[1] | R. deCharms and M. Merzenich. Primary cortical representation of sounds by the coordination of action-potential timing. Nature, 381:610-613, 1996. [ bib ] |
[2] | H. Abarbanel, R. Huerta, and M. Rabinovich. Dynamical model of long-term synaptic plasticity. Proc. Natl. Academy of Sci. USA, 59(10137-10143), 2002. [ bib ] |
[3] | H. Abarbanel, R. Huerta, and M. Rabinovich. Dynamical model of long-term synaptic plasticity. Proc. Natl. Academy of Sci. USA, 59(10137-10143), 2002. [ bib ] |
[4] | H. Abarbanel, G. Mindlin, S. Talathi, L. Gibb, and M. Rabinovich. Dynamic model of birdsong maintenance and control. Physical Review E, 70:051911, 2004. [ bib ] |
[5] | H. Abarbanel, S. Talathi, L. Gibb, and M. Rabinovich. Synaptic plasticity with discrete state synapses. Phys. Rev. E, 72:031914, 2005. [ bib ] |
[6] | H. D. I. Abarbanel, L. Gibb, G. B. Mindlin, and S. Talathi. Mapping neural architectures onto acoustic featurs of birdsong. J. Neurophysiology, 92:96-110, 2004. [ bib ] |
[7] | L. Abbott. Decoding neuronal firing and modeling neural networks. Quart. Refv. Biophys., 27:291-331, 1994. [ bib ] |
[8] | L. Abbott and S. Song. Temporally asymmetric hebbian learning, spike timing and neuronal response variability. In S. Kearns, S. A. . Solla, and D. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 69-75. MIT-Press, Cambridge, 1999. [ bib ] |
[9] | L. F. Abbott. Realistic synaptic inputs for model neural networks. Network, 2:245-258, 1991. [ bib ] |
[10] | L. F. Abbott. A network of oscillators. J. Phys. A, 23:3835-3859, 1990. [ bib ] |
[11] | L. F. Abbott and K. I. Blum. Functional significance of long-term potentiation for sequence learning and prediction. Cerebral Cortex, 6:406-416, 1996. [ bib ] |
[12] | L. F. Abbott, E. Fahri, and S. Gutmann. The path integral for dendritic trees. Biol. Cybern., 66:49-60, 1991. [ bib ] |
[13] | L. F. Abbott and T. B. Kepler. Model neurons: from Hodgkin-Huxley to Hopfield. In L. Garrido, editor, Statistical Mechanics of Neural Networks. Springer, Berlin, 1990. [ bib ] |
[14] | L. F. Abbott and S. B. Nelson. Synaptic plastictiy - taming the beast. Nature Neuroscience, 3:1178-1183, 2000. [ bib ] |
[15] | L. F. Abbott and S. B. Nelson. Synaptic plasticity: Taming the beast. Nature Neuroscience, 3:1178-1183, 2000. [ bib ] |
[16] | L. F. Abbott and S. B. Nelson. Synaptic plasticity: taming the beast. Nature Neuroscience, 3(Suppl.):1178-1183, 2000. [ bib ] |
[17] | L. F. Abbott and W. G. Regehr. Synaptic computation. Nature, 431(7010):796-803, Oct. 2004. [ bib | http ] |
[18] | L. F. Abbott, J. A. Varela, K. Sen, and S. B. Nelson. Synaptic depression and cortical gain control. Science, 275:220-224, 1997. [ bib ] |
[19] | L. F. Abbott and C. van Vreeswijk. Asynchronous states in a network of pulse-coupled oscillators. Phys. Rev. E, 48:1483-1490, 1993. [ bib ] |
[20] | M. Abeles. Firing rates and well-timed events. In E. Domany, K. Schulten, and J. L. van Hemmen, editors, Models of Neural Networks 2, chapter 3, pages 121-140. Springer, New York, 1994. [ bib ] |
[21] | M. Abeles. Corticonics. Cambridge University Press, Cambridge, 1991. [ bib ] |
[22] | M. Abeles. Local cortical circuits. Springer-Verlag, Berlin Heidelberg New York, 1982. [ bib ] |
[23] | M. Abeles, H. Bergman, E. Margalit, and E. Vaadia. Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J. of Neurophysiology, 70:1629-1638, 1993. [ bib ] |
[24] | M. Abeles and Y. Lass. Transmission of information by the axon. Biol. Cybern., 19:121-125, 1975. [ bib ] |
[25] | M. Abeles, Y. Prut, H. Bergmann, E. Vaadia, and A. Aertsen. Integration, synchronicity, and periodicity. In A. Aertsen, editor, Brain Theory, pages 149-181. Elsevier Science Publishers, 1993. [ bib ] |
[26] | M. Abeles, E. Vaadia, H. Bergman, Y.Prut, I. Haalman, and H. Slovin. Dynamics of neuronal interactions in the frontal cortex of behaving monkeys. Concepts in Neuroscience, 4:131-158, 1993. [ bib ] |
[27] | W. Abraham. How long will long-term potentiation last? Philosophical Transactions R. Soc. Lond B: Biological Sciences, 358:735 - 744, 2003. [ bib ] |
[28] | W. Abraham, B. Logan, J. Greenwood, and M. Dragunow. Induction and experience-dependent consolidation of stable long-term potentiation lasting months in the hippocampus. J. Neuroscience, 22:9626 - 9634, 2002. [ bib ] |
[29] | W. C. Abraham. Metaplasticity: tuning synapses and networks for plasticity. Nat Rev Neurosci, 9(5):387, May 2008. [ bib | DOI | http ] |
[30] | P. Achard and E. De Schutter. Complex parameter landscape for a complex neuron model. PLoS Comput Biol, 2(7):e94, 2006. [ bib | DOI ] |
[31] | J. Adams and S. Dudek. Late-phase long-term potentiation: getting to the nucleus. Nature Reviews Neuroscience, 6:737-743, 2005. [ bib ] |
[32] | E. H. Adelsen and J. R. Bergen. Spatiotemporal energy models for the perception of motion. Journal Optical Society of America A, 2(2):284-299, 1985. [ bib ] |
[33] | E. D. Adrian. The basis of sensation. W.W. Norton, New York, 1928. [ bib ] |
[34] | E. D. Adrian. The impulses produced by sensory nerve endings. J. Physiol. (London), 61:49-72, 1926. [ bib ] |
[35] | A. Aertsen and M. Arndt. Response synchronization in the visual cortex. Current Opinion in Neurobiology, 3:586-594, 1993. [ bib ] |
[36] | A. Aertsen, G. Gerstein, and P. Johannesma. From neuron to assembly: Neuronal organization and stimulus representation. In G. Palm and A. Aertsen, editors, Brain Theory, pages 7-24. Springer-Verlag, Berlin Heidelberg New York, 1986. [ bib ] |
[37] | B. Aguera y Arcas and A. Fairhall. What causes a neuron to spike? Neural Computation, 15:1789-1803, 2003. [ bib ] |
[38] | B. Aguera y Arcas, A. Fairhall, and W. Bialek. Computation in a single neuron: Hodgkin-huxley revisited. Neural Computation, 15:1715-1749, 2003. [ bib ] |
[39] | B. Aguera y Arcas, A. L. Fairhall, and W. Bialek. Computation in a single neuron: Hodgkin and huxley revisited. Neural Comput, 15(8):1715-1749, 2003. [ bib | DOI ] |
[40] | M. Ahissar and S. Hochstein. The reverse hierarchy theory of visual perceptual learning. Trends in Cognitive Sciences, 8:457-464, 2004. [ bib ] |
[41] | J. Albus. Brain, Behavior, Robotics. Byte Books, Peterborough, 1981. [ bib ] |
[42] | J. Albus. A theory of cerebellar function. J. Mathematical Biosciences, 10:25-61, 1971. [ bib ] |
[43] | K. Albus and W. Wolf. Early post-natal development of neuronal function in the kitten's visual cortex: a laminar analysis. Journal of Physiology, 348:153-185, 1984. [ bib ] |
[44] | J. Alonso and L. Martinez. Functional connectivity between simple cells and complex cells in cat striate cortex. Nat Neurosci, 1(5):395-403, 1998. [ bib ] |
[45] | D. Amaral. Emerging principles of intrinsic hippocampal organization. Current Opinion in Neurobiology, 3:225-229, 1993. [ bib ] |
[46] | S. Amari. Neural theory of assocation and concept-formation. Biol. Cybern., 26:175-185, 1977. [ bib ] |
[47] | S. Amari. A mathematical foundation of statistical neurodynamics. SIAM J. Applied Mathematics, 33:95-126, 1977. [ bib ] |
[48] | S. Amari. Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern., 27:77-87, 1977. [ bib ] |
[49] | S. Amari. A method of statistical neurodynamics. Kybernetik, 14:201-215, 1974. [ bib ] |
[50] | S. Amari. Characteristics of random nets of analog neuron-like elements. IEEE transactions systems, man, cybernetics, 2:643-657, 1972. [ bib ] |
[51] | D. Amit and S. Fusi. Learning in neural networks with material synapses. Neural Computation, 6:957-982, 1994. [ bib ] |
[52] | D. J. Amit. Modeling brain function. Cambridge University Press, Cambridge UK, 1989. [ bib ] |
[53] | D. J. Amit and N. Brunel. Dynamics of a recurrent network of spiking neurons before and following learning. Network, 8:373-404, 1997. [ bib ] |
[54] | D. J. Amit and N. Brunel. A model of spontaneous activity and local delay activity during delay periods in the cerebral cortex. Cerebral Cortex, 7:237-252, 1997. [ bib ] |
[55] | D. J. Amit, H. Gutfreund, and H. Sompolinsky. Statistical mechanics of neural networks near saturation. Ann Phys (NY), 173:30-67, 1987. [ bib ] |
[56] | D. J. Amit, H. Gutfreund, and H. Sompolinsky. Information storage in neural networks with low levels of activity. Phys. Rev. A, 35:2293-2303, 1987. [ bib ] |
[57] | D. J. Amit, H. Gutfreund, and H. Sompolinsky. Spin-glass models of neural networks. Phys. Rev. A, 32:1007-1032, 1985. [ bib ] |
[58] | D. J. Amit, H. Gutfreund, and H. Sompolinsky. Storing infinite number of patterns in a spin-glass model of neural networks. Phys. Rev. Lett., 55:1530-1533, 1985. [ bib ] |
[59] | D. J. Amit and M. V. Tsodyks. Effective neurons and attractor neural networks in cortical environment. Network, 3:121-137, 1992. [ bib ] |
[60] | D. J. Amit and M. V. Tsodyks. Quantitative study of attractor neural networks retrieving at low spike rates. i: Substrate - spikes, rates, and neuronal gain. Network, 2:259-273, 1991. [ bib ] |
[61] | J. A. Anderson. A simple neural network generating an interactive memory. Math. Biosc., 14:197-220, 1972. [ bib ] |
[62] | J. A. Anderson. A memory storage model utilizing spatial correlation functions. Kybernetik, 5:113-119, 1968. [ bib ] |
[63] | M. I. Anderson and K. J. Jeffery. Heterogeneous modulation of place cell firing by changes in context. Journal of Neuroscience, 23(26):8827-8835, Oct. 2003. [ bib ] |
[64] | A. Anzai, X. Peng, and D. Van Essen. Neurons in monkey visual area V2 encode combinations of orientations. Nature Neuroscience, 10:1313-1321, 2007. [ bib ] |
[65] | P. Apicella, T. Ljunberg, E. Scarnati, and W. Schultz. Responses to reward in monkey dorsal and ventral striatum. Exp. Brain Research, 85:491-500, 1991. [ bib ] |
[66] | P. Apicella, E. Scarnati, T. Ljunberg, and W. Schultz. Neuronal activity in monkey striatum related to the expectation of predictable environmental events. J. Neurophysiol., 68:945-960, 1992. [ bib ] |
[67] | P. Appleby and T. Elliot. Stable competitive dynamics emerge from multispike interactions in a stochastic model of spike-timing-dependent plasticity. Neural Computation, 18(10):2414-2464, 2006. [ bib ] |
[68] | P. Appleby and T. Elliot. Synaptic and temporal ensemble interpretation of spike-timing-dependent plasticity. Neural Computation, 17(11):2316-2336, 2005. [ bib ] |
