02215, USA.},
journal = {Conscious Cogn},
keywords = {Attention; Brain; Consciousness; Humans; Learning; Models, Psychological; Psychophysiology},
month = {Mar},
number = {1},
owner = {gerstner},
pages = {1--44},
pii = {S1053-8100(98)90372-5},
pmid = {10072692},
timestamp = {2008.07.14},
title = {The link between brain learning, attention, and consciousness.},
url = {http://dx.doi.org/10.1006/ccog.1998.0372},
volume = {8},
year = {1999},
bdsk-url-1 = {http://dx.doi.org/10.1006/ccog.1998.0372}
}
@article{Gutkin05a,
abstract = {Neuronal firing is determined largely by incoming barrages of excitatory
postsynaptic potentials (EPSPs), each of which produce a transient
increase in firing probability. To measure the effects of weak transient
inputs on firing probability of cortical neurons, we compute phase-response
curves (PRCs). PRCs, whose shape can be related to the dynamics of
spike generation, document the changes in timing of spikes caused
by an EPSP in a repetitively firing neuron as a function of when
it arrives in the interspike interval (ISI). The PRC can be exactly
related to the poststimulus time histogram (PSTH) so that knowledge
of one uniquely determines the other. Typically, PRCs have zero values
at the start and end of the ISI, where EPSPs have minimal effects
and a peak in the middle. Where the peak occurs depends in part on
the firing properties of neurons. The PRC can have regions of positivity
and negativity corresponding respectively to speeding up and slowing
down the time of the next spike. A simple canonical model for spike
generation is introduced that shows how both the background firing
rate and the degree of postspike afterhyperpolarization contribute
to the shape of the PRC and thus to the PSTH. PRCs in strongly adapting
neurons are highly skewed to the right (indicating a higher change
in probability when the EPSPs appear late in the ISI) and can have
negative regions (indicating a decrease in firing probability) early
in the ISI. The PRC becomes more skewed to the right as the firing
rate decreases. Thus at low firing rates, the spikes are triggered
preferentially by inputs that occur only during a small time interval
late in the ISI. This implies that the neuron is more of a coincidence
detector at low firing frequencies and more of an integrator at high
frequencies. The steady-state theory is shown to also hold for slowly
varying inputs.},
author = {Boris S Gutkin and G. Bard Ermentrout and Alex D Reyes},
doi = {10.1152/jn.00359.2004},
institution = {itut Pasteur, 75015 Paris, France. bgutkin@pasteur.fr},
journal = {J Neurophysiol},
keywords = {Animals; Cerebral Cortex; Computer Simulation; Electric Stimulation; Excitatory Postsynaptic Potentials; Models, Neurological; Neurons; Reaction Time; Synaptic Transmission; Time Factors},
month = {Aug},
number = {2},
owner = {cmellier},
pages = {1623--1635},
pii = {00359.2004},
pmid = {15829595},
timestamp = {2008.07.30},
title = {Phase-response curves give the responses of neurons to transient inputs.},
url = {http://dx.doi.org/10.1152/jn.00359.2004},
volume = {94},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00359.2004}
}
@article{Haas06,
abstract = {Actions of inhibitory interneurons organize and modulate many neuronal
processes, yet the mechanisms and consequences of plasticity of inhibitory
synapses remain poorly understood. We report on spike-timing-dependent
plasticity of inhibitory synapses in the entorhinal cortex. After
pairing presynaptic stimulations at time t(pre) with evoked postsynaptic
spikes at time t(post) under pharmacological blockade of excitation
we found, via whole cell recordings, an asymmetrical timing rule
for plasticity of the remaining inhibitory responses. Strength of
response varied as a function of the time interval Deltat = t(post)
- t(pre): for Deltat > 0 inhibitory responses potentiated, peaking
at a delay of 10 ms. For Deltat < 0, the synaptic coupling depressed,
again with a maximal effect near 10 ms of delay. We also show that
changes in synaptic strength depend on changes in intracellular calcium
concentrations and demonstrate that the calcium enters the postsynaptic
cell through voltage-gated channels. Using network models, we demonstrate
how this novel form of plasticity can sculpt network behavior efficiently
and with remarkable flexibility.},
author = {Julie S Haas and Thomas Nowotny and H. D I Abarbanel},
doi = {10.1152/jn.00551.2006},
institution = {Institute for Nonlinear Science, University of California-San Diego, 9500 Gilman Dr. MC0402, La Jolla, CA 92093-0402, USA. julie.haas@gmail.com},
journal = {J Neurophysiol},
keywords = {Animals; Bicuculline; Calcium Channel Blockers; Calcium Channels, L-Type; Chelating Agents; Egtazic Acid; Electrophysiology; Entorhinal Cortex; Excitatory Postsynaptic Potentials; GABA Antagonists; Models, Neurological; Nerve Net; Neuronal Plasticity; Nimodipine; Patch-Clamp Techniques; Rats; Rats, Long-Evans; Receptors, AMPA; Receptors, N-Methyl-D-Aspartate; Seizures; Synapses},
month = {Dec},
number = {6},
owner = {gerstner},
pages = {3305--3313},
pii = {00551.2006},
pmid = {16928795},
timestamp = {2008.07.14},
title = {Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex.},
url = {http://dx.doi.org/10.1152/jn.00551.2006},
volume = {96},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00551.2006}
}
@article{Harmeling03,
author = {Harmeling, Stefan and Ziehe, Andreas and Kawanabe, Motoaki and M{\"u}ller, Klaus-Robert},
journal = {Neural Computation},
keywords = {ICA},
owner = {sprekeler},
pages = {1089--1124},
timestamp = {2008.04.14},
title = {Kernel-Based Nonlinear Blind Source Separation},
volume = {15},
year = {2003}
}
@article{Heil03,
abstract = {Thresholds of auditory-nerve (AN) fibers and auditory neurons are
commonly specified in terms of sound pressure only, implying that
they are independent of time. At the perceptual level, however, the
sound pressure required for detection decreases with increasing stimulus
duration, suggesting that the auditory system integrates sound over
time. The quantity commonly believed to be integrated is sound intensity,
implying that the auditory system would have an energy threshold.
However, leaky integrators of intensity with time constants of hundreds
of milliseconds are required to fit the data. Such time constants
are unknown in physiology and are also incompatible with the high
temporal resolution of the auditory system, creating the resolution-integration
paradox. Here we demonstrate that cortical and perceptual responses
are based on integration of the pressure envelope of the sound, as
we have previously shown for AN fibers, rather than on intensity.
The functions relating the pressure envelope integration thresholds
and time for AN fibers, cortical neurons, and perception in the same
species (cat), as well as for perception in many different vertebrate
species, are remarkably similar. They are well described by a power
law that resolves the resolution-integration paradox. The data argue
for the integrator to be located in the first synapse in the auditory
pathway and we discuss its mode of operation.},
author = {Peter Heil and Heinrich Neubauer},
doi = {10.1073/pnas.1030017100},
institution = {Leibniz Institute of Neurobiology, Brenneckestrasse 6, 39118 Magdeburg, Germany. peter.heil@ifn-magdeburg.de},
journal = {Proc Natl Acad Sci U S A},
keywords = {Animals; Auditory Pathways; Auditory Threshold; Cats; Humans; Models, Neurological; Nerve Fibers; Neurons; Perception; Reaction Time},
month = {May},
number = {10},
owner = {gerstner},
pages = {6151--6156},
pii = {1030017100},
pmid = {12724527},
timestamp = {2008.07.14},
title = {A unifying basis of auditory thresholds based on temporal summation.},
url = {http://dx.doi.org/10.1073/pnas.1030017100},
volume = {100},
year = {2003},
bdsk-url-1 = {http://dx.doi.org/10.1073/pnas.1030017100}
}
@article{Heil01,
abstract = {Current propositions of the quantity of sound driving the central
auditory system, specifically around threshold, are diverse and at
variance with one another. They include sound pressure, sound power,
or intensity, which are proportional to the square of pressure, and
energy, i.e., the integral of sound power over time. Here we show
that the relevant sound quantity and the nature of the threshold
can be obtained from the timing of the first spike of auditory-nerve
(AN) fibers after the onset of a stimulus. We reason that the first
spike is triggered when the stimulus reaches threshold and occurs
with fixed delay thereafter. By probing cat AN fibers with characteristic
frequency tones of different sound pressure levels and rise times,
we show that the differences in relative timing of the first spike
(including latencies >100 msec of fibers with low spontaneous rates)
can be well accounted for by essentially linear integration of pressure
over time. The inclusion of a constant pressure loss or gain to the
integrator improves the fit of the model and also accounts for most
of the variation of spontaneous rates across fibers. In addition,
there are tight correlations among delay, threshold, and spontaneous
rate. First-spike timing cannot be explained by models based on a
fixed pressure threshold, a fixed power or intensity threshold, or
an energy threshold. This suggests that AN fiber thresholds are best
measured in units of pressure by time. Possible mechanisms of pressure
integration by the inner hair cell-AN fiber complex are discussed.},
author = {P. Heil and H. Neubauer},
institution = {Leibniz Institute for Neurobiology, D-39118 Magdeburg, Germany. peter.heil@ifn-magdeburg.de},
journal = {J Neurosci},
keywords = {Acoustic Stimulation; Action Potentials; Animals; Auditory Threshold; Cats; Cochlear Nerve; Female; Hearing; Male; Models, Neurological; Nerve Fibers; Pressure; Reaction Time; Reproducibility of Results; Sound; Time Factors},
month = {Sep},
number = {18},
owner = {gerstner},
pages = {7404--7415},
pii = {21/18/7404},
pmid = {11549751},
timestamp = {2008.07.14},
title = {Temporal integration of sound pressure determines thresholds of auditory-nerve fibers.},
volume = {21},
year = {2001}
}
@article{Helmstaedter07,
abstract = {The characterization of individual neurons by Golgi and Cajal has
been the basis of neuroanatomy for a century. A new challenge is
to anatomically describe, at cellular resolution, complete local
circuits that can drive behavior. In this essay, we review the possibilities
to obtain a model cortical column by using in vitro and in vivo pair
recordings, followed by anatomical reconstructions of the projecting
and target cells. These pairs establish connection modules that eventually
may be useful to synthesize an average cortical column in silico.
Together with data on sensory evoked neuronal activity measured in
vivo, this will allow to model the anatomical and functional cellular
basis of behavior based on more realistic assumptions than previously
attempted.},
author = {M. Helmstaedter and C. P J de Kock and D. Feldmeyer and R. M. Bruno and B. Sakmann},
doi = {10.1016/j.brainresrev.2007.07.011},
journal = {Brain Res Rev},
keywords = {Animals; Cerebral Cortex; Computer Simulation; Humans; Models, Neurological; Nerve Net; Neural Pathways; Neurons},
month = {Oct},
number = {2},
owner = {tomm},
pages = {193--203},
pii = {S0165-0173(07)00136-1},
pmid = {17822776},
timestamp = {2008.12.31},
title = {Reconstruction of an average cortical column in silico.},
url = {http://dx.doi.org/10.1016/j.brainresrev.2007.07.011},
volume = {55},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.brainresrev.2007.07.011}
}
@article{Helmstaedter08d,
abstract = {Interneurons in layers 2/3 are excited by pyramidal cells within the
same layer (Reyes et al., 1998; Gupta et al., 2000), but little is
known about translaminar innervation of these interneurons by spiny
neurons in the main cortical input layer 4 (L4). Here, we investigated
(1) how efficiently L4 spiny neurons excite L2/3 interneurons via
monosynaptic connections, (2) whether glutamate release from axon
terminals of L4 spiny neurons depends on the identity of the postsynaptic
interneuron, and (3) how L4-to-L2/3 interneuron connections compare
with L4-to-L2/3 pyramidal neuron connections. We recorded from pairs
of L4 spiny neurons and L2/3 interneurons in acute slices of rat
barrel cortex of postnatal day 20 (P20) to P29 rats. The L4-to-L2/3
interneuron connections had an average unitary EPSP of 1.2 +/- 1.1
mV. We found an average of 2.3 +/- 0.8 contacts per connection, and
the L4-to-L2/3 interneuron innervation domains were mostly column
restricted. Unitary EPSP amplitudes and paired-pulse ratios in the
L4-to-L2/3 interneuron connections depended on the {\"}group{\"} of the
postsynaptic interneuron. Averaged over all L4-to-L2/3 interneuron
connections, unitary EPSP amplitudes were 1.8-fold higher than in
the translaminar L4-to-L2/3 pyramidal cell connections. Our results
suggest that L4 spiny neurons may more efficiently recruit L2/3 interneurons
than L2/3 pyramidal neurons, and that glutamate release from translaminar
boutons of L4 spiny neuron axons is target cell specific.},
author = {Moritz Helmstaedter and Jochen F Staiger and Bert Sakmann and Dirk Feldmeyer},
doi = {10.1523/JNEUROSCI.5701-07.2008},
journal = {J Neurosci},
keywords = {Action Potentials; Animals; Interneurons; Organ Culture Techniques; Rats; Rats, Wistar; Recruitment, Neurophysiological; Somatosensory Cortex; Synaptic Transmission},
month = {Aug},
number = {33},
owner = {tomm},
pages = {8273--8284},
pii = {28/33/8273},
pmid = {18701690},
timestamp = {2009.01.07},
title = {Efficient recruitment of layer 2/3 interneurons by layer 4 input in single columns of rat somatosensory cortex.},
url = {http://dx.doi.org/10.1523/JNEUROSCI.5701-07.2008},
volume = {28},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.5701-07.2008}
}
@article{Hendin94,
abstract = {We describe models for the olfactory bulb which perform separation
and decomposition of mixed odor inputs from different sources. The
odors are unknown to the system; hence this is an analog and extension
of the engineering problem of blind separation of signals. The separation
process makes use of the different temporal fluctuations of the input
odors which occur under natural conditions. We discuss two possibilities,
one relying on a specific architecture connecting modules with the
same sensory inputs and the other assuming that the modules (e.g.,
glomeruli) have different receptive fields in odor space. We compare
the implications of these models for the testing of mixed odors from
a single source.},
author = {O. Hendin and D. Horn and J. J. Hopfield},
institution = {School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Israel.},
journal = {Proc Natl Acad Sci U S A},
keywords = {Animals; Dendrites; Humans; Models, Neurological; Nerve Net; Neurons, Afferent; Odors; Olfactory Bulb; Signal Transduction},
month = {Jun},
number = {13},
owner = {cmellier},
pages = {5942--5946},
pmid = {8016093},
timestamp = {2008.07.14},
title = {Decomposition of a mixture of signals in a model of the olfactory bulb.},
volume = {91},
year = {1994}
}
@article{Hines97,
abstract = {The moment-to-moment processing of information by the nervous system
involves the propagation and interaction of electrical and chemical
signals that are distributed in space and time. Biologically realistic
modeling is needed to test hypotheses about the mechanisms that govern
these signals and how nervous system function emerges from the operation
of these mechanisms. The NEURON simulation program provides a powerful
and flexible environment for implementing such models of individual
neurons and small networks of neurons. It is particularly useful
when membrane potential is nonuniform and membrane currents are complex.
We present the basic ideas that would help informed users make the
most efficient use of NEURON.},
author = {M. L. Hines and N. T. Carnevale},
institution = {Department of Computer Science and Neuroengineering, Yale University, New Haven, CT 06520, USA.},
journal = {Neural Comput},
keywords = {Animals; Membrane Potentials; Models, Neurological; Neurons; Signal Transduction},
month = {Aug},
number = {6},
owner = {gerstner},
pages = {1179--1209},
pmid = {9248061},
timestamp = {2008.07.14},
title = {The NEURON simulation environment.},
volume = {9},
year = {1997}
}
@article{Hipp06,
abstract = {Rodents excel in making texture judgments by sweeping their whiskers
across a surface. Here we aimed to identify the signals present in
whisker vibrations that give rise to such fine sensory discriminations.
First, we used sensors to capture vibration signals in metal whiskers
during active whisking of an artificial system and in natural whiskers
during whisking of rats in vivo. Then we developed a classification
algorithm that successfully matched the vibration frequency spectra
of single trials to the texture that induced it. For artificial whiskers,
the algorithm correctly identified one texture of eight alternatives
on 40\% of trials; for in vivo natural whiskers, the algorithm correctly
identified one texture of five alternatives on 80\% of trials. Finally,
we asked which were the key discriminative features of the vibration
spectra. Under both artificial and natural conditions, the combination
of two features accounted for most of the information: The modulation
power-the power of the part of the whisker movement representing
the modulation due to the texture surface-increased with the coarseness
of the texture; the modulation centroid-a measure related to the
center of gravity within the power spectrum-decreased with the coarseness
of the texture. Indeed, restricting the signal to these two parameters
led to performance three-fourths as high as the full spectra. Because
earlier work showed that modulation power and centroid are directly
related to neuronal responses in the whisker pathway, we conclude
that the biological system optimally extracts vibration features
to permit texture classification.},
author = {Joerg Hipp and Ehsan Arabzadeh and Erik Zorzin and Jorg Conradt and Christoph Kayser and Mathew E Diamond and Peter K?nig},
doi = {10.1152/jn.01104.2005},
journal = {J Neurophysiol},
keywords = {Action Potentials; Afferent Pathways; Animals; Biomimetics; Computer Simulation; Discrimination Learning; Male; Mechanoreceptors; Models, Neurological; Movement; Physical Stimulation; Rats; Rats, Wistar; Sensory Thresholds; Space Perception; Surface Properties; Touch; Vibration; Vibrissae},
month = {Mar},
number = {3},
owner = {tomm},
pages = {1792--1799},
pii = {01104.2005},
pmid = {16338992},
timestamp = {2008.06.20},
title = {Texture signals in whisker vibrations.},
url = {http://dx.doi.org/10.1152/jn.01104.2005},
volume = {95},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.01104.2005}
}
@article{Hopfield91,
abstract = {Animals that are primarily dependent on olfaction must obtain a description
of the spatial location and the individual odor quality of environmental
odor sources through olfaction alone. The variable nature of turbulent
air flow makes such a remote sensing problem solvable if the animal
can make use of the information conveyed by the fluctuation with
time of the mixture of odor sources. Behavioral evidence suggests
that such analysis takes place. An adaptive network can solve the
essential problem, isolating the quality and intensity of the components
within a mixture of several individual unknown odor sources. The
network structure is an idealization of olfactory bulb circuitry.
The dynamics of synapse change is essential to the computation. The
synaptic variables themselves contain information needed by higher
processing centers. The use of the same axons to convey intensity
information and quality information requires time-coding of information.
Covariation defines an individual odor source (object), and this
may have a parallel in vision.},
author = {J. J. Hopfield},
institution = {Divisions of Chemistry, California Institute of Technology, Pasadena 91125.},
journal = {Proc Natl Acad Sci U S A},
keywords = {Animals; Computer Simulation; Mathematics; Models, Neurological; Odors; Perception; Smell},
month = {Aug},
number = {15},
owner = {gerstner},
pages = {6462--6466},
pmid = {1862075},
timestamp = {2008.07.14},
title = {Olfactory computation and object perception.},
volume = {88},
year = {1991}
}
@article{Hyvarinen99a,
author = {A. Hyv{\"a}rinen},
journal = {IEEE Transactions on Neural Networks},
keywords = {ICA},
owner = {sprekeler},
pages = {626--634},
timestamp = {2008.04.14},
title = {Fast and robust fixed-point algorithms for independent component analysis},
volume = {10},
year = {1999}
}
@article{Hyvarinen99b,
author = {Aapo Hyv{\"a}rinen},
journal = {Neural Computing Surveys},
keywords = {ICA},
owner = {sprekeler},
pages = {94-128},
timestamp = {2008.04.14},
title = {Survey on Independent Component Analysis},
volume = {2},
year = {1999}
}
@book{Hyvarinen01a,
author = {Hyv{\"a}rinen, A. and Karhunen, J. and Oja, E.},
keywords = {ICA},
owner = {sprekeler},
publisher = {Wiley, New York},
timestamp = {2008.04.14},
title = {{Independent Component Analysis}},
year = {2001}
}
@article{Hyvarinen99,
author = {Hyv{\"a}rinen, A. and Pajunen, P.},
journal = {Neural Networks},
keywords = {ICA, optimal-coding},
number = {3},
owner = {sprekeler},
pages = {429--439},
publisher = {Elsevier},
timestamp = {2008.04.14},
title = {Nonlinear independent component analysis: Existence and uniqueness results},
volume = {12},
year = {1999}
}
@article{Jin02,
abstract = {The dynamical attractors are thought to underlie many biological functions
of recurrent neural networks. Here we show that stable periodic spike
sequences with precise timings are the attractors of the spiking
dynamics of recurrent neural networks with global inhibition. Almost
all spike sequences converge within a finite number of transient
spikes to these attractors. The convergence is fast, especially when
the global inhibition is strong. These results support the possibility
that precise spatiotemporal sequences of spikes are useful for information
encoding and processing in biological neural networks.},
author = {Dezhe Z Jin},
institution = {Howard Hughes Medical Institute and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.},
journal = {Phys Rev Lett},
keywords = {Action Potentials; Brain; Models, Biological; Nerve Net; Neural Conduction; Neurons},
month = {Nov},
number = {20},
owner = {gerstner},
pages = {208102},
pmid = {12443511},
timestamp = {2008.07.14},
title = {Fast convergence of spike sequences to periodic patterns in recurrent networks.},
volume = {89},
year = {2002}
}
@article{Jolivet06b,
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},
author = {Jolivet, Renaud and Rauch, Alexander and Luscher, Hans-Rudolf and Gerstner, Wulfram},
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},
journal = {J Comput Neurosci},
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},
number = {1},
own = {NLM},
owner = {sprekeler},
pages = {35--49},
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},
title = {Predicting spike timing of neocortical pyramidal neurons by simple threshold models.},
volume = {21},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1007/s10827-006-7074-5}
}
@article{Jutten03,
author = {Jutten, C. and Karhunen, J.},
journal = {Proc. of the 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003)},
keywords = {ICA},
owner = {sprekeler},
pages = {245--256},
timestamp = {2008.04.14},
title = {{Advances in nonlinear blind source separation}},
year = {2003}
}
@article{Koerding00a,
abstract = {For biological realism, models of learning in neuronal networks often
assume that synaptic plasticity solely depends on locally available
signals, in particular only on the activity of the pre- and post-synaptic
cells. As a consequence, synapses influence the plasticity of other
synapses exclusively via the post-synaptic activity. Inspired by
recent research on the properties of apical dendrites it has been
suggested, that a second integration site in the apical dendrite
may mediate specific global information. Here we explore this issue
considering the example of learning invariant responses by examining
a network of spiking neurones with two sites of synaptic integration.
We demonstrate that results obtained in networks of units with continuous
outputs transfer to the more realistic neuronal model. This allows
a number of more specific experimental predictions, and is a necessary
step to unified description of learning rules exploiting timing of
action potentials.},
author = {K. P. K?rding and P. K?nig},
institution = {Institute of Neuroinformatics, ETH/University Z?rich, Winterthurerstr. 190, 8057, Z?rich, Switzerland. peterk@ini.phys.ethz.ch},
journal = {J Physiol Paris},
keywords = {Action Potentials; Animals; Brain; Learning; Models, Neurological; Nerve Net; Neuronal Plasticity; Neurons; Reaction Time; Synapses},
number = {5-6},
owner = {cmellier},
pages = {539--548},
pii = {S0928-4257(00)01088-3},
pmid = {11165918},
timestamp = {2008.07.14},
title = {A spike based learning rule for generation of invariant representations.},
volume = {94},
year = {2000}
}
@article{Koerding00d,
abstract = {Since the classical work of D O Hebb 1949 The Organization of Behaviour
(New York: Wiley) it is assumed that synaptic plasticity solely depends
on the activity of the pre- and the postsynaptic cells. Synapses
influence the plasticity of other synapses exclusively via the post-synaptic
activity. This confounds effects on synaptic plasticity and neuronal
activation and, thus, makes it difficult to implement networks which
optimize global measures of performance. Exploring solutions to this
problem, inspired by recent research on the properties of apical
dendrites, we examine a network of neurons with two sites of synaptic
integration. These communicate in such a way that one set of synapses
mainly influences the neurons' activity; the other set gates synaptic
plasticity. Analysing the system with a constant set of parameters
reveals: (1) the afferents that gate plasticity act as supervisors,
individual to every cell. (2) While the neurons acquire specific
receptive fields the net activity remains constant for different
stimuli. This ensures that all stimuli are represented and, thus,
contributes to information maximization. (3) Mechanisms for maximization
of coherent information can easily be implemented. Neurons with non-overlapping
receptive fields learn to fire correlated and preferentially transmit
information that is correlated over space. (4) We demonstrate how
a new measure of performance can be implemented: cells learn to represent
only the part of the input that is relevant to the processing at
higher stages. This criterion is termed 'relevant infomax'.},
author = {K. P. K?rding and P. K?nig},
institution = {Institute of Neuroinformatics, ETH/UNI Z?rich, Switzerland. koerding@ini.phys.ethz.ch},
journal = {Network},
keywords = {Cell Communication; Cerebral Cortex; Learning; Neural Networks (Computer); Pyramidal Cells; Synapses},
month = {Feb},
number = {1},
owner = {cmellier},
pages = {25--39},
pmid = {10735527},
timestamp = {2008.07.14},
title = {Learning with two sites of synaptic integration.},
volume = {11},
year = {2000}
}
@article{Kelley04,
abstract = {The robust nature of vocal communication in frogs has long attracted
the attention of natural philosophers and their biologically inclined
successors. Each frog species produces distinctive calls that facilitate
pre-mating reproductive isolation and thus speciation. In many terrestrial
species, a chorus of simultaneously calling males attracts females
to breeding sites; reproductive females then choose and locate one
male, using distinctive acoustic cues. Males compete with each other
vocally and sometimes physically as well. Anuran acoustic signaling
systems are thus subject to the strong pressures of sexual selection.