[69] | P. Appleby and T. Elliott. Synaptic and temporal ensemble interpretation of spike-timing-dependent plasticity. Neural Computation, 17:2316-2336, 2005. [ bib ] |
[70] | I. E. T. de Araujo, E. T. Rolls, and S. M. Stringer. A view model which accounts for the spatial fields of hippocampal primate spatial view cells and rat place cells. Hippocampus, 11:699-706, 2001. [ bib ] |
[71] | M. A. Arbib. The handbook of brain theory and neural networks. MIT Press, Cambridge, MA, 1995. [ bib ] |
[72] | G. W. Arbuthnit and J. R. Wickens. Dopamine cells are neurons too. Trends in Neurosciences, 19:279, 1996. [ bib ] |
[73] | A. Arieli, A. STerkin, A. Grinvald, and A. Aertsen. Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses. Science, 273:1868-1871, 1996. [ bib ] |
[74] | A. Arleo and W. Gerstner. Hippocampal spatial model for state space representation in robotic reinforcement learning. In M. A. Wiering, editor, Proceedings of the fifth European Workshop on Reinforcement learning, pages 1-3. CKI, Utrecht University, 2001. [ bib ] |
[75] | A. Arleo and W. Gerstner. Spatial orientation in navigating agents: Modeling head-direction cells. Neurocomputing, 38-40:1059-1065, 2001. [ bib ] |
[76] | A. Arleo and W. Gerstner. Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity. Biological Cybernetics, 83(3):287-299, 2000. [ bib ] |
[77] | A. Arleo and W. Gerstner. A vision-driven model of hippocampal place cells and temporally asymmetric LTP-induction for action learning. In ICANN'99 Artificial Neural Networks, pages 132-137. IEE Conference Publication, 1999. [ bib ] |
[78] | A. Arleo, F. Smeraldi, and W. Gerstner. Cognitive navigation based on non-uniform gabor space sampling, unsupervised growing networks, and reinforcement learning. IEEE Transactions on Neural Networks,, 15:639-652, 2004. [ bib ] |
[79] | A. Arleo, M. Zugaro, C. Déjean, E. Burguière, M. Khamassi, and S. I. Wiener. Rat anterodorsal thalamic head direction neurons depend upon dynamic visual signals to select anchoring landmark cues. Europ Journal of Neuroscience, 20:530-536, 2004. [ bib ] |
[80] | M. Arsiero, H.-R. Luscher, B. N. Lundstrom, and M. Giugliano. The impact of input fluctuations on the frequency-current relationships of layer 5 pyramidal neurons in the rat medial prefrontal cortex. J Neurosci, 27(12):3274-3284, 2007. [ bib | DOI ] |
[81] | A. Artola, S. Bröcher, and W. Singer. Different voltage dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature, 347:69-72, 1990. [ bib ] |
[82] | A. Artola and W. Singer. Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation. Trends Neurosci., 16(11):480-487, 1993. [ bib ] |
[83] | V. Astakhov, M. Hasler, T. Kapitaniak, A. Shabunin, and V. Anishchenko. Effect of parameter mismatch on the mechanism of chaos synchronization loss in coupled systems. Physical Revue E, 58:5620-5628, 1998. [ bib ] |
[84] | J. Atick and A. Redlich. Towards a theory of early visual processing. Neural Computation, 4:559-572, 1990. [ bib ] |
[85] | J. J. Atick. Could information theory provide an ecological theory of sensory processing? Network: Computation in Neural Systems, 3:213-251, 1992. [ bib ] |
[86] | F. Attneave. Some informational aspects of visual perception. Psychological Review, 61(3):183-192, 1954. [ bib ] |
[87] | E. Av-Ron, H. Parnas, and L. Segel. A minimal biophysical model for an excitable and oscillatory neuron. Biol. Cybern., 65:487-500, 1991. [ bib ] |
[88] | Y. Aviel and W. Gerstner. From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness. Phys. Rev. E, 73:51908, 2006. [ bib ] |
[89] | Y. Aviel and W. Gerstner. From spiking neurons to rate models: A cascade model as an approximation to spiking neuron models with refractoriness. Physical Review E, 73(5):51908, 2006. [ bib ] |
[90] | R. Azouz and C. Gray. Dynamic spike threshold reveals a mechanism for coincidence detection in cortical neurons in vivo. Proc. National Academy of Sciences USA, 97:8110-8115, 2000. [ bib ] |
[91] | R. Azouz and C. M. Gray. Adaptive coincidence detection and dynamic gain control in visual cortical neurons in vivo. Neuron, 37:513-523, 2003. [ bib ] |
[92] | R. Azouz and C. M. Gray. Adaptive coincidence detection and dynamic gain control in visual cortical neurons in vivo. Neuron, 37(3):513-523, 2003. [ bib ] |
[93] | M. Bach, C. Schmitt, T. Quenzer, T. Meigen, and M. Fahle. Summation of texture segregation across orientation and spatial frequency: electrophysiological and psychophysical findings. Vision Research, 40(26):3559-3566, 2000. [ bib ] |
[94] | R. Baddeley, L. F. Abbott, M. Booth, F. Sengpiel, and T. Freeman. Responses of neurons in primary and inferior temporal visual cortices to natural scenes. In Proceedings of the Royal Society London, number 264 in B, pages 1775-1783, 1998. [ bib ] |
[95] | L. Badel, W. Gerstner, and M. Richardson. Dependence of the spike-triggered average voltage on membrane response properties. Neurocomputing, 69:1062-1065, 2007. [ bib ] |
[96] | L. Badel, S. Lefort, R. Brette, C. Petersen, W. Gerstner, and M. Richardson. Dynamic i-v curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J Neurophysiol, 2007. [ bib | DOI ] |
[97] | L. Badel and A. Tonnelier. Pulse propagation in discrete excitatory networks of integrate-and-fire neurons. Physical Review E, 70:11906, 2004. [ bib ] |
[98] | M. Badoual, Q. Zou, A. P. Davison, M. Rudolph, T. Bal, Y. Frégnac, and A. Destexhe. Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity. Int J Neural Syst, 16(2):79-97, Apr 2006. [ bib ] |
[99] | W. Bair and C. Koch. Temporal precision of spike trains in extrastriate cortex of the behaving macaque monekey. Neural Computation, 8:1185-1202, 1996. [ bib ] |
[100] | W. Bair, C. Koch, W. Newsome, and K. Britten. Power spectrum analysis of MT neurons in the behaving monkey. J. Neurosci., 14:2870-2892, 1994. [ bib ] |
[101] | W. Bair, E. Zohary, and W. Newsome. Correlated firing in macaque visual area mt: Time scales and relationship to behavior. J. Neuroscience, 21:1676-1697, 2001. [ bib ] |
[102] | B. Baird. Nonlinear dynamics of pattern formation and pattern recognition in the rabbit olfactory bulb. Physica D, 22:150-175, 1986. [ bib ] |
[103] | J. C. Baird, J. S. Taube, and D. V. Peterson. Statistical and information properties of head direction cells. Perception & psychophysics, 63(6):1026-1037, Aug. 2001. [ bib ] |
[104] | P. Bak and C. Tang. xxx. J. Geophys. Res., 94:15635, 1976. [ bib ] |
[105] | F. Baldissera and B. Gustafsson. Firing behavior of a neuron model based on the afterhyperpolarisation conductance time course and algebraic summation. adaptation and steady state firing. Acta Physiol. Scand., 92:27-47, 1974. [ bib ] |
[106] | K. Ball and R. Sekuler. Direction-specific improvement in motion discrimination. Vision Research, 27:953-965, 1987. [ bib ] |
[107] | K. Ball and R. Sekuler. A specific and enduring improvement in visual motion discrimination. Science, 218:697-698, 1982. [ bib ] |
[108] | D. Baras and R. Meir. Reinforcement learning, spike-time-dependent plasticity, and the bcm rule. Neural Computation, 19(8):2245-2279, 2007. [ bib | DOI ] |
[109] | D. Barber and F. Agakov. Spiking sequence learning using maximum likelihood: Hopfield networks. Neural Computation, to appear:xx, 2002. [ bib ] |
[110] | H. Barlow. The absolute efficiency of perceptual decisions. Phil. Trans. R. Soc. Lond. Ser. B, 290:71-82, 1980. [ bib ] |
[111] | H. Barlow. Retinal noise and absolute threshold. J. Opt. Soc. Am., 46:634-639, 1956. [ bib ] |
[112] | H. Barlow and P. Földiák. Adaptation and decorrelation in the cortex, pages 54-72. Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA, 1989. [ bib ] |
[113] | H. Barlow and W. Levick. 3 factors limiting reliable detection of light by retinal ganglion cells of cat. J. Physiology (London), 200:1-24, 1969. [ bib ] |
[114] | H. B. Barlow. Possible principles underlying the transformations of sensory messages. In W. A. Rosenblith, editor, Sensory Communication, pages 183-192. MIT Press, Cambridge edition, 1961. [ bib ] |
[115] | H. B. Barlow. Possible principles underlying the transformation of sensory messages. In W. A. Rosenbluth, editor, Sensory Communication, pages 217-234. MIT Press, 1961. [ bib ] |
[116] | H. B. Barlow. Unsupervised learning. Neural. Comp., 1:295-311, 1989. [ bib ] |
[117] | H. B. Barlow. The information capacity of nervous transmission. Kybernetik, 2:1, 1963. [ bib ] |
[118] | H. B. Barlow, W. R. Levick, and M. Yoon. Responses to single quanta of light in retinal ganglion cells of the cat. Vision Res. Suppl., 3:87-101, 1971. [ bib ] |
[119] | J. Barlow. Inertial navigation as a basis for animal navigation. Journal of Theoretical Biology, 6:76-117, 1964. [ bib ] |
[120] | O. Barndorff-Nielsen and D. Cox. Asymptotic techniques for use in statistics. Chapman and Hall New York, 1989. [ bib ] |
[121] | A. Barto, R. Sutton, and C. Anderson. Neuronlike adaptive elements that can solve difficult learning and control problems. IEEE transactions on systems, man, and cybernetics, 13:835-846, 1983. [ bib ] |
[122] | A. Bartsch and J. van Hemmen. Combined Hebbian development of geniculocortical and lateral connectivity in a model of primary visual cortex. Biological Cybernetics, 84(1):41-55, 2001. [ bib ] |
[123] | J. Bastian. Pyramidal cell plasticity in weakly electric fish: a mechansim for attenuating responses to reafferent electrosensory inputs. J. Comp. Physiol. A, 176:63-73, 1992. [ bib ] |
[124] | H. U. Bauer and K. Pawelzik. Alternating oscillatory and stochastic dynamics in a model for a neuronal assembly. Physica D, 69:380-393, 1993. [ bib ] |
[125] | H. U. Bauer, K. Pawelzik, and T. Geisel. Emergence of transient oscillations in an ensemble of neurons. In S. Gielen and B. Kappen, editors, Proceedings of the ICANN'93, pages 136-141, London, 1993. Springer. [ bib ] |
[126] | J. Baxter and P. Bartlett. Infinite-horizon policy-gradient estimation. Journal of Artificial Intelligence Research, 15(4):319-350, 2001. [ bib ] |
[127] | J. Baxter, P. Bartlett, and L. Weaver. Experiments with infinite-horizon, policy- gradient estimation. Journal of Artificial Intelligence Research, 15:351-381, 2001. [ bib ] |
[128] | R. Beale and T. Jackson. Neural Computation: An Introduction. IOP publishing, Bristol, 1990. [ bib ] |