We are beginning to understand the ways in which vocal signals are
produced and decoded by the nervous system and the roles of neurally
active hormones in both processes.},
author = {Darcy B Kelley},
doi = {10.1016/j.conb.2004.10.015},
institution = {Department of Biological Sciences, MC2432, Columbia University, New York, New York 10027, USA. dbk3@columbia.edu},
journal = {Curr Opin Neurobiol},
keywords = {Animal Communication; Animals; Auditory Perception; Brain; Cues; Female; Male; Models, Animal; Neuropeptides; Neurosecretory Systems; Ranidae; Sexual Behavior, Animal; Vocalization, Animal},
month = {Dec},
number = {6},
owner = {gerstner},
pages = {751--757},
pii = {S0959-4388(04)00167-9},
pmid = {15582379},
timestamp = {2008.07.14},
title = {Vocal communication in frogs.},
url = {http://dx.doi.org/10.1016/j.conb.2004.10.015},
volume = {14},
year = {2004},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.conb.2004.10.015}
}
@article{Kempter01a,
abstract = {Computational maps are of central importance to a neuronal representation
of the outside world. In a map, neighboring neurons respond to similar
sensory features. A well studied example is the computational map
of interaural time differences (ITDs), which is essential to sound
localization in a variety of species and allows resolution of ITDs
of the order of 10 micros. Nevertheless, it is unclear how such an
orderly representation of temporal features arises. We address this
problem by modeling the ontogenetic development of an ITD map in
the laminar nucleus of the barn owl. We show how the owl's ITD map
can emerge from a combined action of homosynaptic spike-based Hebbian
learning and its propagation along the presynaptic axon. In spike-based
Hebbian learning, synaptic strengths are modified according to the
timing of pre- and postsynaptic action potentials. In unspecific
axonal learning, a synapse's modification gives rise to a factor
that propagates along the presynaptic axon and affects the properties
of synapses at neighboring neurons. Our results indicate that both
Hebbian learning and its presynaptic propagation are necessary for
map formation in the laminar nucleus, but the latter can be orders
of magnitude weaker than the former. We argue that the algorithm
is important for the formation of computational maps, when, in particular,
time plays a key role.},
author = {R. Kempter and C. Leibold and H. Wagner and J. L. van Hemmen},
doi = {10.1073/pnas.061369698},
institution = {Keck Center for Integrative Neuroscience, University of California, San Francisco, CA 94143-0732, USA. kempter@phy.ucsf.edu},
journal = {Proc Natl Acad Sci U S A},
keywords = {Animals; Learning; Models, Neurological; Neurons; Presynaptic Terminals; Strigiformes; Synaptic Transmission},
month = {Mar},
number = {7},
owner = {gerstner},
pages = {4166--4171},
pii = {061369698},
pmid = {11274439},
timestamp = {2008.07.14},
title = {Formation of temporal-feature maps by axonal propagation of synaptic learning.},
url = {http://dx.doi.org/10.1073/pnas.061369698},
volume = {98},
year = {2001},
bdsk-url-1 = {http://dx.doi.org/10.1073/pnas.061369698}
}
@article{Keren05,
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},
author = {Keren, Naomi and Peled, Noam and Korngreen, Alon},
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},
journal = {J Neurophysiol},
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},
number = {6},
own = {NLM},
owner = {sprekeler},
pages = {3730--3742},
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},
title = {Constraining compartmental models using multiple voltage recordings and genetic algorithms.},
volume = {94},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00408.2005}
}
@article{Koene08,
abstract = {We propose a mechanism to explain both retrospective and prospective
recall activity found in experimental data from hippocampal regions
CA3 and CA1. Our model of temporal context dependent episodic memory
replicates reverse recall in CA1, as recently recorded and published
[Foster, D., & Wilson, M. (2006). Reverse replay of behavioural sequences
in hippocampal place cells during the awake state. Nature, 440, 680-683],
as well as the prospective and retrospective activity recorded in
region CA3 during spatial tasks [Johnson, A., & Redish, A. (2006).
Neural ensembles in ca3 transiently encode paths forward of the animal
at a decision point: a possible mechanism for the consideration of
alternatives. In 2006 neuroscience meeting planner. Atlanta, GA:
Society for Neuroscience. (Program no. 574.2)]. We suppose that CA3
encodes episodic memory of both forward and reversed sequences of
perforant path spikes representing place input. Using a persistent
firing buffer mechanism in layer II of entorhinal cortex, simulated
episodic learning involves dentate gyrus, layer III of entorhinal
cortex, and hippocampal regions CA3 and CA1. Associations are formed
between buffered episodic cues, unique temporal context specific
representations in dentate gyrus, and episodic memory in the CA3
recurrent network.},
author = {Randal A Koene and Michael E Hasselmo},
doi = {10.1016/j.neunet.2007.12.029},
institution = {Center for Memory and Brain, Department of Psychology, Boston University, Boston, MA 02215, USA. randalk@bu.edu},
journal = {Neural Netw},
keywords = {Action Potentials; Animals; Computer Simulation; Hippocampus; Humans; Memory; Models, Biological; Neural Pathways; Neurons; Spatial Behavior},
number = {2-3},
owner = {sprekeler},
pages = {276--288},
pii = {S0893-6080(07)00253-5},
pmid = {18242057},
timestamp = {2009.02.16},
title = {Reversed and forward buffering of behavioral spike sequences enables retrospective and prospective retrieval in hippocampal regions CA3 and CA1.},
url = {http://dx.doi.org/10.1016/j.neunet.2007.12.029},
volume = {21},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.neunet.2007.12.029}
}
@article{Kuhn03,
abstract = {Pairwise correlations among spike trains recorded in vivo have been
frequently reported. It has been argued that correlated activity
could play an important role in the brain, because it efficiently
modulates the response of a postsynaptic neuron. We show here that
a neuron's output firing rate critically depends on the higher-order
statistics of the input ensemble. We constructed two statistical
models of populations of spiking neurons that fired with the same
rates and had identical pairwise correlations, but differed with
regard to the higher-order interactions within the population. The
first ensemble was characterized by clusters of spikes synchronized
over the whole population. In the second ensemble, the size of spike
clusters was, on average, proportional to the pairwise correlation.
For both input models, we assessed the role of the size of the population,
the firing rate, and the pairwise correlation on the output rate
of two simple model neurons: a continuous firing-rate model and a
conductance-based leaky integrate-and-fire neuron. An approximation
to the mean output rate of the firing-rate neuron could be derived
analytically with the help of shot noise theory. Interestingly, the
essential features of the mean response of the two neuron models
were similar. For both neuron models, the three input parameters
played radically different roles with respect to the postsynaptic
firing rate, depending on the interaction structure of the input.
For instance, in the case of an ensemble with small and distributed
spike clusters, the output firing rate was efficiently controlled
by the size of the input population. In addition to the interaction
structure, the ratio of inhibition to excitation was found to strongly
modulate the effect of correlation on the postsynaptic firing rate.},
author = {Alexandre Kuhn and Ad Aertsen and Stefan Rotter},
doi = {10.1162/089976603321043702},
institution = {Neurobiology and Biophysics, Biology III, Albert-Ludwigs-University, D-79104 Freiburg, Germany. kuhn@biologie.uni-freiburg.de},
journal = {Neural Comput},
keywords = {Action Potentials; Membrane Potentials; Models, Neurological; Neu; rons},
month = {Jan},
number = {1},
owner = {gerstner},
pages = {67--101},
pmid = {12590820},
timestamp = {2008.07.14},
title = {Higher-order statistics of input ensembles and the response of simple model neurons.},
url = {http://dx.doi.org/10.1162/089976603321043702},
volume = {15},
year = {2003},
bdsk-url-1 = {http://dx.doi.org/10.1162/089976603321043702}
}
@article{Kyriazi93,
abstract = {Layer IV of rodent somatosensory cortex contains identifiable networks
of neurons, called {\"}barrels,{\"} that are related one-to-one to individual
whiskers on the face. A previous study (Simons and Carvell, 1989)
described differences between the response properties of thalamic
and cortical vibrissa neurons and proposed that these transformations
can be explained by several features of barrel anatomy and physiology:
nonlinear neuronal properties, strongly responsive inhibitory and
less responsive excitatory neurons, convergent thalamic inputs to
cells of both types, and interconnections among barrel neurons. In
the present study these features were incorporated into a computational
model in order to test their explanatory power quantitatively. The
relative numbers of excitatory and inhibitory cells and the relative
numbers of synapses of thalamic and intrabarrel origin were chosen
to be consistent with available light and electron microscopic data.
Known functional differences between excitatory and inhibitory barrel
neurons were simulated through differences in spike activation functions,
refractory periods, postsynaptic potential decay rates, and synaptic
strengths. The model network was activated by spike trains recorded
previously from thalamic neurons in response to three different whisker
deflection protocols, and output, which consisted of spikes generated
by the simulated neurons, was compared to data from our previous
neurophysiological experiments. For each type of whisker stimulus,
the same set of parameter values yielded accurate simulations of
the cortical response. Realistic output was obtained under conditions
where each barrel cell integrated excitatory and inhibitory synaptic
inputs from a number of thalamic and other barrel neurons and where
the ratios between network excitation, network inhibition, and thalamic
excitation were approximately constant. Several quantities are defined
that may be generally useful in characterizing neuronal networks.
One important implication of the results is that thalamic relay neurons
not only provide essential drive to the cortex but could, by changing
their tonic activities, also directly regulate the tonic inhibition
present in the cortex and thereby modulate cortical receptive field
properties.},
author = {H. T. Kyriazi and D. J. Simons},
journal = {J Neurosci},
keywords = {Animals; Cerebral Cortex; Computer Simulation; Models, Neurological; Nervous System; Nervous System Physiological Phenomena; Neurons; Thalamus; Vibrissae},
month = {Apr},
number = {4},
owner = {tomm},
pages = {1601--1615},
pmid = {8463838},
timestamp = {2009.01.06},
title = {Thalamocortical response transformations in simulated whisker barrels.},
volume = {13},
year = {1993}
}
@article{Kaplan00,
abstract = {This paper explores the hypothesis that language communication in
its very first stage is bootstrapped in a social learning process
under the strong influence of culture. A concrete framework for social
learning has been developed based on the notion of a language game.
Autonomous robots have been programmed to behave according to this
framework. We show experiments that demonstrate why there has to
be a causal role of language on category acquisition; partly by showing
that it leads effectively to the bootstrapping of communication and
partly by showing that other forms of learning do not generate categories
usable in communication or make information assumptions which cannot
be satisfied},
author = {Steels L and Kaplan, F.},
journal = {Evolution of communication},
keywords = {Extralinguistic context ; Artificial intelligence ; Algorithm ; Model ; Categorization ; Language game ; Comprehension ; Cognitive process ; Socialization ; Verbal communication ; Language acquisition ; Psycholinguistics},
owner = {gerstner},
pages = {3-32},
timestamp = {2008.07.30},
title = {AIBO's first words: The social learning of language and meaning},
volume = {4},
year = {2000}
}
@article{Luna05,
abstract = {We sought to determine the neural code(s) for frequency discrimination
of vibrotactile stimuli. We tested five possible candidate codes
by analyzing the responses of single neurons recorded in primary
somatosensory cortex of trained monkeys while they discriminated
between two consecutive vibrotactile stimuli. Differences in the
frequency of two stimuli could be discriminated using information
from (i) time intervals between spikes, (ii) average spiking rate
during each stimulus, (iii) absolute number of spikes elicited by
each stimulus, (iv) average rate of bursts of spikes or (v) absolute
number of spike bursts elicited by each stimulus. However, only a
spike count code, in which spikes are integrated over a time window
that has most of its mass in the first 250 ms of each stimulus period,
covaried with behavior on a trial-by-trial basis, was consistent
with psychophysical biases induced by manipulation of stimulus duration,
and produced neurometric discrimination thresholds similar to behavioral
psychophysical thresholds.},
author = {Rogelio Luna and Adri?n Hern?ndez and Carlos D Brody and Ranulfo Romo},
doi = {10.1038/nn1513},
institution = {Instituto de Fisiolog?a Celular, Universidad Nacional Aut?noma de M?xico, 04510 M?xico, DF, M?xico.},
journal = {Nat Neurosci},
keywords = {Action Potentials; Animals; Discrimination (Psychology); Fourier Analysis; Macaca mulatta; Neurons; Physical Stimulation; Psychophysics; Reaction Time; Sensation; Sensory Thresholds; Somatosensory Cortex; Time Factors; Touch},
month = {Sep},
number = {9},
owner = {cmellier},
pages = {1210--1219},
pii = {nn1513},
pmid = {16056223},
timestamp = {2008.07.15},
title = {Neural codes for perceptual discrimination in primary somatosensory cortex.},
url = {http://dx.doi.org/10.1038/nn1513},
volume = {8},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1038/nn1513}
}
@article{Mainen95b,
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},
author = {Mainen, Z F and Joerges, J and Huguenard, J R and Sejnowski, T J},
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},
journal = {Neuron},
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},
number = {6},
own = {NLM},
owner = {sprekeler},
pages = {1427--1439},
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},
title = {A model of spike initiation in neocortical pyramidal neurons.},
volume = {15},
year = {1995}
}
@article{Markram06,
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},
author = {Markram, Henry},
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},
journal = {Nat Rev Neurosci},
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},
number = {2},
own = {NLM},
owner = {sprekeler},
pages = {153--160},
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},
title = {The blue brain project.},
volume = {7},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1038/nrn1848}
}
@article{Markram04a,
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},
author = {Markram, Henry and Toledo-Rodriguez, Maria and Wang, Yun and Gupta, Anirudh and Silberberg, Gilad and Wu, Caizhi},
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},
journal = {Nat Rev Neurosci},
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},
number = {10},
own = {NLM},
owner = {sprekeler},
pages = {793--807},
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},
title = {Interneurons of the neocortical inhibitory system.},
volume = {5},
year = {2004},
bdsk-url-1 = {http://dx.doi.org/10.1038/nrn1519}
}
@article{Mazzoni91a,
abstract = {Many recent studies have used artificial neural network algorithms
to model how the brain might process information. However, back-propagation
learning, the method that is generally used to train these networks,
is distinctly {\"}unbiological.{\"} We describe here a more biologically
plausible learning rule, using reinforcement learning, which we have
applied to the problem of how area 7a in the posterior parietal cortex
of monkeys might represent visual space in head-centered coordinates.