[129] | M. Bear. Bidirectional synaptic plasticity: from theory to reality. Philosophical Transactions R. Soc. Lond B: Biological Sciences, 358:649 - 655, 2003. [ bib ] |
[130] | M. Bear and R. C. Malenka. Synaptic plasticity: Ltp and ltd. Curr. Opin. Neurobil., 4:389-399, 1994. [ bib ] |
[131] | H. Beck and Y. Yaari. Plasticity of intrinsic neuronal properties in cns disorders. Nat Rev Neurosci, 9(5):357-369, May 2008. [ bib | DOI | http ] |
[132] | S. Becker. A computational principle for hippocampal learning and neurogenesis. Hippocampus, 15:722-738, 2005. [ bib ] |
[133] | S. Becker. Mutual information maximization: Models of cortical self-organization. Network: Computation in Neural Systems, pages 7-31, 1996. [ bib ] |
[134] | S. Becker and G. E. Hinton. A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355(6356):161-163, 1992. [ bib ] |
[135] | J. Bednar and R. Miikkulainen. Tilt aftereffects in a self-organizing model of the primary visual cortex. Neural Computation, 12(7):1721-1740, 2000. [ bib ] |
[136] | J. Beggs. A statistical theory of long-term potentiation and depression. Neural Computation, 13:87-111, 2000. [ bib ] |
[137] | A. Bell and T. Sejnowski. An information maximization approach to blind separation and blind deconvolution. Neural Computation, 7:1129-1159, 1995. [ bib ] |
[138] | A. J. Bell and T. J. Sejnowski. The 'independent components' of natural scenes are edge filters. Vision Research, 37:3327-3338, 1997. [ bib ] |
[139] | A. J. Bell and T. J. Sejnowski. An information maximisation approach to blind separation and blind deconvolution. Neural Computation, 7(6):1129-1159, 1995. [ bib ] |
[140] | C. Bell, D. Bodznick, J. Montgomery, and J. Bastian. The generation and subtraction of sensory expectations within cerebellar-like structures. Brain. Beh. Evol., 50:17-31, 1997. suppl. I. [ bib ] |
[141] | C. Bell, V. Han, Y. Sugawara, and K. Grant. Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature, 387:278-281, 1997. [ bib ] |
[142] | R. Bellman. A markov decision process. J. Mathematical Mechanics, 6:679-684, 1957. [ bib ] |
[143] | R. E. Bellman. Dynamic Programming. Princeton University Press, Princeton, 1957. [ bib ] |
[144] | A. J. Bell and L. C. Parra. Maximising sensitivity in a spiking network. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 121-128. MIT Press, Cambridge, MA, 2005. [ bib ] |
[145] | A. Belouchrani, K. Abed-Meraim, J.-F. Cardoso, and E. Moulines. A blind source separation technique using second order statistics. IEEE Transactions on Signal Processing, 45:434-444, 1997. [ bib ] |
[146] | I. Belykh, V. Belykh, and M. Hasler. Hierarchy and stability of partially synchronous oscillations of diffusively coupled dynamical systems. Physical Revue E, 62:6332-6345, 2000. [ bib ] |
[147] | I. Belykh, E. D. Lange, and M. Hasler. Synchronization of bursting neurons: What matters in the network topology. Phys. Rev. Lett., 94(18):8101, 2005. [ bib ] |
[148] | V. Belykh, I. Belykh, and M. Hasler. Connection graph stability method for synchronized coupled chaotic systems. Physica D, 195:159-187, 2004. [ bib ] |
[149] | V. Belykh, I. Belykh, and M. Hasler. Blinking model and synchronization in small-world networks with a time-varying coupling. Physica D, 195:188-206, 2004. [ bib ] |
[150] | V. Belykh, I. Belykh, M. Hasler, and K. Nevidin. Cluster synchronization in three-dimensional lattices of diffusively coupled oscillators. Int. J. Bifurcation and Chaos, 13:755-779, 2003. [ bib ] |
[151] | V. Belykh, I. Belykh, E. Mosekilde, and M. Colding-Jorgensen. Homoclinic bifurcation of cell model with bursting oscillations. European Physical Journal E, 3:205-219, 2000. [ bib ] |
[152] | V. Belykh, I. Belykh, K. Nevidin, and M. Hasler. Persistent clusters in lattices of coupled nonidentical chaotic systems. Int. J. Bifurcation and Chaos, 13:165-178, 2003. [ bib ] |
[153] | G. Ben Arous and A. Guionnet. Large deviations for langevin spin glass dynamics. Probability Theory and Related Fields, 102:455-509, 1995. [ bib ] |
[154] | R. Ben-Yishai, R. Bar-Or, and H. Sompolinsky. Theory of orientation tuning in visual cortex. Proc. Natl. Acad. Sci. USA, 92:3844-3848, 1995. [ bib ] |
[155] | J. Benda and A. V. M. Herz. A universal model for spike-frequency adaptation. Neural Computation, 15(11):2523-2564, 2003. [ bib ] |
[156] | S. Benhamou. An analysis of movements of the wood mouse apodemus sylvaticus in its home range. Behavioral Processes, 22:235-250, 1990. [ bib ] |
[157] | P. Berkes. Handwritten digit recognition with nonlinear fisher discriminant analysis. Proceedings of ICANN 2005, 2(LNCS 3696):285-287, 2005. [ bib ] |
[158] | P. Berkes. Pattern recognition with slow feature analysis. Cognitive Sciences EPrint Archive (CogPrints), 4104, 2005. [ bib ] |
[159] | P. Berkes and L. Wiskott. Analysis of inhomogeneous quadratic forms for physiological and theoretical studies. In Proc. Computational and Systems Neuroscience, COSYNE'05, Salk Lake City, Mar. 2005. (abstract). [ bib ] |
[160] | P. Berkes and L. Wiskott. Slow feature analysis yields a rich repertoire of complex cells. Journal of Vision, 5(6):579-602, 2005. [ bib ] |
[161] | P. Berkes and T. Zito. Modular modular toolkit for data processing (version 2.1). http://mdp-toolkit.sourceforge.net, 2007. [ bib | http ] |
[162] | Ö. Bernander, R. J. Douglas, K. A. C. Martin, and C. Koch. Synaptic background activity influences spatiotemporal integration in single pyramidal cells. Proc. Natl. Acad. Sci. USA, 88:11569-11573, 1991. [ bib ] |
[163] | G. Berns and T. Sejnowski. A computational model of how the basal ganglia produce sequences. J. Cog. Neuroscience, 10:108-121, 1998. [ bib ] |
[164] | K. Berridge. The debate over dopamine’s role in reward: the case for incentive salience. Psychopharmacology, 191(3):391-431, Apr. 2007. [ bib | http ] |
[165] | M. Berry and M. Meister. Refractoriness and neural precision. J. of Neuroscience, 18:2200-2211, 1998. [ bib ] |
[166] | M. J. Berry, D. K. Warland, and M. Meister. The structure and precision of retinal spike trains. Proc. Nat. Ac. Sciences (USA), 94:5411-5416, 1997. [ bib ] |
[167] | N. Bertschinger and T. Natschläger. Real-time computation at the edge of chaos in recurrent neural networks. Neural Computation, 16:1413-1436, 2004. [ bib ] |
[168] | D. Bertsekas. Dynamic Programming: Deterministic and Stochastic Models. Prentice-Hall, Englewood cliffs, NJ, 1987. [ bib ] |
[169] | D. Bertsekas and J. Tsitsiklis. Neuro-Dynamic Programming. Athena Scientific, 1996. [ bib ] |
[170] | M. Bethge, K. Pawelzik, R. Rothenstein, and M. Tsodyks. Noise as a signal for neuronal populations. Preprint - appeared in different form as Silberberg et al. 2004, xx:xx, 2001. [ bib ] |
[171] | M. Bethge, D. Rotermund, and K. Pawelzik. Optimal neural rate coding leads to bimodal firing rate distributions. NETWORK-COMPUTATION IN NEURAL SYSTEMS, 14:303-319, 2003. [ bib ] |
[172] | M. Bethge, D. Rotermund, and K. Pawelzik. Optimal short-term population coding: when Fisher information fails. Neural Computation, 14:2317-2351, 2002. [ bib ] |
[173] | G.-Q. Bi. Spatiotemporal specificity of synaptic plasticity: cellular rules and mechanisms. Biological Cybernetics, 319-332, 2002. [ bib ] |
[174] | G. Bi and M. Poo. Distributed synaptic modification in neural networks induced by patterned stimulation. Nature, 401:792-796, 1999. [ bib ] |
[175] | G. Bi and M. Poo. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci., 18:10464-10472, 1998. [ bib ] |
[176] | G. Bi and M. Poo. Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience, 18(24):10464-10472, 1998. [ bib ] |
[177] | G. Bi and M. Poo. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type. Journal of Neuroscience, 18(24):10464, 1998. [ bib ] |
[178] | G. Bi and M. Poo. Synaptic modification of correlated activity: Hebb's postulate revisited. Ann. Rev. Neurosci., 24:139-166, 2001. [ bib ] |
[179] | G. Bi and H. Wang. Temporal asymmetry in spike timing-dependent synaptic plasticity. Physiology and Behavior, 77:551-555, 2002. [ bib ] |
[180] | W. Bialek, I. Nemenman, and N. Tishby. Predictability, complexity and learning. Neural Computation, 13(11):2409-2463, 2001. [ bib ] |
[181] | W. Bialek and F. Rieke. Reliability and information transmission in spiking neurons. Trends in Neurosciences, 15(11):428-433, 1992. [ bib ] |
[182] | W. Bialek, F. Rieke, R. R. de Ruyter van Stevenick, and D. Warland. Reading a neural code. Science, 252:1854-1857, 1991. [ bib ] |
[183] | E. Bienenstock, L. Cooper, and P. Munroe. Theory of the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. Journal of Neuroscience, 2:32-48, 1982. reprinted in Anderson and Rosenfeld, 1990. [ bib ] |
[184] | E. Bienenstock and R. Doursat. The hebbian development of synfire chains. in preparation, 1995. [ bib ] |
[185] | E. L. Bienenstock, L. N. Cooper, and P. W. Munro. Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. Journal of Neuroscience, 2(1):32-48, 1982. [ bib ] |
[186] | G. Billings and M. van Rossum. Memory retention and spike timing dependent plasticity. Preprint, xx:xx, 2008. [ bib ] |
[187] | V. Billock. Very short term visual memory via reverberation: A role for the cortico-thalamic excitatory circuit in temporal filling-in during blinks and saccades? Vision Research, 37:949-953, 1997. [ bib ] |
[188] | L. Bindman, G. Christofi, K. Murphy, and A. Nowicky. Long-term potentiation (LTP) and depression (LTD) in the neocortex and hippocampus: an overview. In T. Stone, editor, Aspects of synaptic transmission., volume 1, pages 3-25, London, 1991. Taylor Francis. [ bib ] |
[189] | C. M. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995. [ bib ] |
[190] | H. Blair and P. Sharp. Anticipatory head direction signals in anterior thalamus: Evidence for a thalamocortical circuit that integrates angular head motion to compute head direction. Journal of Neuroscience, 15(9):6260-6270, 1995. [ bib ] |
[191] | C. Blakemore. Vision: coding and efficiency. Cambridge University Press, Cambridge, 1990. [ bib ] |
[192] | C. Blakemore and D. Price. The organization and post-natal development of area 18 of the cat's visual cortex, 1987. [ bib ] |
[193] | T. Blaschke. Independent Component Analysis and Slow Feature Analysis: Relations and Combination. PhD thesis, Humboldt-Universität zu Berlin, 2005. [ bib ] |
[194] | T. Blaschke, P. Berkes, and L. Wiskott. What is the relation between slow feature analysis and independent component analysis? Neural Computation, 18(10):2495-2508, 2006. [ bib ] |
[195] | T. Blaschke and L. Wiskott. CuBICA: Independent component analysis by simultaneous third- and fourth-order cumulant diagonalization. IEEE Transactions on Signal Processing, 52(5):1250-1256, May 2004. [ bib ] |
[196] | T. Blaschke, T. Zito, and L. Wiskott. Independent slow feature analysis and nonlinear blind source separation. Neural Computation, 19(4):994-1021, 2007. [ bib ] |
[197] | G. G. Blasdel. Orientations selectivity, preference, and continuity in monkey striate cortex. The Journal of Neuroscience, 12:3139-3161, 1992. [ bib ] |
[198] | G. G. Blasdel and G. Salama. Voltage sensitive dyes reveal a modular organization of the cortex. Nature, 321:579-585, 1986. [ bib ] |
[199] | F. Blayo and M. Verleysen. Les réseaux de neurones artificiels. Que sais-je? Press Universitaires de France, 1996. [ bib ] |
[200] | T. Bliss, G. Collingridge, and R. Morris. Long-term potentiation: enhancing neuroscience for 30 years - introduction. Phil. Trans. R. Soc. Lond B: Biological Sciences, 358:607-611, 2003. [ bib ] |
[201] | T. Bliss and A. Gardner-Medwin. Long-lasting potentation of synaptic transmission in the dendate area of unanaesthetized rabbit following stimulation of the perforant path. J. Physiol., 232:357-374, 1973. [ bib ] |
[202] | T. Bliss and T. Lomo. Long-lasting potentation of synaptic transmission in the dendate area of anaesthetized rabbit following stimulation of the perforant path. J. Physiol., 232:351-356, 1973. [ bib ] |
[203] | T. V. P. Bliss and G. L. Collingridge. A synaptic model of memory: long-term potentiation in the hippocampus. Nature, 361:31-39, 1993. [ bib ] |
[204] | K. Blum and L. Abbott. A model of spatial map formation in the hippocampus of the rat. Neural Comput., 8:85-93, 1996. [ bib ] |
[205] | B. Blumenfeld, S. Preminger, D. Sagi, and M. Tsodyks. Dynamics of Memory Representations in Networks with Novelty-Facilitated Synaptic Plasticity. Neuron, 52(2):383-394, 2006. [ bib ] |
[206] | E. de Boer and P. Kuyper. Triggered correlation. IEEE Trans. Biomedical Engineering, 15:169-179, 1968. [ bib ] |
[207] | R. Bogacz, E. Brown, J. Moehlis, P. Holmes, and J. Cohen. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Phsychological Review, 113:700-765, 2006. [ bib ] |
[208] | S. Bohte, J. N. K. H., and L. Poutré. Spike-prop: error-backprogation in multi-layer networks of spiking neurons. Neurocomputing, XX-XX:XX-XX, 2002. [ bib ] |
[209] | S. M. Bohte and M. C. Mozer. Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity. Neural Comp., 19(2):371-403, 2007. [ bib ] |
[210] | S. M. Bohte and M. C. Mozer. Reducing spike train variability: A computational theory of spike-timing dependent plasticity. In Neural Information Processing Systems, volume 2005, 2005. [ bib ] |
[211] | S. M. Bohte and M. C. Mozer. Reducing spike train variability: A computational theory of spike-timing dependent plasticity. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 201-208. MIT Press, Cambridge, MA, 2005. [ bib ] |
[212] | T. Bonhoeffer, V. Staiger, and A. Aertsen. Synaptic plasticity in rat hippocampal slice cultures: Local `Hebbian' conjunction of pre- and postsynaptic stimulation leads to distributed synaptic enhancement. Proc. Natl. Acad. Sci. USA, 86:8113-8117, 1989. [ bib ] |
[213] | L. Borg-Graham, C. Monier, and Y. Fregnac. Visual input evokes transient and strong shunting inhibition in visual cortical neurons. Nature, 393:369-373, 1998. [ bib ] |
[214] | J. G. G. Borst, F. Helmchen, and B. Sakmann. Pre- and postsynaptic whole-cell recordings in the medial nucleus of the trapezoid body of the cat. J. Physiol., 1995. [ bib ] |
[215] | Z. A. Bortolotto, S. Lauri, J. T. R. Isaac, and G. L. Collingridge. Kainate receptors and the induction of mossy fibre long-term potentiation. Phil. Trans. R. Soc. Lond B: Biological Sciences, 358(657 - 666), 2003. [ bib ] |
[216] | A. Bose, N. Kopell, and D. Terman. Almost-synchronous solutions for mutually coupled excitatory neurons. Physica D, 140:69-94, 2000. [ bib ] |
[217] | S. Bothe and M. Mozer. Reducing spike train variability: a computational theory of spike-timing dependent plasticity. NIPS conference 2004, to appear, xx:xx, 2004. [ bib ] |
[218] | S. Bottani. Pulse-coupled relaxation oscillators. Phys. Rev. Lett., 74:4189-4192, 1995. [ bib ] |
[219] | D. Boussaoud, R. Desimone, and L. G. Ungerleider. Visual topography of area teo in the macaque. The Journal of Comparative Neurology, 306:554-575, 1991. [ bib ] |
[220] | P. Bovet and S. Benhamou. Spatial analysis of animal's movement using a correlated random walk model. Journal of Theoretical Biology, 131:419-433, 1988. [ bib ] |
[221] | J. M. Bower and D. Beeman. The book of Genesis. Springer, New York, 1995. [ bib ] |
[222] | M. R. Bower, D. R. Euston, and B. L. McNaughton. Sequential-context-dependent hippocampal activity is not necessary to learn sequences with repeated elements. Journal of Neuroscience, 25(6):1313-1323, Feb. 2005. [ bib ] |
[223] | J. Brader, W. Senn, and S. Fusi. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Computation, 19:2881-2912, 2007. [ bib ] |
[224] | V. Braitenberg. Two view of the cerebral cortex. In G. Palm and A. Aertsen, editors, Brain theory, pages 81-96, Berlin Heidelberg New York, 1986. Springer-Verlag. [ bib ] |
[225] | V. Braitenberg. The cerebellar network: attempt at a formalization of its structure. Network, 4:11-17, 1993. [ bib ] |
[226] | V. Braitenberg. Vehicles. Experiments in Synthetic Psychology. MIT Press, Cambridge, 1984. [ bib ] |
[227] | V. Braitenberg and A. Schütz. Anatomy of the cortex. Springer-Verlag, Berlin Heidelberg New York, 1991. [ bib ] |
[228] | A. Brand, O. Behrend, T. Marquardt, D. McAlpine, and B. Grothe. Precise inhibition is essential for microsecond interaural time difference coding. Nature, 417:543-547, 2002. [ bib ] |
[229] | R. Brause. Neuronale Netze. Teubner, Stuttgart, 1991. [ bib ] |
[230] | A. Bray and D. Martinez. Kernel-based extraction of slow features: Complex cells learn disparity and translation invariance from natural images. In Advances in Neural Information Processing Systems 15, pages 253-260. MIT Press, 2002. [ bib ] |
[231] | C. R. Breese, R. E. Hampson, and S. A. Deadwyler. Hippocampal place cells: stereotypy and plasticity. Journal of Neuroscience, 9(4):1097-1111, Apr. 1989. [ bib ] |
[232] | N. Brenner, O. Agam, W. Bialek, and R. de Ruyter van Steveninck. Universal statistical behavior of neuronal spike trains. Physical Review Letters, 81(18):4000-4003, 1998. [ bib ] |
[233] | N. Brenner, W. Bialek, and R. de Ruyter van Steveninck. Adaptive rescaling maximizes information transmission. Neuron, 26:295-701, 2000. [ bib ] |
[234] | P. C. Bressloff and J. G. Taylor. Dynamics of compartmental model neurons. Neural Networks, 7:1153-1165, 1994. [ bib ] |
[235] | P. C. Bressloff and J. G. Taylor. Compartmental-model response function for dendritic trees. Biol. Cybern., 70:199-207, 1993. [ bib ] |
[236] | R. Brette and W. Gerstner. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol., 94:3637 - 3642, 2005. [ bib ] |
[237] | R. Brette and W. Gerstner. Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity. Journal of Neurophysiology, 94(5):3637-3642, 2005. [ bib ] |
[238] | R. Brette and E. Guignon. Reliability of spike timing is a general property of spiking neurons. Neural Computation, 12:279-308, 2003. [ bib ] |
[239] | R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J. M. Bower, M. Diesmann, A. Morrison, P. H. Goodman, F. C. J. Harris, M. Zirpe, T. Natschlager, D. Pecevski, B. Ermentrout, M. Djurfeldt, A. Lansner, O. Rochel, T. Vieville, E. Muller, A. P. Davison, S. El Boustani, and A. Destexhe. Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci, 23(3):349-398, 2007. [ bib | DOI ] |
[240] | D. Brillinger. The maximum likelihood approach to the identification of neuronal firing systems. Annals of Biomedical Engineering, 16:3-16, 1988. [ bib ] |
[241] | D. Brillinger and J. Segundo. Empirical examination of the threshold model of neuronal firing. Biological Cybernetics, 35:213-220, 1979. [ bib ] |
[242] | D. R. Brillinger. Nerve cell spike train data analysis: a progressiion of thechniques. J. American Stastistical Association, 87:260-271, 1992. [ bib ] |
[243] | D. R. Brillinger. Maximum likelihood analysis of spike trains of interacting nerve cells. Biol. Cybern., 59:189-200, 1988. [ bib ] |
[244] | K. Britten, M. Shadlen, W. Newsome, and J. Movshon. The analysis of visual motion: A comparison of neuronal and psychophysical performance. J. Neuroscience, 12:4745-4765, 1992. [ bib ] |
[245] | C. Brody and J. Hopfield. Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron, 37:843-852, 2003. [ bib ] |
[246] | R. A. Brooks. Intelligence without representation. Artificial Intelligence, 47:139-159, 1991. [ bib ] |
[247] | J. Brown, D. Bullock, and S. Grossberg. How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected reward cues. J. Neuroscience, to appear:xx, 1999. [ bib ] |
[248] | J. E. Brown, B. J. Yates, and J. S. Taube. Does the vestibular system contribute to head direction cell activity in the rat? Physiology & behavior, 77(4-5):743-748, Dec. 2002. [ bib ] |
[249] | M. Brown and P. Sharp. Simulation of spatial-learning in the Morris water maze by a neural network model of the hippocampal-formation and nucleus accumbens. Hippocampus, 5:171-188, 1995. [ bib ] |
[250] | T. H. Brown, V. C. Chang, A. H. Ganong, C. L. Keenan, and S. R. Kelso. Biophysical properties of dendrites and spines that may contral the induction and expression of long-term synaptic potentiation, pages 201-264. Alan R. Liss, xxx, 1988. [ bib ] |
[251] | T. H. Brown and S. Chatarji. Hebbian Synaptic Plasticity: Evolution of the Contemporary Concept, pages 287-314. Springer-Verlag, New York, 1994. chap. 8. [ bib ] |
[252] | T. H. Brown, A. H. Ganong, E. W. Kairiss, C. L. Keenan, and S. R. Kelso. Long-term potentation in two synaptic systems of the hippocampal brain slice. In J. Byrne and W. Berry, editors, Neural models of plasticity., pages 266-306. Academic Press, San Diego, 1989. [ bib ] |