The network behaves similarly to networks trained by using back-propagation
and to neurons recorded in area 7a. These results show that a neural
network does not require back propagation to acquire biologically
interesting properties.},
author = {P. Mazzoni and R. A. Andersen and M. I. Jordan},
institution = {Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139.},
journal = {Proc Natl Acad Sci U S A},
keywords = {Animals; Artificial Intelligence; Brain; Haplorhini; Learning; Models, Neurological; Neural Pathways; Ocular Physiology; Parietal Lobe; Posture; Reinforcement (Psychology); Retina; Vision},
month = {May},
number = {10},
owner = {cmellier},
pages = {4433--4437},
pmid = {1903542},
timestamp = {2008.07.15},
title = {A more biologically plausible learning rule for neural networks.},
volume = {88},
year = {1991}
}
@article{Mehta04,
abstract = {Hebbian synaptic learning requires co-activation of presynaptic and
postsynaptic neurons. However, under some conditions, information
regarding the postsynaptic action potential, carried by backpropagating
action potentials, can be strongly degraded before it reaches the
distal exhibit Hebbian long-term potentiation (LTP)? Recent results
show that LTP can indeed occur at synapses on distal dendrites of
hippocamal CA1 neurons, even in the absence of a postsynaptic somatic
spike. Instead. local dendritic spikes contribute to the depolarization
required to induce LTP. Here, a dendritically constrained synaptic
learning rule is proposed, which suggests that nearby synapses can
encode temporally contiguous events.},
author = {Mayank R Mehta},
institution = {iversity, Providence, RI 02912, USA. mayank_mehta@brown.edu},
journal = {Trends Neurosci},
keywords = {Action Potentials; Animals; Brain Mapping; Dendrites; Hippocampus; Humans; Learning; Long-Term Potentiation; Memory; Models, Neurological; Neuronal Plasticity; Neurons; Synaptic Transmission},
month = {Feb},
number = {2},
owner = {gerstner},
pages = {69--72},
pii = {S0166223603003898},
pmid = {15106650},
timestamp = {2008.07.15},
title = {Cooperative LTP can map memory sequences on dendritic branches.},
volume = {27},
year = {2004}
}
@article{Mittelstaedt00,
abstract = {Arthropods as well as mammals are able to return straight home after
a random search excursion under conditions that are designed to exclude
all external cues. After a brief clarification of the terminology,
two principal systems of information processing that can achieve
this performance are introduced and analysed: Polar versus Cartesian
path integration. The different demands and achievements of the two
systems are confronted with neurophysiological findings on the functioning
of the hippocampus, and with a recent comprehensive model of how
the hippocampal place cells perform path integration. To connect
the neurophysiological findings with the behavior of the animal,
a new model is developed. It achieves three functionally diverse
performances: maintenance and control of a compass direction, navigation
by path integration, and formation of goals by connecting non-spatial
features with their location. This is done by three interconnected
feedback loops, set by a common reference variable. Their information-processing
structure enables the animal not only to home but also to go straight
from any stored goal to any other, without explicit representation
of the distance between them, and without a topological arrangement
of the store. The model explains behaviors not yet understood and
predicts still undiscovered performances. Because it allows the isolation
of orienting from storing functions yet also shows how they can be
connected, the model may help to reconcile conflicting views on the
function of the hippocampus.},
author = {H. Mittelstaedt},
institution = {Max-Planck-Institut f?r Verhaltensphysiologie, Seewiesen, Germany. h.mittelstaedt@mpi-seewiesen.mpg.de},
journal = {Biol Cybern},
keywords = {Animals; Arthropods; Homing Behavior; Models, Biological; Models, Theoretical; Neurons},
month = {Sep},
number = {3},
owner = {gerstner},
pages = {261--270},
pmid = {11007300},
timestamp = {2008.07.15},
title = {Triple-loop model of path control by head direction and place cells.},
volume = {83},
year = {2000}
}
@article{Moldakarimov05,
abstract = {Neurons in the visual cortex of the macaque monkey exhibit a variety
of competitive behaviors, including normalization and oscillation,
when presented with multiple visual stimuli. Here we argue that a
biophysically plausible cortical circuit with opponent inhibition,
spike-frequency adaptation, and synaptic depression can account for
the full range of behaviors. The governing parameter is the strength
of inhibition between competing neuronal pools. As the strength of
inhibition is increased, the pattern of network behavior shifts from
normalization mode to oscillatory mode, with oscillations occurring
at progressively lower frequency until, at the extreme, winner-take-all
behavior appears.},
author = {Samat Moldakarimov and Julianne E Rollenhagen and Carl R Olson and Carson C Chow},
doi = {10.1152/jn.00159.2005},
institution = {Department of Mathematics, Thackeray 505, University of Pittsburgh, Pittsburgh, PA 15260, USA.},
journal = {J Neurophysiol},
keywords = {Action Potentials; Animals; Biological Clocks; Computer Simulation; Evoked Potentials, Visual; Feedback; Macaca mulatta; Male; Models, Neurological; Nerve Net; Neural Inhibition; Neurons, Afferent; Photic Stimulation; Task Performance and Analysis; Visual Cortex; Visual Perception},
month = {Nov},
number = {5},
owner = {sprekeler},
pages = {3388--3396},
pii = {00159.2005},
pmid = {15944239},
timestamp = {2008.08.21},
title = {Competitive dynamics in cortical responses to visual stimuli.},
url = {http://dx.doi.org/10.1152/jn.00159.2005},
volume = {94},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00159.2005}
}
@article{Molgedey94,
author = {Molgedey, L. and Schuster, H. G.},
journal = {Physical Review Letters},
keywords = {ICA},
number = {23},
owner = {sprekeler},
pages = {3634--3637},
timestamp = {2008.04.14},
title = {Separation of a mixture of independent signals using time delayed correlations},
volume = {72},
year = {1994}
}
@article{Nowotny07a,
abstract = {The relationship between spiking and bursting dynamics is a key question
in neuroscience, particularly in understanding the origins of different
neural coding strategies and the mechanisms of motor command generation
and neural circuit coordination. Experiments indicate that spiking
and bursting dynamics can be independent. We hypothesize that different
mechanisms for spike and burst generation, intrinsic neuron dynamics
for spiking and a modulational network instability for bursting,
are the origin of this independence. We tested the hypothesis in
a detailed dynamical analysis of a minimal inhibitory neural microcircuit
(motif) of three reciprocally connected Hodgkin-Huxley neurons. We
reduced this high-dimensional dynamical system to a rate model and
showed that both systems have identical bifurcations from tonic spiking
to burst generation, which, therefore, does not depend on the details
of spiking activity.},
author = {Thomas Nowotny and Mikhail I Rabinovich},
institution = {University of Sussex, Falmer, Brighton BN1 9QJ, UK. T.Nowotny@sussex.ac.uk},
journal = {Phys Rev Lett},
keywords = {Finite Element Analysis; Kinetics; Models, Statistical; Nerve Net; Neurons; Nonlinear Dynamics},
month = {Mar},
number = {12},
owner = {gerstner},
pages = {128106},
pmid = {17501162},
timestamp = {2008.07.15},
title = {Dynamical origin of independent spiking and bursting activity in neural microcircuits.},
volume = {98},
year = {2007}
}
@article{Oja95,
author = {Oja, E. and Karhunen, J.},
journal = {Computational Intelligence: A Dynamic System Perspective},
keywords = {plasticity, ICA},
owner = {sprekeler},
pages = {83--97},
timestamp = {2008.04.14},
title = {{Signal separation by nonlinear Hebbian learning}},
year = {1995}
}
@article{Otto06a,
abstract = {In perceptual learning, stimuli are usually assumed to be presented
to a constant retinal location during training. However, due to tremor,
drift, and microsaccades of the eyes, the same stimulus covers different
retinal positions on sequential trials. Because of these variations
the mathematical decision problem changes from linear to non-linear
(). This non-linearity implies three predictions. First, varying
the spatial position of a stimulus within a moderate range does not
deteriorate perceptual learning. Second, improvement for one stimulus
variant can yield negative transfer to other variants. Third, interleaved
training with two stimulus variants yields no or strongly diminished
learning. Using a bisection task, we found psychophysical evidence
for the first and last prediction. However, no negative transfer
was found as opposed to the second prediction.},
author = {Thomas U Otto and Michael H Herzog and Manfred Fahle and Li Zhaoping},
doi = {10.1016/j.visres.2006.03.021},
institution = {Laboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique F?d?rale de Lausanne (EPFL), Switzerland. tom.otto@epfl.ch},
journal = {Vision Res},
keywords = {Attention; Eye Movements; Humans; Learning; Models, Psychological; Psychophysics; Uncertainty; Visual Perception},
month = {Oct},
number = {19},
owner = {gerstner},
pages = {3223--3233},
pii = {S0042-6989(06)00191-X},
pmid = {16690098},
timestamp = {2008.07.17},
title = {Perceptual learning with spatial uncertainties.},
url = {http://dx.doi.org/10.1016/j.visres.2006.03.021},
volume = {46},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.visres.2006.03.021}
}
@article{Pinto00,
abstract = {Previous experimental studies of both cortical barrel and thalamic
barreloid neuron responses in rodent somatosensory cortex have indicated
an active role for barrel circuitry in processing thalamic signals.
Previous modeling studies of the same system have suggested that
a major function of the barrel circuit is to render the response
magnitude of barrel neurons particularly sensitive to the temporal
distribution of thalamic input. Specifically, thalamic inputs that
are initially synchronous strongly engage recurrent excitatory connections
in the barrel and generate a response that briefly withstands the
strong damping effects of inhibitory circuitry. To test this experimentally,
we recorded responses from 40 cortical barrel neurons and 63 thalamic
barreloid neurons evoked by whisker deflections varying in velocity
and amplitude. This stimulus evoked thalamic response profiles that
varied in terms of both their magnitude and timing. The magnitude
of the thalamic population response, measured as the average number
of evoked spikes per stimulus, increased with both deflection velocity
and amplitude. On the other hand, the degree of initial synchrony,
measured from population peristimulus time histograms, was highly
correlated with the velocity of whisker deflection, deflection amplitude
having little or no effect on thalamic synchrony. Consistent with
the predictions of the model, the cortical population response was
determined largely by whisker velocity and was highly correlated
with the degree of initial synchrony among thalamic neurons (R(2)
= 0.91), as compared with the average number of evoked thalamic spikes
(R(2) = 0.38). Individually, the response of nearly all cortical
cells displayed a positive correlation with deflection velocity;
this homogeneity is consistent with the dependence of the cortical
response on local circuit interactions as proposed by the model.
By contrast, the response of individual thalamic neurons varied widely.
These findings validate the predictions of the modeling studies and,
more importantly, demonstrate that the mechanism by which the cortex
processes an afferent signal is inextricably linked with, and in
fact determines, the saliency of neural codes embedded in the thalamic
response.},
author = {D. J. Pinto and J. C. Brumberg and D. J. Simons},
journal = {J Neurophysiol},
keywords = {Animals; Electrophysiology; Female; Membrane Potentials; Nerve Net; Neurons; Patch-Clamp Techniques; Physical Stimulation; Posterior Thalamic Nuclei; Rats; Somatosensory Cortex; Thalamus; Vibrissae},
month = {Mar},
number = {3},
owner = {tomm},
pages = {1158--1166},
pmid = {10712446},
timestamp = {2008.12.31},
title = {Circuit dynamics and coding strategies in rodent somatosensory cortex.},
volume = {83},
year = {2000}
}
@article{Prinz03a,
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},
author = {Prinz, Astrid A and Billimoria, Cyrus P and Marder, Eve},
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},
journal = {J Neurophysiol},
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},
number = {6},
own = {NLM},
owner = {sprekeler},
pages = {3998--4015},
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},
title = {Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons.},
volume = {90},
year = {2003},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00641.2003}
}
@article{Rabinovich06b,
abstract = {The generation of informational sequences and their reorganization
or reshaping is one of the most intriguing subjects for both neuroscience
and the theory of autonomous intelligent systems. In spite of the
diversity of sequential activities of sensory, motor, and cognitive
neural systems, they have many similarities from the dynamical point
of view. In this review we discus the ideas, models, and mathematical
image of sequence generation and reshaping on different levels of
the neural hierarchy, i.e., the role of a sensory network dynamics
in the generation of a motor program (hunting swimming of marine
mollusk Clione), olfactory dynamical coding, and sequential learning
and decision making. Analysis of these phenomena is based on the
winnerless competition principle. The considered models can be a
basis for the design of biologically inspired autonomous intelligent
systems.},
author = {Mikhail I Rabinovich and Ram?n Huerta and Pablo Varona and Valentin S Afraimovich},
doi = {10.1007/s00422-006-0121-5},
institution = {UCSD, Institute for Nonlinear Science, 9500 Gilman Dr., La Jolla, CA 92093-0402, USA. mrabinovich@ucsd.edu},
journal = {Biol Cybern},
keywords = {Animals; Decision Making; Learning; Mathematics; Models, Neurological; Nerve Net; Neurons; Olfactory Pathways; Smell},
month = {Dec},
number = {6},
owner = {cmellier},
pages = {519--536},
pmid = {17136380},
timestamp = {2008.07.17},
title = {Generation and reshaping of sequences in neural systems.},
url = {http://dx.doi.org/10.1007/s00422-006-0121-5},
volume = {95},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1007/s00422-006-0121-5}
}
@article{Reymann07,
abstract = {Our review focuses on the mechanisms which enable the late maintenance
of hippocampal long-term potentiation (LTP; >3h), a phenomenon which
is thought to underlie prolonged memory. About 20 years ago we showed
for the first time that the maintenance of LTP - like memory storage--depends
on intact protein synthesis and thus, consists of at least two temporal
phases. Here we concentrate on mechanisms required for the induction
of the transient early-LTP and of the protein synthesis-dependent
late-LTP. Our group has shown that the induction of late-LTP requires
the associative activation of heterosynaptic inputs, i.e. the synergistic
activation of glutamatergic and modulatory, reinforcing inputs within
specific, effective time windows. The induction of late-LTP is characterized
by novel, late-associative properties such as 'synaptic tagging'
and 'late-associative reinforcement'. Both phenomena require the
associative setting of synaptic tags as well as the availability
of plasticity-related proteins (PRPs) and they are restricted to
functional dendritic compartments, in general. 'Synaptic tagging'
guarantees input specificity and thus the specific processing of
afferent signals for the establishment of late-LTP. 'Late-associative
reinforcement' describes a process where early-LTP by the co-activation
of modulatory inputs can be transformed into late-LTP in activated
synapses where a tag is set. Recent evidence from behavioral experiments,
which studied processes of emotional and cognitive reinforcement
of LTP, point to the physiological relevance of the above mechanisms
during cellular and system's memory formation.},
author = {Klaus G Reymann and Julietta U Frey},
doi = {10.1016/j.neuropharm.2006.07.026},
institution = {Department for Neurophysiology, Leibniz Institute for Neurobiology, Brenneckestrasse 6, D-39118 Magdeburg, Germany.},
journal = {Neuropharmacology},
keywords = {Animals; Hippocampus; Long-Term Potentiation; Models, Neurological; Synapses; Time Factors},
month = {Jan},
number = {1},
owner = {eleni},
pages = {24--40},
pii = {S0028-3908(06)00240-1},
pmid = {16919684},
timestamp = {2008.11.12},
title = {The late maintenance of hippocampal LTP: requirements, phases, 'synaptic tagging', 'late-associativity' and implications.},
url = {http://dx.doi.org/10.1016/j.neuropharm.2006.07.026},
volume = {52},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.neuropharm.2006.07.026}
}
@article{Rollenhagen05,
abstract = {Some neurons in the inferotemporal cortex (IT) of the macaque monkey
respond to visual stimuli by firing action potentials in a series
of sharply defined bursts at a frequency of about 5 Hz. The aim of
the present study was to test the hypothesis that the oscillatory
responses of these neurons depend on competitive interactions with
other neurons selective for different stimuli. To test this hypothesis,
we monitored responses to probe images displayed in the presence
of other already visible backdrop images. Two stimuli were used in
testing each neuron: a foveal image that, when displayed alone, elicited
an excitatory response (the {\"}object") and a peripheral image that,
when displayed alone, elicited little or no activity (the {\"}flanker").