[253] | T. H. Brown and D. Johnston. Voltage-clamp analysis of mossy fiber synaptic input to hippocampal neurons. J. Neurophysiology, 50:487-507, 1983. [ bib ] |
[254] | T. H. Brown, A. M. Zador, Z. F. Mainen, and B. J. Claiborne. Hebbian modifications in hippocampal neurons. In M. Baudry and J. Davis, editors, Long-term potentiation., pages 357-389. MIT Press, Cambridge, London, 1991. [ bib ] |
[255] | V. H. Brun, M. K. Oetnaess, S. Molden, H.-A. Steffenach, M. P. Witter, M.-B. Moser, and E. I. Moser. Place cells and place cell recognition maintained by direct entorhinal-hippocampal circuitry. Science, 296:2243-2246, 2002. [ bib ] |
[256] | N. Brunel. Persistent activity and the single cell f-i curve in a cortical network model. Network - computation in neural systems, 12:xx, 2001. to appear. [ bib ] |
[257] | N. Brunel. Dynamics of sparsely connected networls of excitatory and inhibitory neurons. Computational Neuroscience, 8:183-208, 2000. [ bib ] |
[258] | N. Brunel, F. Chance, N. Fourcaud, and L. Abbott. Effects of synaptic noise and filtering on the frequency response of spiking neurons. Physical Review Letters, 86:2186-2189, 2001. [ bib ] |
[259] | N. Brunel and V. Hakim. Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Computation, 11:1621-1671, 1999. [ bib ] |
[260] | N. Brunel and V. Hakim. Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Computation, 11:1621-1671, 1999. [ bib ] |
[261] | N. Brunel, V. Hakim, P. Isope, J.-P. Nadal, and B. Barbour. Optimal information storage and the distribution of synaptic weights: Perceptron versus purkinje cell. Neuron, 43:745-757, 2004. [ bib ] |
[262] | N. Brunel and P. Lathan. Firing rate of the noisy quadratic integrate-and-fire neuron. Neural Computation, 15:2281-2306, 2003. [ bib ] |
[263] | N. Brunel and O. Trullier. Plasticity of spatially selective neuronal activity in a model of the rat hippocampus. Hippocampus, 8:651-665, 1998. [ bib ] |
[264] | H. L. Bryant and J. P. Segundo. Spike inititation by transmembrane current: a white noise analysis. Journal of Physiology, 260:279-314, 1976. [ bib ] |
[265] | H. L. Bryant and J. P. Segundo. Spike initiation by transmembrane current: a white-noise analysis. J Physiol, 260(2):279-314, 1976. [ bib ] |
[266] | N. Buchs and W. Senn. Spike-based synaptic plasticity and the emergence of diretion selective simple cells: simulation results. J. Computational Neuroscience, xx:xx, 2002. [ bib ] |
[267] | J. Buck and E. Buck. Synchronous fireflies. Scientific American, 234:74-85, 1976. [ bib ] |
[268] | G. Bugmann, C. Christodoulou, and J. G. Taylor. Role of temporal integration and fluctuation detection in the highly irregular firing of leaky integrator neuron model with partial reset. Neural Computation, 9:985-1000, 1997. [ bib ] |
[269] | J. Buhmann. Oscillations and low firing rates in associative memory neural networks. Phys. Rev. A, 40:4145-4148, 1989. [ bib ] |
[270] | J. Buhmann. Associative memory with high information content. Phys. Rev. A, 39:2689-2692, 1989. [ bib ] |
[271] | J. Buhmann and K. Schulten. Noise-driven temporal association in neural networks. Europhys. Lett., 4:1205-1209, 1987. [ bib ] |
[272] | J. Buhmann and K. Schulten. Associative recognition and storage in a model network with physiological neurons. Biol. Cybern., 54:319-335, 1986. [ bib ] |
[273] | J. Bullier. Integrated model of visual processing. Brain Research Reviews, 36(2-3):96-107, 2001. [ bib ] |
[274] | A. Bulsara, E. W. Jacobs, T. Zhou, F. Moss, and L. Kiss. Stochastic resonance in a single neuron model: Theory and analog simulation. Journal of Theoretical Biology, 152:531-555, 1991. [ bib ] |
[275] | A. R. Bulsara, T. C. Elston, C. R. Doering, S. B. Lowen, and K. Lindenberg. Cooperative behavior in periodically driven noisy integrate- fire models of neuronal dynamics. Physical Review E, 53:3958-3969, 1996. [ bib ] |
[276] | D. Buonomano and M. Merzenich. Cortical plasticity: From synapses to maps. Annual Review of Neuroscience, 21:149-186, 1998. [ bib ] |
[277] | D. V. Buonomano and M. M. Merzenich. Cortical plasticity: From synapses to maps. Annual Reviews of Neuroscience, 21:149-186, 1998. [ bib | DOI | http ] |
[278] | G. T. Buracas, A. M. Zador, M. R. DeWeese, and T. D. Albright. Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron, 20(5):959-969, May 1998. [ bib ] |
[279] | N. Burgess. Computational models of the spatial and mnemonic functions of the hippocampus. In The hippocampus book, pages 715-749. Oxford university press, 2007. [ bib ] |
[280] | N. Burgess, F. Cacucci, C. Lever, and J. O'keefe. Characterizing multiple independent behavioral correlates of cell firing in freely moving animals. Hippocampus, 15(2):149-153, 2005. [ bib ] |
[281] | N. Burgess and J. O'Keefe. Neuronal computations underlying the firing of place cells and their role in navigation. Hippocampus, 6(6):749-762, 1996. [ bib ] |
[282] | N. Burgess, M. Recce, and J. O'Keefe. A model of hippocampal function. Neural Networks, 6/7(7):1065-1081, 1994. [ bib ] |
[283] | N. Burgess, M. Recce, and J. O'Keefe. A model of hippocampal function. Neural Networks, 7:1065-1081, 1994. [ bib ] |
[284] | A. N. Burkitt. A review of the integrate-and-fire neuron model: I. homogeneous synaptic input. Biol. Cybernetics, 95:1-19, 2006. [ bib ] |
[285] | A. N. Burkitt. A review of the integrate-and-fire neuron model: Ii. inhomogeneous synaptic input and network properties. Biol. Cybernetics, 95:97-112, 2006. [ bib ] |
[286] | A. N. Burkitt. Balanced neurons: Analysis of leaky integrate-and-fire neurons with reversal potential. Biological Cybernetics, 85:247-255, 2001. [ bib ] |
[287] | A. N. Burkitt and G. M. Clark. Analysis of integrate-and-fire neurons: synchronization of synaptic input and spike output. Neural Computation, 11:871-901, 1999. [ bib ] |
[288] | A. N. Burkitt, M. H. Meffin, and D. Grayden. Spike-timing-dependent plasticity: The relationship to rate-based learning for models with weight dynamics determined by a stable fixed point. Neural Computation, 16:885-940, 2004. [ bib ] |
[289] | R. Burridge and L. Knopoff. xx. Bull. Seism. Soc. Am., 57:341, 1967. [ bib ] |
[290] | J. Burrone and V. Murthy. Synaptic gain control and homeostasis. Curr Opin Neurobiol, 13(5):560-7, 2003. [ bib ] |
[291] | J. Burrone, M. O'Byrne, and V. Murthy. Multiple forms of synaptic plasticity triggered by selective suppression of activity in individual neurons. Nature, 420(6914):414-8, 2002. [ bib ] |
[292] | R. Burwell and D. Hafeman. Positional firing properties of postrhinal cortex neurons. Neuroscience, 119:577-588, 2003. [ bib ] |
[293] | P. Buser and M. Imbert. Vision. MIT Press, Cambridge, 1992. [ bib ] |
[294] | P. C. Bush and R. J. Douglas. Synchronization of bursting action potential discharge in a model network of neocortical neurons. Neural Computation, 3:19-30, 1991. [ bib ] |
[295] | P. C. Bush and T. J. Sejnowski. Reduced compartmental models of neocortical pyramidal cells. J Neurosci Methods, 46(2):159-166, 1993. [ bib ] |
[296] | D. A. Butts, P. O. Kanold, and C. J. Shatz. A burst-based Hebbian learning rule at retinogeniculate synapses links retinal waves to activity-dependent refinement. PLoS Biology, 5(3):e61 EP -, Mar. 2007. [ bib | http ] |
[297] | G. Buzsáki. Large-scale recording of neuronal ensembles. Nature Neuroscience, 7(5):446-451, 2004. [ bib ] |
[298] | J. H. Byrne and W. O. Berry. Neural Models of Plasticity. Academic Press, San Diego, 1989. [ bib ] |
[299] | F. Cacucci, C. Lever, T. Wills, N. Burgess, and J. O'Keefe. Theta-modulated place-by-direction cells in the hippocampal formation in the rat. Journal of Neuroscience, 24(38):8265-8277, 2004. [ bib ] |
[300] | D. Cai, L. Tao, and D. McLaughlin. An embedded network approach for scale-up of fluctuation-driven systems with preservation of spike information. Proc. National Academy Sciences (USA), 101:14288-14293, 2004. [ bib ] |
[301] | D. Cai, L. Tao, M. Shelley, and D. McLaughlin. An effective kinetic representation of fluctuation-driven neuronal networks with application to simple and complex cells in visual cortex. Proc. National Academy Sciences (USA), 101:7757-7762, 2004. [ bib ] |
[302] | J. L. Calton and J. S. Taube. Degradation of head direction cell activity during inverted locomotion. Journal of Neuroscience, 25(9):2420-2428, Mar. 2005. [ bib ] |
[303] | W. Calvin and C. Stevens. Synaptic noise and other sources of randomness in motoneuron interspike intervals. J. Neurophysiology, 31:574-587, 1968. [ bib ] |
[304] | J. Cang, R. Renteria, M. Kaneko, X. Liu, D. Copenhagen, and M. Stryker. Development of precise maps in visual cortex requires patterned spontaneous activity in the retina. Neuron, 48(5):797-809, 2005. [ bib ] |
[305] | R. M. Capocelli and L. M. Ricciardi. Diffusion approximation and first passage time problem for a neuron model. Kybernetik, 8:214-223, 1971. [ bib ] |
[306] | M. Carandini, J. B. Demb, V. Mante, D. J. Tolhurst, Y. Dan, B. A. Olshausen, J. L. Gallant, and N. C. Rust. Do we know what the early visual system does? Journal of Neuroscience, 25(46):10577-10597, 2005. [ bib ] |
[307] | M. Carandini, J. B. Demb, V. Mante, D. J. Tolhurst, Y. Dan, B. A. Olshausen, J. L. Gallant, and N. C. Rust. Do we know what the early visual system does? The Journal of Neuroscience, 25(46):10577-97, 2005. [ bib ] |
[308] | M. Carandini, D. Heeger, and J. Movshon. Linearity and gain control in V1 simple cells. Cerebral cortex, 13:401-443, 1999. [ bib ] |
[309] | M. Carandini and D. L. Ringach. Predictions of a recurrent model of orientation selectivity. Vision Research, 37(21):3061-3071, 1997. [ bib ] |
[310] | G. Carpenter. Distributed learning, recognition and prediction by ART and ARTMAP neural networks. Neural Networs, 10:1473-1494, 1997. [ bib ] |
[311] | G. Carpenter and S. Grossberg. ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition. Neural Networks, 3:129-152, 1990. [ bib ] |
[312] | G. Carpenter and S. Grossberg. Art 2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics, 26:4919-4930, 1987. [ bib ] |