We assessed the results of presenting these images separately and
together in monkeys trained to maintain central fixation. Two novel
phenomena emerged. First, displaying the object in the presence of
the flanker enhanced the strength of the oscillatory component of
the response to the object. This effect varied in strength across
task contexts and may have depended on the monkey's allocating attention
to the flanker. Second, displaying the flanker in the presence of
the object gave rise to sometimes strong oscillations in which the
initial phase was negative. This was all the more striking because
the flanker by itself elicited little or no response. This effect
was robust and invariant across task contexts. These results can
be accounted for by competition between two neuronal populations,
one selective for the object and the other for the flanker, if it
is assumed that the visual responses of each population are subject
to fatigue.},
author = {Julianne E Rollenhagen and Carl R Olson},
doi = {10.1152/jn.00158.2005},
institution = {Center for the Neural Basis of Cognition, Mellon Institute, Room 115, 4400 Fifth Ave., Pittsburgh, PA 15213-2683, USA.},
journal = {J Neurophysiol},
keywords = {Action Potentials; Animals; Biological Clocks; Evoked Potentials, Visual; Macaca mulatta; Male; Neurons, Afferent; Photic Stimulation; Task Performance and Analysis; Temporal Lobe; Visual Cortex; Visual Perception},
month = {Nov},
number = {5},
owner = {sprekeler},
pages = {3368--3387},
pii = {00158.2005},
pmid = {15928064},
timestamp = {2008.08.21},
title = {Low-frequency oscillations arising from competitive interactions between visual stimuli in macaque inferotemporal cortex.},
url = {http://dx.doi.org/10.1152/jn.00158.2005},
volume = {94},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00158.2005}
}
@article{Rossum02l,
abstract = {We model the propagation of neural activity through a feedforward
network consisting of layers of integrate-and-fire neurons. In the
presence of a noisy background current and spontaneous background
firing, firing rate modulations are transmitted linearly through
many layers, with a delay proportional to the synaptic time constant
and with little distortion. Single neuron properties and firing statistics
are in agreement with physiological data. The proposed mode of propagation
allows for fast computation with population coding based on firing
rates, as is demonstrated with a local motion detector.},
author = {Mark C W van Rossum and Gina G Turrigiano and Sacha B Nelson},
institution = {Department of Biology, Brandeis University, Waltham, Massachusetts 02454-9110, USA. vrossum@brandeis.edu},
journal = {J Neurosci},
keywords = {Computer Simulation; Models, Neurological; Motion Perception; Neural Networks (Computer); Neurons; Reproducibility of Results; Sensory Thresholds; Synapses; Synaptic Transmission},
month = {Mar},
number = {5},
owner = {cmellier},
pages = {1956--1966},
pii = {22/5/1956},
pmid = {11880526},
timestamp = {2008.07.23},
title = {Fast propagation of firing rates through layered networks of noisy neurons.},
volume = {22},
year = {2002}
}
@article{Rudolph06a,
abstract = {Event-driven simulation strategies were proposed recently to simulate
integrate-and-fire (IF) type neuronal models. These strategies can
lead to computationally efficient algorithms for simulating large-scale
networks of neurons; most important, such approaches are more precise
than traditional clock-driven numerical integration approaches because
the timing of spikes is treated exactly. The drawback of such event-driven
methods is that in order to be efficient, the membrane equations
must be solvable analytically, or at least provide simple analytic
approximations for the state variables describing the system. This
requirement prevents, in general, the use of conductance-based synaptic
interactions within the framework of event-driven simulations and,
thus, the investigation of network paradigms where synaptic conductances
are important. We propose here a number of extensions of the classical
leaky IF neuron model involving approximations of the membrane equation
with conductance-based synaptic current, which lead to simple analytic
expressions for the membrane state, and therefore can be used in
the event-driven framework. These conductance-based IF (gIF) models
are compared to commonly used models, such as the leaky IF model
or biophysical models in which conductances are explicitly integrated.
All models are compared with respect to various spiking response
properties in the presence of synaptic activity, such as the spontaneous
discharge statistics, the temporal precision in resolving synaptic
inputs, and gain modulation under in vivo-like synaptic bombardment.
Being based on the passive membrane equation with fixed-threshold
spike generation, the proposed gIF models are situated in between
leaky IF and biophysical models but are much closer to the latter
with respect to their dynamic behavior and response characteristics,
while still being nearly as computationally efficient as simple IF
neuron models. gIF models should therefore provide a useful tool
for efficient and precise simulation of large-scale neuronal networks
with realistic, conductance-based synaptic interactions.},
author = {Michelle Rudolph and Alain Destexhe},
doi = {10.1162/neco.2006.18.9.2146},
institution = {1198 Gif-sur-Yvette, France. Rudolph@iaf.cnrs-gif.fr},
journal = {Neural Comput},
keywords = {Action Potentials; Computer Simulation; Models, Neurological; Neural Networks (Computer)},
month = {Sep},
number = {9},
owner = {cmellier},
pages = {2146--2210},
pmid = {16846390},
timestamp = {2008.07.17},
title = {Analytical integrate-and-fire neuron models with conductance-based dynamics for event-driven simulation strategies.},
url = {http://dx.doi.org/10.1162/neco.2006.18.9.2146},
volume = {18},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1162/neco.2006.18.9.2146}
}
@article{Sajikumar04a,
abstract = {Protein synthesis-dependent, synapse input-specific late phases of
long-term potentiation (LTP) and depression (LTD) may underlie memory
formation at the cellular level. Recently, it was described that
the induction of LTP can mark a specifically activated synapse by
a synaptic tag to capture synapse non-specific plasticity-related
proteins (PRPs) and thus maintaining input-specific LTP for prolonged
periods. Here we show in rat hippocampal slices in vitro, that the
induction of protein synthesis-dependent late-LTD is also characterized
by synaptic tagging and that heterosynaptic induction of either LTD
or LTP on two sets of independent synaptic inputs S1 and S2 can lead
to late-associative interactions: early-LTD in S2 was transformed
into a late-LTD, if late-LTP was induced in S1. The synthesis of
process-independent PRPs by late-LTP in S1 was sufficient to transform
early- into late-LTD in S2 when process-specific synaptic tags were
set. We name this new associative property of cellular information
processing 'cross-tagging.'},
author = {Sreedharan Sajikumar and Julietta U Frey},
doi = {10.1016/j.nlm.2004.03.003},
institution = {Department for Neurophysiology, Leibniz-Institute for Neurobiology, Brenneckestr. 6, D-39118 Magdeburg, Germany.},
journal = {Neurobiol Learn Mem},
keywords = {Animals; Cell Communication; Dopamine; Electric Stimulation; Hippocampus; Long-Term Potentiation; Long-Term Synaptic Depression; Male; Models, Neurological; Nerve Tissue Proteins; Neuronal Plasticity; Neurons; Organ Culture Techniques; Rats; Rats, Wistar; Synapses},
month = {Jul},
number = {1},
owner = {cmellier},
pages = {12--25},
pii = {S1074742704000255},
pmid = {15183167},
timestamp = {2008.07.17},
title = {Late-associativity, synaptic tagging, and the role of dopamine during LTP and LTD.},
url = {http://dx.doi.org/10.1016/j.nlm.2004.03.003},
volume = {82},
year = {2004},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.nlm.2004.03.003}
}
@article{Sarid07,
abstract = {We report a step in constructing an in silico model of a neocortical
column, focusing on the synaptic connection between layer 4 (L4)
spiny neurons and L2/3 pyramidal cells in rat barrel cortex. It is
based first on a detailed morphological and functional characterization
of synaptically connected pairs of L4-L2/3 neurons from in vitro
recordings and second, on in vivo recordings of voltage responses
of L2/3 pyramidal cells to current pulses and to whisker deflection.
In vitro data and a detailed compartmental model of L2/3 pyramidal
cells enabled us to extract their specific membrane resistivity (
approximately 16,000 ohms x cm(2)) and capacitance ( approximately
0.8 microF/cm(2)) and the spatial distribution of L4-L2/3 synaptic
contacts. The average peak conductance per L4 synaptic contact is
0.26 nS for the alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic
acid and 0.2 nS for NMDA receptors. The in vivo voltage response
for current steps was then used to calibrate the model for in vivo
conditions in the Down state. Consequently, the effect of a single
whisker deflection was modeled by converging, on average, 350 +/-
20 L4 axons onto the modeled L2/3 pyramidal cell. Based on values
of synaptic conductance, the spatial distribution of L4 synapses
on L2/3 dendrites, and the average in vivo spiking probability of
L4 spiny neurons, the model predicts that the feed-forward L4-L2/3
connection on its own does not fire the L2/3 neuron. With a broader
distribution in the number of L4 neurons or with slight synchrony
among them, the L2/3 model does spike with low probability.},
author = {Leora Sarid and Randy Bruno and Bert Sakmann and Idan Segev and Dirk Feldmeyer},
doi = {10.1073/pnas.0707853104},
journal = {Proc Natl Acad Sci U S A},
keywords = {Animals; Electrophysiology; Evoked Potentials, Somatosensory; Models, Neurological; Neocortex; Pyramidal Cells; Rats; Receptors, AMPA; Receptors, N-Methyl-D-Aspartate; Synapses; Vibrissae},
month = {Oct},
number = {41},
owner = {tomm},
pages = {16353--16358},
pii = {0707853104},
pmid = {17913876},
timestamp = {2008.12.31},
title = {Modeling a layer 4-to-layer 2/3 module of a single column in rat neocortex: interweaving in vitro and in vivo experimental observations.},
url = {http://dx.doi.org/10.1073/pnas.0707853104},
volume = {104},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1073/pnas.0707853104}
}
@article{Saudargiene05,
abstract = {In spike-timing-dependent plasticity (STDP) the synapses are potentiated
or depressed depending on the temporal order and temporal difference
of the pre- and post-synaptic signals. We present a biophysical model
of STDP which assumes that not only the timing, but also the shapes
of these signals influence the synaptic modifications. The model
is based on a Hebbian learning rule which correlates the NMDA synaptic
conductance with the post-synaptic signal at synaptic location as
the pre- and post-synaptic quantities. As compared to a previous
paper [Saudargiene, A., Porr, B., Worgotter, F., 2004. How the shape
of pre- and post-synaptic signals can influence stdp: a biophysical
model. Neural Comp.], here we show that this rule reproduces the
generic STDP weight change curve by using real neuronal input signals
and combinations of more than two (pre- and post-synaptic) spikes.
We demonstrate that the shape of the STDP curve strongly depends
on the shape of the depolarising membrane potentials, which induces
learning. As these potentials vary at different locations of the
dendritic tree, model predicts that synaptic changes are location
dependent. The model is extended to account for the patterns of more
than two spikes of the pre- and post-synaptic cells. The results
show that STDP weight change curve is also activity dependent.},
author = {A. Saudargiene and B. Porr and F. W?rg?tter},
doi = {10.1016/j.biosystems.2004.09.010},
institution = {Department of Psychology, University of Stirling, Stirling FK9 4LA, Scotland, UK. ausra@cn.stir.ac.uk},
journal = {Biosystems},
keywords = {Action Potentials; Biophysics; Models, Neurological; Neuronal Plasticity; Synapses},
number = {1-3},
owner = {gerstner},
pages = {3--10},
pii = {S0303-2647(04)00151-0},
pmid = {15649584},
timestamp = {2008.07.17},
title = {Synaptic modifications depend on synapse location and activity: a biophysical model of STDP.},
url = {http://dx.doi.org/10.1016/j.biosystems.2004.09.010},
volume = {79},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.biosystems.2004.09.010}
}
@article{Schaette05,
abstract = {Reliable accounts of the variability observed in neural spike trains
are a prerequisite for the proper interpretation of neural dynamics
and coding principles. Models that accurately describe neural variability
over a wide range of stimulation and response patterns are therefore
highly desirable, especially if they can explain this variability
in terms of basic neural observables and parameters such as firing
rate and refractory period. In this work, we analyze the response
variability recorded in vivo from locust auditory receptor neurons
under acoustic stimulation. In agreement with results from other
systems, our data suggest that neural refractoriness has a strong
influence on spike-train variability. We therefore explore a stochastic
model of spike generation that includes refractoriness through a
recovery function. Because our experimental data are consistent with
a renewal process, the recovery function can be derived from a single
interspike-interval histogram obtained under constant stimulation.
The resulting description yields quantitatively accurate predictions
of the response variability over the whole range of firing rates
for constant-intensity as well as amplitude-modulated sound stimuli.
Model parameters obtained from constant stimulation can be used to
predict the variability in response to dynamic stimuli. These results
demonstrate that key ingredients of the stochastic response dynamics
of a sensory neuron are faithfully captured by a simple stochastic
model framework.},
author = {Roland Schaette and Tim Gollisch and Andreas V M Herz},
doi = {10.1152/jn.00758.2004},
institution = {Institute for Theoretical Biology, Department of Biology, Humboldt University, Invalidenstr. 43, 10115 Berlin, Germany. r.schaette@biologie.hu-berlin.de},
journal = {J Neurophysiol},
keywords = {Acoustic Stimulation; Action Potentials; Animals; Auditory Pathways; Dose-Response Relationship, Radiation; Grasshoppers; Models, Neurological; Neurons; Nonlinear Dynamics; Predictive Value of Tests; Reproducibility of Results; Time Factors},
month = {Jun},
number = {6},
owner = {gerstner},
pages = {3270--3281},
pii = {00758.2004},
pmid = {15689392},
timestamp = {2008.07.17},
title = {Spike-train variability of auditory neurons in vivo: dynamic responses follow predictions from constant stimuli.},
url = {http://dx.doi.org/10.1152/jn.00758.2004},
volume = {93},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1152/jn.00758.2004}
}
@article{Schultz97a,
abstract = {The capacity to predict future events permits a creature to detect,
model, and manipulate the causal structure of its interactions with
its environment. Behavioral experiments suggest that learning is
driven by changes in the expectations about future salient events
such as rewards and punishments. Physiological work has recently
complemented these studies by identifying dopaminergic neurons in
the primate whose fluctuating output apparently signals changes or
errors in the predictions of future salient and rewarding events.
Taken together, these findings can be understood through quantitative
theories of adaptive optimizing control.},
author = {W. Schultz and P. Dayan and P. R. Montague},
institution = {Institute of Physiology, University of Fribourg, CH-1700 Fribourg, Switzerland. Wolfram.Schultz@unifr.ch},
journal = {Science},
keywords = {Algorithms; Animals; Computer Simulation; Conditioning (Psychology); Cues; Dopamine; Learning; Mesencephalon; Models, Neurological; Neurons; Rats; Reward},
month = {Mar},
number = {5306},
owner = {gerstner},
pages = {1593--1599},
pmid = {9054347},
timestamp = {2008.07.17},
title = {A neural substrate of prediction and reward.},
volume = {275},
year = {1997}
}
@article{Schulz06,
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},
author = {Schulz, David J and Goaillard, Jean-Marc and Marder, Eve},
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},
journal = {Nat Neurosci},
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},
number = {3},
own = {NLM},
owner = {sprekeler},
pages = {356--362},
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},
title = {Variable channel expression in identified single and electrically coupled neurons in different animals.},
volume = {9},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1038/nn1639}
}
@article{Series04,
abstract = {Several studies have shown that the information conveyed by bell-shaped
tuning curves increases as their width decreases, leading to the
notion that sharpening of tuning curves improves population codes.
This notion, however, is based on assumptions that the noise distribution
is independent among neurons and independent of the tuning curve
width. Here we reexamine these assumptions in networks of spiking
neurons by using orientation selectivity as an example. We compare
two principal classes of model: one in which the tuning curves are
sharpened through cortical lateral interactions, and one in which
they are not. We report that sharpening through lateral interactions
does not improve population codes but, on the contrary, leads to
a severe loss of information. In addition, the sharpening models
generate complicated codes that rely extensively on pairwise correlations.
Our study generates several experimental predictions that can be
used to distinguish between these two classes of model.},
author = {Peggy Seri?s and Peter E Latham and Alexandre Pouget},
doi = {10.1038/nn1321},
institution = {Gatsby Computational Neuroscience Unit, Alexandra House, 17 Queen Square, London WC1N 3AR, UK.},
journal = {Nat Neurosci},
keywords = {Action Potentials; Animals; Contrast Sensitivity; Geniculate Bodies; Humans; Models, Neurological; Neural Inhibition; Neural Pathways; Neurons; Orientation; Synaptic Transmission; Visual Cortex; Visual Perception},
month = {Oct},
number = {10},
owner = {gerstner},
pages = {1129--1135},
pii = {nn1321},
pmid = {15452579},
timestamp = {2008.07.17},
title = {Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations.},
url = {http://dx.doi.org/10.1038/nn1321},
volume = {7},
year = {2004},
bdsk-url-1 = {http://dx.doi.org/10.1038/nn1321}
}
@article{Shipley74,
author = {M. T. Shipley},
journal = {J Neurophysiol},
keywords = {Animals; Electrophysiology; Hair; Microelectrodes; Neurons, Afferent; Physical Stimulation; Rats; Sense Organs; Skin; Somatosensory Cortex; Stereotaxic Techniques; Synaptic Transmission; Trigeminal Nerve},
month = {Jan},
number = {1},
owner = {tomm},
pages = {73--90},
pmid = {4359792},
timestamp = {2008.06.23},
title = {Response characteristics of single units in the rat's trigeminal nuclei to vibrissa displacements.},
volume = {37},
year = {1974}
}
@article{Shouval05,
abstract = {Synaptic weights store memories that can last a lifetime. Yet, memory
depends on synaptic protein receptors that are recycled in and out
of the membrane at a fast rate, possibly several times an hour. Several
proposals to bridge this vast gap in time scales between memory and
its molecular substrate have relied on bistable molecular switches.
Here, we propose an alternative to this approach based on clusters
of interacting receptors in the synaptic membrane. We show that such
clusters can be metastable and that the lifetime of such clusters
can be many orders of magnitude larger than the lifetime of the receptors
of which they are composed. We also demonstrate how bidirectional
synaptic plasticity can be implemented in this framework.},
author = {Harel Z Shouval},
doi = {10.1073/pnas.0506934102},
institution = {Department of Neurobiology and Anatomy, University of Texas Medical School, 6431 Fannin Street, Houston, TX 77030, USA. harel.shouval@uth.tmc.edu},
journal = {Proc Natl Acad Sci U S A},
keywords = {Computer Simulation; Memory; Models, Neurological; Neuronal Plasticity; Receptors, Neurotransmitter; Synapses; Time Factors},
month = {Oct},
number = {40},
owner = {gerstner},
pages = {14440--14445},
pii = {0506934102},
pmid = {16189022},
timestamp = {2008.07.17},
title = {Clusters of interacting receptors can stabilize synaptic efficacies.},
url = {http://dx.doi.org/10.1073/pnas.0506934102},
volume = {102},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1073/pnas.0506934102}
}
@article{Siegel00,
abstract = {The classical view of cortical information processing is that of a
bottom-up process in a feedforward hierarchy. However, psychophysical,
anatomical, and physiological evidence suggests that top-down effects
play a crucial role in the processing of input stimuli. Not much
is known about the neural mechanisms underlying these effects. Here
we investigate a physiologically inspired model of two reciprocally
connected cortical areas. Each area receives bottom-up as well as
top-down information. This information is integrated by a mechanism
that exploits recent findings on somato-dendritic interactions. (1)
This results in a burst signal that is robust in the context of noise
in bottom-up signals. (2) Investigating the influence of additional
top-down information, priming-like effects on the processing of bottom-up
input can be demonstrated. (3) In accordance with recent physiological
findings, interareal coupling in low-frequency ranges is characteristically
enhanced by top-down mechanisms. The proposed scheme combines a qualitative
influence of top-down directed signals on the temporal dynamics of
neuronal activity with a limited effect on the mean firing rate of
the targeted neurons. As it gives an account of the system properties
on the cellular level, it is possible to derive several experimentally
testable predictions.},
author = {M. Siegel and K. P. K?rding and P. K?nig},
institution = {Institute of Neuroinformatics, ETH/University Z?rich.},
journal = {J Comput Neurosci},
keywords = {Action Potentials; Animals; Cerebral Cortex; Dendrites; Humans; Information Theory; Models, Neurological; Nerve Net; Sensation; Signal Transduction; Synaptic Transmissi; Time Factors; on},
number = {2},
owner = {gerstner},
pages = {161--173},
pmid = {10798600},
timestamp = {2008.07.17},
title = {Integrating top-down and bottom-up sensory processing by somato-dendritic interactions.},
volume = {8},
year = {2000}
}
@article{Simen06,
abstract = {Optimal performance in two-alternative, free response decision-making
tasks can be achieved by the drift-diffusion model of decision making--which
can be implemented in a neural network--as long as the threshold
parameter of that model can be adapted to different task conditions.
Evidence exists that people seek to maximize reward in such tasks
by modulating response thresholds. However, few models have been
proposed for threshold adaptation, and none have been implemented
using neurally plausible mechanisms. Here we propose a neural network
that adapts thresholds in order to maximize reward rate. The model
makes predictions regarding optimal performance and provides a benchmark
against which actual performance can be compared, as well as testable
predictions about the way in which reward rate may be encoded by
neural mechanisms.},
author = {Patrick Simen and Jonathan D Cohen and Philip Holmes},
doi = {10.1016/j.neunet.2006.05.038},
institution = {Center for the Study of Brain, Mind and Behavior, Princeton University, Princeton, NJ 08544, USA. psimen@math.princeton.edu},
journal = {Neural Netw},
keywords = {Algorithms; Animals; Computer Simulation; Decision Making; Differential Threshold; Discrimination (Psychology); Humans; Models, Psychological; Nerve Net; Neural Networks (Computer); Reaction Time; Reward},
month = {Oct},
number = {8},
owner = {gerstner},
pages = {1013--1026},
pii = {S0893-6080(06)00162-6},
pmid = {16987636},
timestamp = {2008.07.17},
title = {Rapid decision threshold modulation by reward rate in a neural network.},
url = {http://dx.doi.org/10.1016/j.neunet.2006.05.038},
volume = {19},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.neunet.2006.05.038}
}
@article{Sprekeler08,
abstract = {Slow feature analysis is an algorithm for unsupervised learning of
invariant representations from data with temporal correlations. Here,
we present a mathematical analysis of slow feature analysis for the
case where the input-output functions are not restricted in complexity.
We show that the optimal functions obey a partial differential eigenvalue
problem of a type that is common in theoretical physics. This analogy
allows the transfer of mathematical techniques and intuitions from
physics to concrete applications of slow feature analysis, thereby
providing the means for analytical predictions and a better understanding
of simulation results. We put particular emphasis on the situation
where the input data are generated from a set of statistically independent
sources. The dependence of the optimal functions on the sources is
calculated analytically for the cases where the sources have Gaussian
or uniform distribution.},
author = {Henning Sprekeler and Dr. Laurenz Wiskott},
keywords = {slow feature analysis, unsupervised learning, invariant representations, statistically independent sources, theoretical analysis},
month = {August},
note = {preprint on cogprints},
title = {Understanding Slow Feature Analysis: A Mathematical Framework},
url = {http://cogprints.org/6223/},
year = {2008},
bdsk-url-1 = {http://cogprints.org/6223/}
}
@article{Standage07,
abstract = {We present two weight- and spike-time dependent synaptic plasticity
rules consistent with the physiological data of Bi and Poo (J Neurosci
18:10464-10472, 1998). One rule assumes synaptic saturation, while
the other is scale free. We extend previous analyses of the asymptotic
consequences of weight-dependent STDP to the case of strongly correlated
pre- and post-synaptic spiking, more closely resembling associative
learning. We further provide a general formula for the contribution
of any number of spikes to synaptic drift. Asymptotic weights are
shown to principally depend on the correlation and rate of pre- and
post-synaptic activity, decreasing with increasing rate under correlated
activity, and increasing with rate under uncorrelated activity. Spike
train statistics reveal a quantitative effect only in the pre-asymptotic
regime, and we provide a new interpretation of the relation between
BCM and STDP data.},
author = {Dominic Standage and Sajiya Jalil and Thomas Trappenberg},
doi = {10.1007/s00422-007-0152-6},
institution = {Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, Canada B3H 1W5.},
journal = {Biol Cybern},
keywords = {Action Potentials; Animals; Computer Simulation; Models, Neurological; Nerve Net; Neuronal Plasticity; Neurons; Synapses; Synaptic Transmission; Time Factors},
month = {Jun},
number = {6},
owner = {gerstner},
pages = {615--623},
pmid = {17468882},
timestamp = {2008.07.22},
title = {Computational consequences of experimentally derived spike-time and weight dependent plasticity rules.},
url = {http://dx.doi.org/10.1007/s00422-007-0152-6},
volume = {96},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1007/s00422-007-0152-6}
}
@article{Stone01,
author = {James V. Stone},
journal = {Neural Computation},
keywords = {ICA, slowness},
number = {7},
owner = {sprekeler},
pages = {1559--1574},
timestamp = {2008.04.14},
title = {Blind Source Separation Using Temporal Predictability},
volume = {13},
year = {2001}
}
@article{Sur05,
abstract = {The cerebral cortex of the human brain is a sheet of about 10 billion
neurons divided into discrete subdivisions or areas that process
particular aspects of sensation, movement, and cognition. Recent
evidence has begun to transform our understanding of how cortical
areas form, make specific connections with other brain regions, develop
unique processing networks, and adapt to changes in inputs.},
author = {Mriganka Sur and John L R Rubenstein},
doi = {10.1126/science.1112070},
institution = {Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave., 46-6237, Cambridge, MA 02139, USA. msur@mit.edu},
journal = {Science},
keywords = {Animals; Axons; Body Patterning; Brain Mapping; Cerebral Cortex; Dominance, Ocular; Gene Expression Regulation, Developmental; Human; Models, Neurological; Morphogenesis; Nerve Net; Neural Pathways; Neuronal Plasticity; Thalamus; s},
month = {Nov},
number = {5749},
owner = {cmellier},
pages = {805--810},
pii = {310/5749/805},
pmid = {16272112},
timestamp = {2008.07.23},
title = {Patterning and plasticity of the cerebral cortex.},
url = {http://dx.doi.org/10.1126/science.1112070},
volume = {310},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1126/science.1112070}
}
@article{Takacs06,
author = {Balint Takacs and Andras Lorincz},
journal = {Neurocomputing},
keywords = {hippocampus , ICA},
number = {10-12},
owner = {sprekeler},
pages = {1249--1252},
timestamp = {2008.04.14},
title = {Independent component analysis forms place cells in realistic robot simulations},
volume = {69},
year = {2006}
}
@article{Tanabe99,
abstract = {Spike timing precision in response to a subthreshold stimulation can
be enhanced by noise in ensembles of neurons [X. Pei, L. Wilkens,
and F. Moss, Phys. Rev. Lett. 77, 4679 (1996)]. We elucidate the
mechanism underlying this phenomenon by computing the membrane potential
distributions of ensembles of Hodgkin-Huxley neuron models. For small
noise amplitudes, the membrane potential distribution takes on a
Gaussian form centered on the resting potential, while for large
fluctuations, there is a significant spread to lower potentials.