[313] | C. E. Carr. The Development of Nucleus Laminaris in the Barn Owl. In G. A. Manley, G. M. Klump, C. Köppl, H. Fastl, and H. Oeckinghaus, editors, Advances in Hearing Research, pages 24-30, Singapure, 1995. World Scientific. [ bib ] |
[314] | C. E. Carr. Processing of temporal information in the brain. Annual Rev. Neurosci., 16:223-43, 1993. [ bib ] |
[315] | C. E. Carr and R. E. Boudreau. Central projections of auditory nerve fibers in the barn owl. J. Comp. Neurol., 314:306-318, 1991. [ bib ] |
[316] | C. E. Carr and M. Konishi. A circuit for detection of interaural time differences in the brain stem of the barn owl. J. Neurosci., 10:3227-3246, 1990. [ bib ] |
[317] | C. E. Carr and M. Konishi. Axonal delay lines for measurement in the owl's brainstem. Proc. Natl. Acad. Sci USA, 85:8311-8315, 1988. [ bib ] |
[318] | S. Cash and R. Yuste. Input summation by cultured pyramidal neurons is linear and position-independent. J Neurosci, 18(1):10-15, 1998. [ bib ] |
[319] | S. Cash and R. Yuste. Input summation by cultured pyramidal neurons is linear and position-independent. J. Neuroscience, 18:10-15, 1988. [ bib ] |
[320] | G. Castellani, E. Quinlan, L. Shouval, and H. Cooper. A biophysical model of bidirectional synaptic plasticity: dependence on ampa and nmda receptors. Proc. Natl. Acad. Sci. USA, 98:12772-12777, 2001. [ bib ] |
[321] | B. Cessac, B. Doyon, M. Quoy, and M. Samuleides. Mean-field equations, bifurcation map and route to chaos in discrete time neural networks. Phyisca D, 74:24-44, 1994. [ bib ] |
[322] | M. J. Chacron, B. Lindner, and A. Longtin. Noise shaping by interval correlations increases information transfer. Phys. Rev. Letters, 92:80601, 2004. [ bib ] |
[323] | F. Chance. Modeling Cortical Dynamics and the Responses of Neurons in the Primary Visual Cortex. PhD dissertaion. Brandeis University, 2000. [ bib ] |
[324] | F. S. Chance, S. du Lac, and L. F. Abbott. An integrate-and-fire model of spike-rate dynamics. Society of Neuroscience Abstracts, 27:821.44, 2001. [ bib ] |
[325] | F. S. Chance, S. B. Nelson, and L. F. Abbott. Complex cells as cortically amplified simple cells. Nat Neurosci, 2(3):277-282, Mar 1999. [ bib ] |
[326] | H. Chateau and T. Fukai. A stochastic model to predict the consequences of arbitrary forms of spike-timing dependent plasticity. Neural Computation, 15:597-620, 2003. [ bib ] |
[327] | R. Chavarriaga, E. Sauser, and W. Gerstner. Modelling directional firing properties of place cells. CNS Meeting 2003, 2003. [ bib ] |
[328] | R. Chavarriaga, T. Strösslin, D. Sheynikhovich, and W. Gerstner. Competition between cue response and place response: A model of rat navigation behaviour. Connection Science, 17:167-183, 2005. [ bib ] |
[329] | R. Chavarriaga, T. Strösslin, D. Sheynikhovich, and W. Gerstner. A computational model of parallel navigation systems in rodents. Neuroinformatics, 3:223-241, 2005. [ bib ] |
[330] | G. Chechik. Spike-timing-dependent plasticity and relevant mututal information maximization. Neural Computation, 15:1481-1510, 2003. [ bib ] |
[331] | G. Chechik and A. Globerson. Information bottleneck and linear projections of gaussian processes. Technical Report 4, Hebrew University, May 2003. [ bib ] |
[332] | G. Chechik, A. Globerson, N. Tishby, and Y. Weiss. Information bottleneck for Gaussian variables. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 15, 2003. [ bib ] |
[333] | G. Chechik, A. Globerson, N. Tishby, and Y. Weiss. Information bottleneck for Gaussian variables. The Journal of Machine Learning Research, 6:165-188, 2005. [ bib ] |
[334] | L. Chen, L. Lin, C. Barnes, and B. McNaughton. Head-direction cells in the rat posterior cortex. II. Contributions of visual and ideothetic information to the directional firing. Experimental Brain Research, 101(1):24-34, 1994. [ bib ] |
[335] | L. Chen, L. Lin, E. Green, C. Barnes, and B. McNaughton. Head-direction cells in the rat posterior cortex. I. Anatomical distribution and behavioral modulation. Experimental Brain Research, 101(1):8-23, 1994. [ bib ] |
[336] | X. Chen, F. Han, M.-M. Poo, and Y. Dan. Excitatory and suppressive receptive field subunits in awake monkey primary visual cortex (v1). Proceedings of the National Academy of Sciences, 104(48):19120-19125, Nov 2007. [ bib | DOI | http ] |
[337] | E. J. Chichilnisky. A simple white noise analysis of neuronal light responses. Network, 12(199-213), 2001. [ bib ] |
[338] | M. Y. Choi. Dynamic model of neural networks. Phys. Rev. Lett., 61:2809-2812, 1988. [ bib ] |
[339] | C. C. Chow. Phase-locking in weakly heterogeneous neuronal networks. Physica D, 118:343-370, 1998. [ bib ] |
[340] | C. C. Chow and N. Kopell. Dynamics of spiking neurons with electrical couplings. Neural Computation, 12:1643-1678, 2000. [ bib ] |
[341] | C. C. Chow and J. White. Spontaneous action potential fluctuations due to channel fluctuations. Bioph. J., 71:3013-3021, 1996. [ bib ] |
[342] | P. S. Chruchland and T. J. Sejnowski. The computational brain. MIT Press, Cambridge, 1992. [ bib ] |
[343] | C. Chubb and J. Talevich. Attentional control of texture orientation judgments. Vision Research, 42(3):311-330, Feb. 2002. [ bib ] |
[344] | A. Cichocki and R. Unbehauen. Neural Networks for Optimization and Signal Processing. John Wiley, Chichester, 1993. [ bib ] |
[345] | Clay and Goel. Diffusion model for firing of a neuron with varying threshold. J. theor. Biol, 39:633-644. [ bib ] |
[346] | C. W. G. Clifford. Perceptual adaptation: motion parallels orientation. TRENDS in Cognitive Sciences, 6(3):136-142, 2002. [ bib ] |
[347] | C. Clopath, R. Jolivet, A. Rauch, H.-R. Luescher, and W. Gerstner. Predicting neuronal activity with simple models of the threshold type: Adaptive exponential integrate-and-fire model with two compartments. Neurocomputing, xx:xx, 2007. http://www.sciencedirect.com/ ONLINE since Oct. 2006. [ bib ] |
[348] | A. H. Cohen, S. Rossignol, and S. Grillner. Neural Control of Rhythmic Movement in Vertebrates. John Wiley, New York, 1988. [ bib ] |
[349] | M. A. Cohen and S. Grossberg. Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE trans. on systems, man, and cybernetics, 13:815-823, 1983. [ bib ] |
[350] | H. S. Colburn, Y. an Han, and C. P. Culotta. Coincidence model of MSO responses. Hearing research, 49:335-3346, 1990. [ bib ] |
[351] | T. Collett, E. Dillman, A. Giger, and R. Wehner. Visual landmarks and route following in desert ants. Journal of Comparative Physiology, 170:435-442, 1992. [ bib ] |
[352] | G. L. Collingridge. A question of reliability. Nature, 371:652-653, 1994. [ bib ] |
[353] | G. L. Collingridge, S. J. Kehl, and H. McLennan. Excitatory amino acids in synaptic transmission in the schaffer collateral-commissural pathway of the rat hippocampus. J. Physiol., 334:33-46, 1983. [ bib ] |
[354] | J. Collins, C. Chow, A. Capela, and T. Imhoff. Aperiodic stochastic resonance. Physical Review E, 54:5575-5584, 1996. [ bib ] |
[355] | J. Connor, D. Walter, and R. McKown. Neural repetitive firing - modifications of the hodgkin-huxley axon suggested by experimental results from crustacean axons. Biophysical Journal, 18:81-102, 1977. [ bib ] |
[356] | B. W. Connors and M. J. Gutnick. Intrinsic firing patterns of diverse cortical neurons. Trends in Neurosci., 13:99-104, 1990. [ bib ] |
[357] | B. W. Connors and M. J. Gutnick. Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci, 13(3):99-104, 1990. [ bib ] |
[358] | J. Contreras-Vidal and W. Schultz. A predictive reinforcement model of dopamine neurons for learning approach behavior. J. Computational Neuroscience, 6:191-214, 1999. [ bib ] |
[359] | D. Cook, P. Schwindt, L. Grande, and W. Spain. Synaptic depression in the localization of sound. Nature, 421:66-70, 2003. [ bib ] |
[360] | E. P. Cook, J. A. Guest, Y. Liang, N. Y. Masse, and C. M. Colbert. Dendrite-to-soma input/output function of continuous time-varying signals in hippocampal ca1 pyramidal neurons. J Neurophysiol, 98(5):2943-2955, 2007. [ bib | DOI ] |
[361] | S. Coombes, M. R. Owen, and G. D. Smith. Mode locking in a periodically forced integrate-and-fire-or-burst neuron model. Phys Rev E Stat Nonlin Soft Matter Phys, 64(4 Pt 1):041914, Oct 2001. [ bib ] |
[362] | L. Cooper, N. Intrator, B. Blais, and H. Z. Shouval. Theory of cortical plasticity. World Scientific, Singapore, 2004. [ bib ] |
[363] | P. Cordo, J. Inglis, S. Verschueren, J. C. andD. M. Merfeld, and S. Rosenblum. Noise in human muscle spindels. Nature, 383:769-770, 1996. [ bib ] |
[364] | A. Corral, C. P. Perez, A. Diaz-Guilera, and A. Arenas. Self-organized criticality and synchronization in a lattice model of integrate-and-fire oscillators. Phys. Rev. Lett., 74:118-121, 1995. [ bib ] |
[365] | S. S. Correia, S. Bassani, T. C. Brown, M.-F. Lisé, D. S. Backos, A. El-Husseini, M. Passafaro, and J. A. Esteban. Motor protein-dependent transport of ampa receptors into spines during long-term potentiation. Nature Neuroscience, 11(4):457-466, Apr 2008. [ bib | DOI | http ] |
[366] | R. Courant and D. Hilbert. Methods of mathematical physics Part I. Wiley, 1989. [ bib ] |
[367] | T. Cover and J. Thomas. Elements of Information Theory. Wiley, New York, 1991. [ bib ] |
[368] | E. Covey and J. H. Casseday. The monaural nuclei of the lateral lemniscus in an echolocating bat: Parallel pathways for analyzing temporal features of sound. The Journal of Neuroscience, 11(11):3456-3470, 1991. [ bib ] |
[369] | J. Cowan. Statistical mechanics of nervous nets. In E. R. Caianiello, editor, Proceedings of the 1967 NATO Conference on Neural Networks, page xx. Springer, Berlin, 1968. [ bib ] |
[370] | J. Cowan. Advances in Neural Information Processing Systems, volume 6. Morgan Kaufmann Publishers, San Mateo, 1994. [ bib ] |
[371] | J. D. Cowan. A statistical mechancis of nervous activity. In M. Gerstenhaber, editor, Proceedings of 2nd American Mathematical Society Symposium on Mathematical Questions in Biology, pages 1-57. Amer. Math. Society, 1971. [ bib ] |
[372] | D. Cox, P. Meier, N. Oertelt, and J. DiCarlo. "Breaking" position-invariant object recognition. Nature Neuroscience, 8(9):1145-1147, 2005. [ bib ] |
[373] | D. R. Cox. Renewal theory. Methuen, London, 1962. [ bib ] |
[374] | D. R. Cox and D. V. Hinckley. Theoretical statistics. London: Chapman & Hall, 1974. [ bib ] |
[375] | D. R. Cox and P. A. W. Lewis. The statistical analysis of series of events. Methuen, London, 1966. [ bib ] |
[376] | A. Cressant, R. U. Muller, and B. Poucet. Remapping of place cell firing patterns after maze rotations. Experimental Brain Research, 143:470-479, 2002. [ bib ] |
[377] | A. Cressant, R. U. Muller, and B. Poucet. Further study of the control of place cell firing by intra-apparatus objects. Hippocampus, 9:423-431, 1999. [ bib ] |
[378] | A. Cressant, R. U. Muller, and B. Poucet. Failure of centrally placed objects to control the firing fields of hippocampal place cells. Journal of Neuroscience, 17(7):2531-2542, Apr. 1997. [ bib ] |
[379] | O. D. Creutzfeldt. Cortex cerebri. Springer-Verlag, Berlin Heidelberg New York, 1983. pp 76-78. [ bib ] |
[380] | F. Creutzig. Sufficient Encoding of Dynamical Systems. PhD thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, Universitätsbibliothek, 2008. [ bib ] |
[381] | F. Creutzig and H. Sprekeler. Predictive coding and the slowness principle: An information-theoretic approach. Neural Computation, 20(4):1026-1041, 2008. [ bib ] |
[382] | A. Crisanti and H. Sompolinsky. Dynamics of spin systems with randomly asymmetric bonds - ising spins and glauber dynamics. Phys. Rev. A, 37:4865-4874, 1988. [ bib ] |
[383] | R. E. Crist, M. K. Kapadia, G. Westheimer, and C. D. Gilbert. Perceptual learning of spatial localization: specificity for orientation, position, and context. The Journal of Neurophysiology, 78:2889-2894, 1997. [ bib ] |
[384] | R. E. Crist, W. Li, and C. D. Gilbert. Learning to see: experience and attention in primary visual cortex. Nature Neuroscience, 4(5):519-525, 2001. [ bib ] |
[385] | J. Cronin. Mathematical Aspects of Hodgkin Huxley Theory. Cambridge University Press, Cambridge, 1987. [ bib ] |
[386] | J. Crook and U. Eysel. GABA-induced inactivation of functionally characterized sites in cat visual cortex (area 18): effects on orientation tuning. Journal of Neuroscience, 12(5):1816, 1992. [ bib ] |
[387] | M. Cross and P. Hohenberg. Pattern formation outside of equilibrium. Reviews of Modern Physics, 65(3):851-1112, 1993. [ bib ] |
[388] | M. C. Cross and P. C. Hohenberg. Pattern formation outside of equilibrium. Review in Modern Physics, 65(3):851-109?, 1993. [ bib ] |
[389] | J. Csicsvari, B. Jamieson, K. Wise, and G. Buzsaki. Mechanisms of gamma oscillations in the hippocampus of the behaving rat. Neuron, 37:311-322, 2003. [ bib ] |
[390] | H. Câteau and T. Fukai. A stochastic method to preduct the consequence of arbitrary forms of spike-timing-dependent plasticity. Neural Computation, 15:597-620, 2003. [ bib ] |
[391] | J. S. adn D. Wierstra, M. Gagliolo, and F. Gomez. Training recurrent networks by evolino. Neural Computation, 19(3):757-779, 2007. [ bib ] |
[392] | D. Daley and D. Vere-Jones. An introduction to the theory of point processes. Springer, New York, 1988. [ bib ] |
[393] | Y. Dan and M. Poo. Spike Timing-Dependent Plasticity: From Synapse to Perception. Physiological Reviews, 86(3):1033, 2006. [ bib ] |
[394] | Y. Dan and M.-M. Poo. Spike timing-dependent plasticity of neural circuits. Neuron, 44(1):23-30, Sep 2004. [ bib ] |
[395] | Y. Dan and M. Poo. Spike timing-dependent plasticity of neural circuits. Neuron, 44:23-30, 2004. [ bib ] |
[396] | C. R. Darwin. Origin of certain instincts. Nature, 7:417-418, 1873. Early mentioning of dead reckoning for navigation. [ bib ] |
[397] | A. Davydov. Quantum mechanics. Pergamon Press New York, 1976. [ bib ] |
[398] | N. Daw, Y. Niv, and P. Dayan. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience, 8:1704-1711, 2005. [ bib ] |
[399] | P. Dayan. Motivated reinforcement learning. Advances in Neural Information Processing Systems 14, 14:11-18, 2002. [ bib ] |
[400] | P. Dayan. The convergens of TD(λ) for general λ. Machine learning, 8:341-362, 1992. [ bib ] |
[401] | P. Dayan and L. F. Abbott. Theoretical Neuroscience. MIT Press, Cambridge, 2001. [ bib ] |
[402] | P. Dayan and L. F. Abbott. Theoretical neuroscience; Computational and Mathematical Modeling of Neural Systems. The MIT Press; Cambridge, Massachusetts; London, England, 2001. [ bib ] |
[403] | P. Dayan and M. Häusser. Plasticity kernels and temporal statistics. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004. [ bib ] |
[404] | P. Dayan, M. Häusser, and M. London. Plasticity kernels and temporal statistics. In Advances in Neural Information Processing Systems 16. MIT Press, 2004. [ bib ] |
[405] | P. Dayan and T. Sejnowski. TD(λ) converges with probability 1. Machine Learning, 14:295-301, 1994. [ bib ] |
[406] | R. L. De Valois, N. P. Cottaris, L. E. Mahon, S. D. Elfar, and J. A. Wilson. Spatial and temporal receptive fields of geniculate and cortical cells and direction selectivity. Vision Research, 40:3685-3702, 2000. [ bib ] |
[407] | R. L. De Valois, E. W. Yund, and N. Hepler. The orientation and direction selectivity of cells in macaque visual cortex. Vision Research, 22:531-544, 1982. [ bib ] |
[408] | G. C. DeAngelis, I. Ohzwaw, and R. D. Freeman. Receptive-field dynamics in the central visual pathways. Trends in Neurosci., 18:451-458, 1995. [ bib ] |
[409] | G. C. DeAngelis, G. M. Ghose, I. Ohzawa, and R. D. Freeman. Functional micro-organization of primary visual cortex: Receptive field analysis of nearby neurons. Journal of Neuroscience, 19(9):4046-4064, 1999. [ bib ] |
[410] | G. C. DeAngelis, J. G. Robson, I. Ohzawa, and R. D. Freeman. Organization of suppression in receptive fields of neurons in cat visual cortex. Journal of Neurophysiology, 68(1):144-163, 1992. [ bib ] |
[411] | D. Debanne, B. Gähwiler, and S. Thompson. Long-term synaptic plasticity between pairs of individual CA3 pyramidal cells in rat hippocampal slice cultures. J. Physiol., 507:237-247, 1998. [ bib ] |
[412] | D. Debanne, B. H. Gähwiler, and S. M. Thompson. Asynchronous pre- and postsynaptic activity induces associative long-term depression in area CA1 of the rat Hippocampus in vitro. Proc. Natl. Acad. Sci. USA, 91:1148-1152, 1994. [ bib ] |
[413] | D. Debanne, B. H. Gähwiler, and S. M. Thomson. Asynchronous pre- and postsynaptic activity induces associative long-term depression in area CA1 of the rat hippocampus. PNAS, 91:1148-1152, 1994. [ bib ] |
[414] | G. Deco and B. Schürmann. Information transmission and temporal code in central spiking neurons. Physical Review Letters, 79:4697-4700, 1997. [ bib ] |
[415] | G. Deco and J. Zihl. Top-down selective visual atention: a neurodynamical approach. Visual Cognition, 2001. [ bib ] |
[416] | H. Dedieu, T. Schimming, and M. Hasler. Separating a chaotic signal from noise and applications. In G. Chen, editor, Controlling Chaos and Bifurcations in Engineering Systems, pages 457-476. CRC Press, Boca Raton, Fl. USA, 1999. [ bib ] |
[417] | J. Deppisch, H. U. Bauer, T. Schillen, P. König, K. Pawelzik, and T. Geisel. Alternating oscillatory and stochastic states in an network of spiking neurons. Network, 4:243-257, 1993. [ bib ] |
[418] | B. Derrida, E. Gardner, and A. Zippelius. An exactly solvable asymmetric neural network model. Europhysics Letters, 4:167-173, 1987. [ bib ] |
[419] | B. Derrida and Y. Pomeau. Random networks of automata - a simple annealed approximation. Europhysics Letters, 1:45-49, 1986. [ bib ] |
[420] | N. Desai, L. Rutherford, and G. Turrigiano. Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nature Neuroscience, 2(6):515-520, 1999. [ bib ] |
[421] | N. S. Desai. Homeostatic plasticity in the CNS: synaptic and intrinsic forms. Journal of Physiology, 97:391-402, 2003. [ bib ] |
[422] | N. S. Desai, R. H. Cudmore, S. B. Nelson, and G. G. Turrigiano. Critical periods for experience-dependent scaling in visual cortex. Nature Neuroscience, 5:783-789, 2002. [ bib ] |
[423] | R. Desimone and J. Duncan. Neural mechanisms of selective visual-attention. Annual Review of Neuroscience, 18:193-222, 1995. [ bib ] |
[424] | A. Destexhe. Simplified models of neocortical pyramidal cells preserving somatodendritic voltage attenuation. Neurocomputing, 38:167-173, 2001. [ bib ] |
[425] | A. Destexhe. Conductance-based integrate-and-fire models. Neural Computation, 9:503-514, 1997. [ bib ] |
[426] | A. Destexhe, D. Contreras, and M. Steriade. Mechanisms underlying the synchronizing action of corticothalamic feedback through inhibition of thalamic relay cells. J Neurophysiol, 79(2):999-1016, 1998. [ bib ] |
[427] | A. Destexhe and D. Pare. Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. Journal of Neurophysiology 81, 81:1531-1547, 1999. [ bib ] |
[428] | A. Destexhe, M. Rudolph, and D. Pare. The high-conductance state of neocortical neurons in vivo. Nature Reviews Neuroscience, 4:739-751, 2003. [ bib ] |
[429] | M. DeWeese, M. W., and A.M.Zador. Binary spiking in auditory cortex. Journal of Neuroscience, 23:7940-7949, 2003. [ bib ] |
[430] | M. Deweese and A. Zador. Shared and private variability in the auditory cortex. J. Neurophysiology, 92:1840-1855, 2004. [ bib ] |
[431] | K. Diba and G. Buzsáki. Forward and reverse hippocampal place-cell sequences during ripples. Nature Neuroscience, 10:1241-1242, 2007. [ bib ] |
[432] | S. Diederich and M. Opper. Learning of correlated patterns in spin-glass networks by local learning rule. Phys. Rev. Lett., 58:949-952, 1987. [ bib ] |
[433] | M. Diesmann, M.-O. Gewaltig, and A. Aertsen. Stable propagation of synchronous spiking in cortical neural networks. Nature, 402:529-533, 1999. [ bib ] |
[434] | A. G. Dimitrov and J. P. Miller. Neural coding and decoding: Communication channels and decoding. Network: Computation in Neural Systems, 12:441-472, 2001. [ bib ] |
[435] | P. C. Dodwell. The Lie transformation group model of visual perception. Perception and Psychophysics, 34(1):1-16, 1983. [ bib ] |
[436] | D. W. Dong. Spatiotemporal inseparability of natural images and visual sensitivities. In J. M. Zanker and J.Zeil, editors, Computational, neural & ecological constraints of visual motion processing, page 371, 2001. [ bib ] |
[437] | D. W. Dong and J. J. Atick. Statistics of natural time-varying images. Network: Computation in Neural Systems, 6(3):345-358, 1995. [ bib ] |
[438] | R. J. Douglas, C. Koch, M. Mahowald, K. Martin, and H. Suarez. Recurrent excitation in neocortical circuits. Science, 269:981-985, 1995. [ bib ] |