These two regimes are separated by a relatively narrow band where
the distributions transit rapidly from the Gaussian-like shapes to
the spread ones. We argue that the optimal noise that maximizes the
spike timing precision is situated close to this boundary.},
author = {S. Tanabe and S. Sato and K. Pakdaman},
institution = {Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka-shi, 560-8531 Japan.},
journal = {Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics},
keywords = {Action Potentials; Computer Simulation; Excitatory Postsynaptic Potentials; Membrane Potentials; Models, Neurological; Neurons; Noise; Time Factors},
month = {Dec},
number = {6 Pt B},
owner = {gerstner},
pages = {7235--7238},
pmid = {11970667},
timestamp = {2008.07.23},
title = {Response of an ensemble of noisy neuron models to a single input.},
volume = {60},
year = {1999}
}
@article{Tanaka07,
abstract = {Photolysis of a caged Ca(2+) compound was used to characterize the
dependence of cerebellar long-term synaptic depression (LTD) on postsynaptic
Ca(2+) concentration ([Ca(2+)](i)). Elevating [Ca(2+)](i) was sufficient
to induce LTD without requiring any of the other signals produced
by synaptic activity. A sigmoidal relationship between [Ca(2+)](i)
and LTD indicated a highly cooperative triggering of LTD by Ca(2+).
The duration of the rise in [Ca(2+)](i) influenced the apparent Ca(2+)
affinity of LTD, and this time-dependent behavior could be described
by a leaky integrator process with a time constant of 0.6 s. A computational
model, based on a positive-feedback cycle that includes protein kinase
C and MAP kinase, was capable of simulating these properties of Ca(2+)-triggered
LTD. Disrupting this cycle experimentally also produced the predicted
changes in the Ca(2+) dependence of LTD. We conclude that LTD arises
from a mechanism that integrates postsynaptic Ca(2+) signals and
that this integration may be produced by the positive-feedback cycle.},
author = {Keiko Tanaka and Leonard Khiroug and Fidel Santamaria and Tomokazu Doi and Hideaki Ogasawara and Graham C R Ellis-Davies and Mitsuo Kawato and George J Augustine},
doi = {10.1016/j.neuron.2007.05.014},
institution = {Department of Neurobiology, Duke University Medical Center, Box 3209, Durham, NC 27710, USA.},
journal = {Neuron},
keywords = {Aniline Compounds; Animals; Calcium; Calcium Signaling; Cerebellar Co; Dendrites; Egtazic Acid; Feedback; Fluoresceins; Indicators and Reagents; Long-Term Synaptic Depression; Membrane Potentials; Mice; Mitogen-Activated Protein Kinase 1; Organ Culture Techniques; Patch-Clamp Techniques; Protein Kinase C; Purkinje Cells; Rats; Synapses; Synaptic Membranes; Synaptic Transmission; Time Factors; rtex},
month = {Jun},
number = {5},
owner = {gerstner},
pages = {787--800},
pii = {S0896-6273(07)00371-6},
pmid = {17553426},
timestamp = {2008.07.23},
title = {Ca2+ requirements for cerebellar long-term synaptic depression: role for a postsynaptic leaky integrator.},
url = {http://dx.doi.org/10.1016/j.neuron.2007.05.014},
volume = {54},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1016/j.neuron.2007.05.014}
}
@article{Tegner02a,
abstract = {The concept of reverberation proposed by Lorente de N? and Hebb is
key to understanding strongly recurrent cortical networks. In particular,
synaptic reverberation is now viewed as a likely mechanism for the
active maintenance of working memory in the prefrontal cortex. Theoretically,
this has spurred a debate as to how such a potentially explosive
mechanism can provide stable working-memory function given the synaptic
and cellular mechanisms at play in the cerebral cortex. We present
here new evidence for the participation of NMDA receptors in the
stabilization of persistent delay activity in a biophysical network
model of conductance-based neurons. We show that the stability of
working-memory function, and the required NMDA/AMPA ratio at recurrent
excitatory synapses, depend on physiological properties of neurons
and synaptic interactions, such as the time constants of excitation
and inhibition, mutual inhibition between interneurons, differential
NMDA receptor participation at excitatory projections to pyramidal
neurons and interneurons, or the presence of slow intrinsic ion currents
in pyramidal neurons. We review other mechanisms proposed to enhance
the dynamical stability of synaptically generated attractor states
of a reverberatory circuit. This recent work represents a necessary
and significant step towards testing attractor network models by
cortical electrophysiology.},
author = {Jesper Tegn?r and Albert Compte and Xiao-Jing Wang},
doi = {10.1007/s00422-002-0363-9},
institution = {Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA.},
journal = {Biol Cybern},
keywords = {Animals; Cerebral Cortex; Mathematics; Memory; Models, Neurological; N-Methylaspartate; Nerve Net; Neurons; Receptors, AMPA; Receptors, N-Methyl-D-Aspartate; Synapses; Time Factors},
month = {Dec},
number = {5-6},
owner = {gerstner},
pages = {471--481},
pmid = {12461636},
timestamp = {2008.07.23},
title = {The dynamical stability of reverberatory neural circuits.},
url = {http://dx.doi.org/10.1007/s00422-002-0363-9},
volume = {87},
year = {2002},
bdsk-url-1 = {http://dx.doi.org/10.1007/s00422-002-0363-9}
}
@article{Tononi98,
abstract = {Conventional approaches to understanding consciousness are generally
concerned with the contribution of specific brain areas or groups
of neurons. By contrast, it is considered here what kinds of neural
processes can account for key properties of conscious experience.
Applying measures of neural integration and complexity, together
with an analysis of extensive neurological data, leads to a testable
proposal-the dynamic core hypothesis-about the properties of the
neural substrate of consciousness.},
author = {G. Tononi and G. M. Edelman},
institution = {Neurosciences Institute, 10640 John J. Hopkins Drive, San Diego, CA 92121, USA. tononi@nsi.edu},
journal = {Science},
keywords = {Animals; Attention; Brain; Brain Mapping; Cerebral Cortex; Consciousness; Humans; Memory; Models, Neurological; Neural Pathways; Neurons; Thalamus},
month = {Dec},
number = {5395},
owner = {cmellier},
pages = {1846--1851},
pmid = {9836628},
timestamp = {2008.07.23},
title = {Consciousness and complexity.},
volume = {282},
year = {1998}
}
@article{Touboul08,
author = {Touboul, Jonathan},
date-added = {2008-03-31 11:49:45 +0200},
date-modified = {2008-03-31 11:52:07 +0200},
journal = {SIAM Journal on Applied Mathematics},
keywords = {neuron models; dynamical system analysis; nonlinear dynamics; Hopf bifurcation; saddle-node bifurcation; BogdanovTakens bifurcation},
number = {4},
owner = {sprekeler},
pages = {1045-1079},
timestamp = {2008.04.14},
title = {Bifurcation Analysis of a General Class of Nonlinear Integrate-and-Fire Neurons},
volume = {68},
year = {2008}
}
@article{Toussaint06,
abstract = {Experimental studies of reasoning and planned behavior have provided
evidence that nervous systems use internal models to perform predictive
motor control, imagery, inference, and planning. Classical (model-free)
reinforcement learning approaches omit such a model; standard sensorimotor
models account for forward and backward functions of sensorimotor
dependencies but do not provide a proper neural representation on
which to realize planning. We propose a sensorimotor map to represent
such an internal model. The map learns a state representation similar
to self-organizing maps but is inherently coupled to sensor and motor
signals. Motor activations modulate the lateral connection strengths
and thereby induce anticipatory shifts of the activity peak on the
sensorimotor map. This mechanism encodes a model of the change of
stimuli depending on the current motor activities. The activation
dynamics on the map are derived from neural field models. An additional
dynamic process on the sensorimotor map (derived from dynamic programming)
realizes planning and emits corresponding goal-directed motor sequences,
for instance, to navigate through a maze.},
author = {Marc Toussaint},
doi = {10.1162/089976606776240995},
institution = {School of Informatics, University of Edinburgh, Edinburgh, Scotland, UK. mtoussai@inf.ed.ac.uk},
journal = {Neural Comput},
keywords = {Action Potentials; Animals; Brain; Cell Communication; Cognition; Extremities; Humans; Models, Neurological; Movement; Nerve Net; Neural Pathways; Neurons; Orientation; Psychomotor Performance; Sensation; Synaptic Transmission},
month = {May},
number = {5},
owner = {gerstner},
pages = {1132--1155},
pmid = {16595060},
timestamp = {2008.07.23},
title = {A sensorimotor map: modulating lateral interactions for anticipation and planning.},
url = {http://dx.doi.org/10.1162/089976606776240995},
volume = {18},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1162/089976606776240995}
}
@article{Toyoizumi06,
abstract = {We evaluate the Fisher information of a population of model neurons
that receive dynamical input and interact via spikes. With spatially
independent threshold noise, the spike-based Fisher information that
summarizes the information carried by individual spike timings has
a particularly simple analytical form. We calculate the loss of information
caused by abandoning spike timing and study the effect of synaptic
connections on the Fisher information. For a simple spatiotemporal
input, we derive the optimal recurrent connectivity that has a local
excitation and global inhibition structure. The optimal synaptic
connections depend on the spatial or temporal feature of the input
that the system is designed to code.},
author = {Taro Toyoizumi and Kazuyuki Aihara and Shun-ichi Amari},
institution = {Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 113-8656, Japan. taro.toyoizumi@brain.riken.jp},
journal = {Phys Rev Lett},
keywords = {Algorithms; Electrophysiology; Models, Neurological; Models, Statistical; Neurons; Population; Stochastic Processes; Synapses},
month = {Sep},
number = {9},
owner = {cmellier},
pages = {098102},
pmid = {17026405},
timestamp = {2008.07.23},
title = {Fisher information for spike-based population decoding.},
volume = {97},
year = {2006}
}
@article{Urbanczik09,
abstract = {Population coding is widely regarded as an important mechanism for
achieving reliable behavioral responses despite neuronal variability.
However, standard reinforcement learning slows down with increasing
population size, as the global reward signal becomes less and less
related to the performance of any single neuron. We found that learning
speeds up with increasing population size if, in addition to global
reward, feedback about the population response modulates synaptic
plasticity.},
author = {Robert Urbanczik and Walter Senn},
doi = {10.1038/nn.2264},
institution = {Department of Physiology, University of Bern, B¸hlplatz 5, CH-3012 Bern, Switzerland.},
journal = {Nat Neurosci},
keywords = {Action Potentials; Learning; Models, Neurological; Neuronal Plasticity; Neurons; Reinforcement (Psychology)},
month = {Mar},
number = {3},
owner = {sprekeler},
pages = {250--252},
pii = {nn.2264},
pmid = {19219040},
timestamp = {2009.04.21},
title = {Reinforcement learning in populations of spiking neurons.},
url = {http://dx.doi.org/10.1038/nn.2264},
volume = {12},
year = {2009},
bdsk-url-1 = {http://dx.doi.org/10.1038/nn.2264}
}
@article{Vogels05,
abstract = {Transmission of signals within the brain is essential for cognitive
function, but it is not clear how neural circuits support reliable
and accurate signal propagation over a sufficiently large dynamic
range. Two modes of propagation have been studied: synfire chains,
in which synchronous activity travels through feedforward layers
of a neuronal network, and the propagation of fluctuations in firing
rate across these layers. In both cases, a sufficient amount of noise,
which was added to previous models from an external source, had to
be included to support stable propagation. Sparse, randomly connected
networks of spiking model neurons can generate chaotic patterns of
activity. We investigate whether this activity, which is a more realistic
noise source, is sufficient to allow for signal transmission. We
find that, for rate-coded signals but not for synfire chains, such
networks support robust and accurate signal reproduction through
up to six layers if appropriate adjustments are made in synaptic
strengths. We investigate the factors affecting transmission and
show that multiple signals can propagate simultaneously along different
pathways. Using this feature, we show how different types of logic
gates can arise within the architecture of the random network through
the strengthening of specific synapses.},
author = {Tim P Vogels and L. F. Abbott},
doi = {10.1523/JNEUROSCI.3508-05.2005},
institution = {Volen Center for Complex Systems and Department of Biology, Brandeis University, Waltham, Massachusetts 02454-9110, USA. vogels@brandeis.edu},
journal = {J Neurosci},
keywords = {Action Potentials; Ion Channel Gating; Logic; Models, Neurological; Neural Networks (Computer); Neural Pathways; Neurons; Signal Transduction},
month = {Nov},
number = {46},
owner = {cmellier},
pages = {10786--10795},
pii = {25/46/10786},
pmid = {16291952},
timestamp = {2008.07.23},
title = {Signal propagation and logic gating in networks of integrate-and-fire neurons.},
url = {http://dx.doi.org/10.1523/JNEUROSCI.3508-05.2005},
volume = {25},
year = {2005},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.3508-05.2005}
}
@article{Waters06,
abstract = {Neurons are continually exposed to background synaptic activity in
vivo. This is thought to influence neural information processing,
but background levels of excitation and inhibition remain controversial.
Here we show, using whole-cell recordings in anesthetized rats, that
spontaneous depolarizations ("Up states") in neocortical pyramidal
neurons are driven by sparse, mostly excitatory synaptic activity
(less than five inputs per millisecond; approximately 10\% inhibitory).
The mean synaptic conductance change is small (<10 nS at the soma)
and opposed by anomalous rectification, resulting in a net increase
in input resistance during Up states. These conditions enhance the
effectiveness of each synapse at depolarized potentials. Hence, neocortical
networks are relatively quiet at rest, and the effect of synaptic
background is weaker than previously thought.},
author = {Jack Waters and Fritjof Helmchen},
doi = {10.1523/JNEUROSCI.2152-06.2006},
journal = {J Neurosci},
keywords = {Action Potentials; Animals; Biological Clocks; Computer Simulation; Differential Threshold; Excitatory Postsynaptic Potentials; Membrane Potentials; Models, Neurological; Models, Statistical; Neocortex; Neural Inhibition; Neurons; Rats; Rats, Wistar; Synaptic Transmission},
month = {Aug},
number = {32},
owner = {tomm},
pages = {8267--8277},
pii = {26/32/8267},
pmid = {16899721},
timestamp = {2008.12.31},
title = {Background synaptic activity is sparse in neocortex.},
url = {http://dx.doi.org/10.1523/JNEUROSCI.2152-06.2006},
volume = {26},
year = {2006},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.2152-06.2006}
}
@article{Wespatat04,
abstract = {Synaptic modifications depend on the amplitude and temporal relations
of presynaptic and postsynaptic activation. The interactions among
these variables are complex and hard to predict when neurons engage
in synchronized high-frequency oscillations in the beta and gamma
frequency range, as is often observed during signal processing in
the cerebral cortex. Here we investigate in layer II/III pyramidal
cells of rat visual cortex slices how synapses change when synchronized,
oscillatory multifiber activity impinges on postsynaptic neurons
during membrane potential (V(m)) oscillations at 20 and 40 Hz. Synapses
underwent long-term potentiation (LTP) when EPSPs coincided with
the peaks of the V(m) oscillations but exhibited long-term depression
(LTD) when EPSPs coincided with the troughs. The induction of LTP
but not of LTD was NMDA receptor dependent, required additional activation
of muscarinic receptors in older animals, and persisted in a kainate-driven
increased conductance state. Thus, even when neuronal networks engage
in high-frequency oscillations, synaptic plasticity remains exquisitely
sensitive to the timing of discharges. This is an essential prerequisite
for theories which assume that precise synchronization of discharges
serves as signature of relatedness in distributed processing.},
author = {Val?rie Wespatat and Frank Tennigkeit and Wolf Singer},
doi = {10.1523/JNEUROSCI.2221-04.2004},
institution = {Department of Neurophysiology, Max-Planck-Institute for Brain Research, D-60528 Frankfurt/Main, Germany.},
journal = {J Neurosci},
keywords = {Age Factors; Animals; Biological Clocks; Excitatory Postsynaptic Potentials; Long-Term Potentiation; Long-Term Synaptic Depression; Membrane Potentials; Nerve Net; Neurons; Patch-Clamp Techniques; Pyramidal Cells; Rats; Rats, Wistar; Receptors, Muscarinic; Receptors, N-Methyl-D-Aspartate; Synapses; Time Factors; Visual Cortex},
month = {Oct},
number = {41},
owner = {gerstner},
pages = {9067--9075},
pii = {24/41/9067},
pmid = {15483125},
timestamp = {2008.07.23},
title = {Phase sensitivity of synaptic modifications in oscillating cells of rat visual cortex.},
url = {http://dx.doi.org/10.1523/JNEUROSCI.2221-04.2004},
volume = {24},
year = {2004},
bdsk-url-1 = {http://dx.doi.org/10.1523/JNEUROSCI.2221-04.2004}
}
@article{Wiemer00,
abstract = {Stimulus representation is a functional interpretation of early sensory
cortices. Early sensory cortices are subject to stimulus-induced
modifications. Common models for stimulus-induced learning within
topographic representations are based on the stimuli's spatial structure
and probability distribution. Furthermore, we argue that average
temporal stimulus distances reflect the stimuli's relatedness. As
topographic representations reflect the stimuli's relatedness, the
temporal structure of incoming stimuli is important for the learning
in cortical maps. Motivated by recent neurobiological findings, we
present an approach of cortical self-organization that additionally
takes temporal stimulus aspects into account. The proposed model
transforms average interstimulus intervals into representational
distances. Thereby, neural topography is related to stimulus dynamics.
This offers a new time-based interpretation of cortical maps. Our
approach is based on a wave-like spread of cortical activity. Interactions
between dynamics and feedforward activations lead to shifts of neural
activity. The psychophysical saltation phenomenon may represent an
analogue to the shifts proposed here. With regard to cortical plasticity,
we offer an explanation for neurobiological findings that other models
cannot explain. Moreover, we predict cortical reorganizations under
new experimental, spatiotemporal conditions. With regard to psychophysics,
we relate the saltation phenomenon to dynamics and interaction in
early sensory cortices and predict further effects in the perception
of spatiotemporal stimuli.},
author = {J. Wiemer and F. Spengler and F. Joublin and P. Stagge and S. Wacquant},
institution = {Institut f?r Neuroinformatik, Ruhr-Universit?t Bochum, D-44780 Bochum, Germany. jan.weimer@neuroinformatik.ruhr-uni-bochum.de},
journal = {Biol Cybern},
keywords = {Animals; Brain Mapping; Computer Simulation; Haplorhini; Kinetics; Learning; Models, Neurological; Nerve Net; Neuronal Plasticity; Neurons; Physical Stimulation; Psychophysics; Somatosensory Cortex; Space Perception; Synapses; Time Factors; Touch},
month = {Feb},
number = {2},
owner = {gerstner},
pages = {173--187},
pii = {00820173.422},
pmid = {10664104},
timestamp = {2008.07.23},
title = {Learning cortical topography from spatiotemporal stimuli.},
volume = {82},
year = {2000}
}
@article{Wolfe08,
abstract = {Rats discriminate surface textures using their whiskers (vibrissae),
but how whiskers extract texture information, and how this information
is encoded by the brain, are not known. In the resonance model, whisker
motion across different textures excites mechanical resonance in
distinct subsets of whiskers, due to variation across whiskers in
resonance frequency, which varies with whisker length. Texture information
is therefore encoded by the spatial pattern of activated whiskers.
In the competing kinetic signature model, different textures excite
resonance equally across whiskers, and instead, texture is encoded
by characteristic, nonuniform temporal patterns of whisker motion.
We tested these models by measuring whisker motion in awake, behaving
rats whisking in air and onto sandpaper surfaces. Resonant motion
was prominent during whisking in air, with fundamental frequencies
ranging from approximately 35 Hz for the long Delta whisker to approximately
110 Hz for the shorter D3 whisker. Resonant vibrations also occurred
while whisking against textures, but the amplitude of resonance within
single whiskers was independent of texture, contradicting the resonance
model. Rather, whiskers resonated transiently during discrete, high-velocity,
and high-acceleration slip-stick events, which occurred prominently
during whisking on surfaces. The rate and magnitude of slip-stick
events varied systematically with texture. These results suggest
that texture is encoded not by differential resonant motion across
whiskers, but by the magnitude and temporal pattern of slip-stick
motion. These findings predict a temporal code for texture in neural
spike trains.},
author = {Jason Wolfe and Dan N Hill and Sohrab Pahlavan and Patrick J Drew and David Kleinfeld and Daniel E Feldman},
doi = {10.1371/journal.pbio.0060215},
journal = {PLoS Biol},
keywords = {Afferent Pathways; Animals; Evoked Potentials, Somatosensory; Exploratory Behavior; Mechanoreceptors; Models, Biological; Neural Pathways; Rats; Somatosensory Cortex; Vibration; Vibrissae},
month = {Aug},
number = {8},
owner = {tomm},
pages = {e215},
pii = {08-PLBI-RA-0645},
pmid = {18752354},
timestamp = {2009.01.02},
title = {Texture coding in the rat whisker system: slip-stick versus differential resonance.},
url = {http://dx.doi.org/10.1371/journal.pbio.0060215},
volume = {6},
year = {2008},
bdsk-url-1 = {http://dx.doi.org/10.1371/journal.pbio.0060215}
}
@article{Yeckel90,
abstract = {For the past 3 decades, functional characterizations of the hippocampus
have emphasized its intrinsic trisynaptic circuitry, which consists
of successive excitatory projections from the entorhinal cortex to
the dentate gyrus, from granule cells of the dentate to the CA3/4
pyramidal cell region, and from CA3/4 to the CA1/2 pyramidal cell
region. Despite unequivocal anatomical evidence for a monosynaptic
projection from entorhinal to CA3 and CA1/2, few in vivo electrophysiological
studies of the direct pathway have been reported. In the experiments
presented here, we stimulated axons of entorhinal cortical neurons
in vivo and recorded evoked single unit and population spike responses
in the dentate, CA3, and CA1 of hippocampus, to determine if pyramidal
cells are driven primarily via the monosynaptic or trisynaptic pathways.
Our results show that neurons within the three subfields of the hippocampus
discharge simultaneously in response to input from a given subpopulation
of entorhinal cortical neurons and that the initial monosynaptic
excitation of pyramidal cells then is followed by weaker excitatory
volleys transmitted through the trisynaptic pathway. In addition,
we found that responses of CA3 pyramidal cells often precede those
of dentate granule cells and that excitation of CA3 and CA1 pyramidal
cells can occur in the absence of granule cell excitation. In total,
these results argue for a different conceptualization of the functional
organization of the hippocampus with respect to the propagation of
activity through its intrinsic pathways: input from the entorhinal
cortex initiates a two-phase feedforward excitation of pyramidal
cells, with the dentate gyrus providing feedforward excitation of
CA3, and with both the dentate and CA3 providing feedforward excitation
of CA1.},
author = {M. F. Yeckel and T. W. Berger},
journal = {Proc Natl Acad Sci U S A},
keywords = {Afferent Pathways; Animals; Electric Stimulation; Evoked Potentials; Hippocampus; Models, Neurological; Neurons; Pyramidal Tracts; Rabbits; Synapses},
month = {Aug},
number = {15},
owner = {tomm},
pages = {5832--5836},
pmid = {2377621},
timestamp = {2008.06.23},
title = {Feedforward excitation of the hippocampus by afferents from the entorhinal cortex: redefinition of the role of the trisynaptic pathway.},
volume = {87},
year = {1990}
}
@article{Zhaoping03,
abstract = {Residual micro-saccades, tremor and fixation errors imply that, on
different trials in visual tasks, stimulus arrays are inevitably
presented at different positions on the retina. Positional variation
is likely to be specially important for tasks involving visual hyperacuity,
because of the severe demands that these tasks impose on spatial
resolution. In this paper, we show that small positional variations
lead to a structural change in the nature of the ideal observer's
solution to a hyperacuity-like visual discrimination task such that
the optimal discriminator depends quadratically rather than linearly
on noisy neural activities. Motivated by recurrent models of early
visual processing, we show how a recurrent preprocessor of the noisy
activities can produce outputs which, when passed through a linear
discriminator, lead to better discrimination even when the positional
variations are much larger than the threshold acuity of the task.
Since, psychophysically, hyperacuity typically improves greatly over
the course of perceptual learning, we discuss our model in the light
of results on the speed and nature of learning.},
author = {L. Zhaoping and Michael H Herzog and Peter Dayan},
institution = {Department of Psychology, University College, London WC1E 6BT, UK.},
journal = {Network},
keywords = {Animals; Discrimination Learning; Models, Neurological; Motion Perception; Nonlinear Dynamics; Pattern Recognition, Visual; Photic Stimulation; Visual Acuity; Visual Pathways},
month = {May},
number = {2},
owner = {gerstner},
pages = {233--247},
pmid = {12790183},
timestamp = {2008.07.23},
title = {Nonlinear ideal observation and recurrent preprocessing in perceptual learning.},
volume = {14},
year = {2003}
}
@article{Ziehe98,
author = {Ziehe, A. and M{\"u}ller, K.R.},
journal = {Proc. Int. Conf. on Artificial Neural Networks (ICANN '98)},
keywords = {ICA},
owner = {sprekeler},
pages = {675--680},
timestamp = {2008.04.14},
title = {{TDSEP--an efficient algorithm for blind separation using time structure}},
year = {1998}
}
@article{Zou07,
abstract = {Spike-timing dependent plasticity (STDP) is a type of synaptic modification
found relatively recently, but the underlying biophysical mechanisms
are still unclear. Several models of STDP have been proposed, and
differ by their implementation, and in particular how synaptic weights
saturate to their minimal and maximal values. We analyze here kinetic
models of transmitter-receptor interaction and derive a series of
STDP models. In general, such kinetic models predict progressive
saturation of the weights. Various forms can be obtained depending
on the hypotheses made in the kinetic model, and these include a
simple linear dependence on the value of the weight ("soft bounds"),
mixed soft and abrupt saturation ("hard bound"), or more complex
forms. We analyze in more detail simple soft-bound models of Hebbian
and anti-Hebbian STDPs, in which nonlinear spike interactions (triplets)
are taken into account. We show that Hebbian STDPs can be used to
selectively potentiate synapses that are correlated in time, while
anti-Hebbian STDPs depress correlated synapses, despite the presence
of nonlinear spike interactions. This correlation detection enables
neurons to develop a selectivity to correlated inputs. We also examine
different versions of kinetics-based STDP models and compare their
sensitivity to correlations. We conclude that kinetic models generally
predict soft-bound dynamics, and that such models seem ideal for
detecting correlations among large numbers of inputs.},
author = {Quan Zou and Alain Destexhe},
doi = {10.1007/s00422-007-0155-3},
institution = {Integrative and Computational Neuroscience Unit, CNRS, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France.},
journal = {Biol Cybern},
keywords = {Action Potentials; Animals; Kinetics; Models, Neurological; Neuronal Plasticity; Neurons; Nonlinear Dynamics; Synapses; Synaptic Transmission; Time Factors},
month = {Jul},
number = {1},
owner = {cmellier},
pages = {81--97},
pmid = {17530277},
timestamp = {2008.07.23},
title = {Kinetic models of spike-timing dependent plasticity and their functional consequences in detecting correlations.},
url = {http://dx.doi.org/10.1007/s00422-007-0155-3},
volume = {97},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1007/s00422-007-0155-3}
}
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