[439] | R. J. Douglas and K. Martin. A functional microcircuit for cat visual cortex. J. Physiol. London, 440:735-769, 1991. [ bib ] |
[440] | R. J. Douglas and K. A. C. Martin. Neocortex. In S. GM, editor, The synaptic organization of the brain., Oxford, 1990. Oxford Univ. Press. [ bib ] |
[441] | J. Douglass, L. Wilkens, E. Pantazelou, and F. Moss. Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance. Nature, 365:337-340, 1993. [ bib ] |
[442] | K. Doya. Temporal difference learning in continuous time and space. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8, pages 1073-1079. The MIT Press, 1996. [ bib | .html ] |
[443] | K. Doya. Metalearning and neuromodulation. Neural Networks, 15:495-506, 2002. [ bib ] |
[444] | K. Doya. Temporal difference learning in continuous time and space. In Advances in Neural Information Processing systems 8,, pages 1073-1079. MIT press, 1996. [ bib ] |
[445] | P. Drew and L. Abbott. Extending the effects of spike-timing-dependent plasticity to behavioral timescales. Proceedings of the National Academy of Sciences, 103(23):8876-8881, 2006. [ bib ] |
[446] | G. Dreyfus, J. Martinez, M. Samuelides, M. Gordon, F. Badran, and S. Thiria. Réseaux de neurones: méthodologie et applications. Editions Eyrolles, 2002. [ bib ] |
[447] | S. Druckmann, Y. Bannitt, A. A. Gidon, F. Schuermann, and I. Segev. A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci, 1(1), November 2007. [ bib ] |
[448] | J.-l. Du and M.-m. Poo. Rapid bdnf-induced retrograde synaptic modification in a developing retinotectal system. Nature, 429(6994):878-883, June 2004. [ bib | http ] |
[449] | R. Duda, P. Hart, and D. Stock. Pattern Classification. John Wiley, 2001. [ bib ] |
[450] | S. M. Dudek and M. F. Bear. Bidirectional long-term modification of synaptic effectiveness in the adult and immature hippocampus. J. Neuroscience, 13:2910-2918, 1993. [ bib ] |
[451] | S. M. Dudek and M. F. Bear. Homosynaptic long-term depression in area ca1 of hippocampus and effects of n-methyl-d-aspartate receptor blockade. Proc. Natl. Acad. Sci. USA, 89:4363-4367, 1992. [ bib ] |
[452] | V. Dufiet and J. Boissonade. Conventional and unconventional Turing patterns. Journal of Chemical Physics, 96(1):664-673, 1992. [ bib ] |
[453] | I. Duguid and P. Sjostrom. Novel presynaptic mechanisms for coincidence detection in synaptic plasticity. Curr Opin Neurobiol, 16(3):312-322, 2006. [ bib ] |
[454] | S. Dunkelmann and G. Radons. Neural networks and abelian sandpile models of self-organized criticality. In Proceedings of the ICANN'94, pages 867-870, 1994. [ bib ] |
[455] | O. E. Neural networks, principal components, and subspaces. International Journal of Neural Systms, 1:61-68, 1989. [ bib ] |
[456] | E.Ott and J.C.Sommerer. Blowout bifurcations: the occurrence of riddled basins. Phys. Lett.A, pages 39-47, 1994. [ bib ] |
[457] | J. Echegoyen, A. Neu, K. Graber, and I. Soltesz. Homeostatic Plasticity Studied Using In Vivo Hippocampal Activity-Blockade: Synaptic Scaling, Intrinsic Plasticity and Age-Dependence. PLoS ONE, 2(8):e700, 2007. [ bib ] |
[458] | R. Eckhorn, R. Bauer, W. Jordan, M. Brosch, W. Kruse, M. Munk, and H. J. Reitboeck. Coherent oscillations: A mechanism of feature linking in the visual cortex? Biol. Cybern., 60:121-130, 1988. [ bib ] |
[459] | R. Eckhorn, A. Frien, R. Bauer, T. Woelbern, and H. Kehr. High frequency (60-90 hz) oscillations in primary visual cortex of awake monkey. Neuro Report, 4:243-246, 1993. [ bib ] |
[460] | R. Eckhorn, O. J. Grüsser, J. Kröller, K. Pellnitz, and B. Pöpel. Efficiency of different neural codes: information transfer calculations for three different neural systems. Biol. Cybern., 22:49-60, 1976. [ bib ] |
[461] | R. Eckhorn, F. Krause, and J. L. Nelson. The rf-cinematogram: a cross-correlation technique for mapping several visual fields at once. Biol. Cybern., 69:37-55, 1993. [ bib ] |
[462] | R. Eckhorn, H. J. Reitboeck, M. Arndt, and P. Dicke. Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex. Neural Computation, 2:293-307, 1990. [ bib ] |
[463] | B. Edwards and G. H. Wakefield. The spectral shaping of neural discharges by refractory effects. J. Acoust. Soc. Am., 93:3553-3564, 1993. [ bib ] |
[464] | V. Egger, D. Feldmeyer, and B. Sakmann. Coincidence detection and changes of synaptic efficacy in spiny stellated neurons ins barrel cortex. Nature Neuroscience, 2:1098-1105, 1999. [ bib ] |
[465] | V. Egger, D. Feldmeyer, and B. Sakmann. Coincidence detection and changes of synaptic efficacy in spiny stellate neurons in rat barrel cortex. Nature Neuroscience, 2(12):1098-1106, 1999. [ bib ] |
[466] | J. J. Eggermont. The correlative brain. Springer, Berlin Heidelberg New York, 1990. [ bib ] |
[467] | J. Eggert. Derivation of pool dynamics from microscopic neuronal models. In W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, editors, Artificial Neural Networks - ICANN'97, pages 109-114. Springer, 1997. [ bib ] |
[468] | J. Eggert and J. van Hemmen. Unifying framework for neuronal assembly dynamics. Physical Review E, 61(2):1855-1874, 2000. [ bib ] |
[469] | J. Eggert and J. L. van Hemmen. Modeling neuronal assemblies: theory and implementation. Neural Computation, 13:1923-1974, 2001. [ bib ] |
[470] | A. Egorov, B. Hamam, E. Fransen, and M. Hasselmo. Graded persistent activity in entorhinal cortex neurons. Nature, 420:173-178, 2002. [ bib ] |
[471] | H. Eichenbaum and N. Cohen. Representation in the hippocampus: what do hippocampal neurons code? Trends in Neuroscience, 11(6):244-248, 1988. [ bib ] |
[472] | H. Eichenbaum, P. Dudchenko, E. Wood, M. Shapiro, and H. Tanila. The hippocampus, memory, and place cells: Is it spatial memory or a memory space? Neuron, 23:209-226, 1999. [ bib ] |
[473] | H. Eichenbaum, M. Kuperstein, A. Fagan, and JNagode. Cue-sampling and goal-approach correlates of hippocampal unit activity in rats performing an odor-discrimination task. Journal of Neuroscience, 7(3):716-732, 1987. [ bib ] |
[474] | H. Eichenbaum, C. Stewart, and R. Morris. Hippocampal representation in place learning. J. Neuroscience, 10:3531–3542, 1990. [ bib ] |
[475] | R. Eichler West, E. D. Schutter, and G. Wilcox. Using evolutionary algorithms to search for control parameters in a nonlinear partial differential equation. In L. Davis, K. D. Jong, M. Vose, and L. Whitley, editors, Evolutionary Algorithms, IMA Volumes in Mathematics and its Applications, volume 111, pages 33-64. Springer-Verlag, 1999. [ bib ] |
[476] | W. Einhäuser, J. Hipp, J. Eggert, E. Körner, and P. König. Learning viewpoint invariant object representations using a temporal coherence principle. Biological Cybernetics, 93(1):79-90, 2005. [ bib ] |
[477] | W. Einhäuser, C. Kayser, P. König, and K. Körding. Learning the invariance properties of complex cells from their responses to natural stimuli. European Journal of Neuroscience, 15(3):475-86, 2002. [ bib ] |
[478] | O. Ekeberg, P. Wallen, A. Lansner, H. Traven, L. Brodin, and S. Grillner. A computer based model for realistic simulations of neural networks. Biol. Cybern., 65:81-90, 1991. [ bib ] |
[479] | A. Ekstrom, M. Kahana, J. Caplan, T. Fields, E. Isham, E. Newman, and I. Fried. Cellular networks underlying human spatial navigation. Nature, 425:184-188, 2003. [ bib ] |
[480] | S. Ellias and S. Grossberg. Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off-surround networks. Biological Cybernetics, 20:69-98, 1975. [ bib ] |
[481] | A. K. Engel, P. König, A. K. Kreiter, T. B. Schillen, and W. Singer. Temporal encoding in the visual cortex: new vistas on integration in the nervous system. Trends in Neurosciences, 15(6):218-226, 1992. [ bib ] |
[482] | A. K. Engel, P. König, A. K. Kreiter, and W. Singer. Interhemispheric synchronization of oscillatory neural responses in cat visual cortex. Science, 252:1177-1179, 1991. [ bib ] |
[483] | A. K. Engel, P. König, and W. Singer. Direct physiological evidence for scene segmentation by temporal coding. Proc. Natl. Acad. Sci. USA, 88:9136-9140, 1991. [ bib ] |
[484] | F. Engert, H. W. Tao, L. I. Zhang, and M.-M. Poo. Moving visual stimuli rapidly induce direction sensitivity of developing tectal neurons. Nature, 419:470-475, 2002. [ bib ] |
[485] | A. Erisir, D. Lau, B. Rudy, and C. S. Leonard. Specific k+ channels are required to sustain high frequency firing in fast-spiking neocortical interneurons. J. Neurophysiology, 82:2476-2489, 1999. [ bib ] |
[486] | B. Ermentrout. Reduction of conductance-based models with slow synapses to neural nets. Neural Computation, 6:679-695, 1994. [ bib ] |
[487] | G. B. Ermentrout. Type i membranes, phase resetting curves, and synchrony. Neural Computation, 8(5):979-1001, 1996. [ bib ] |
[488] | G. B. Ermentrout. Oscillator death in populations of all to all coupled nonlinear oscillators. Physica D, 41:219-231, 1990. [ bib ] |
[489] | G. B. Ermentrout. Synchronization in a pool of mutually coupled oscillators with random frequencies. J. Math. Biol., 22:1-9, 1985. [ bib ] |
[490] | G. B. Ermentrout. n:m phase-locking in weakly coupled oscillators. J. Math. Biology, 12:327-342, 1981. [ bib ] |
[491] | G. B. Ermentrout and J. D. Cowan. Large scale spatially organized activity in neural nets. SIAM J. Appl. Math., 38:1-21, 1980. [ bib ] |
[492] | G. B. Ermentrout and J. D. Cowan. Temporal oscillations in neuronal nets. J. Math. Biol., 7:265-280, 1979. [ bib ] |
[493] | G. B. Ermentrout and N. Kopell. Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM J. Applied Mathematics, 46:233-253, 1986. [ bib ] |
[494] | G. B. Ermentrout and N. Kopell. Frequency plateaus in a chain of weakly coupled oscillators. SIAM J. on Mathematical Analysis, 15:215-237, 1984. [ bib ] |
[495] | G. B. Ermentrout, M. Pascal, and B. Gutkin. The effects of spike frequency adaptation and negative feedback on the synchronization of neuronal oscillators. Neural Computation, 13:1285-1310, 2001. [ bib ] |
[496] | U. A. Ernst, K. R. Pawelzik, C. Sahar-Pikielny, and M. T. dyks. Intracortical origin of visual maps. Nature Neuroscience, 4:431-436, 2001. [ bib ] |
[497] | U. Ernst, K. Pawelzik, and T. Geisel. Multiple phase clustering of globally coupled neurons with delay. In M. Marinaro and P. G. Morasso, editors, Proceedings of the ICANN'94, pages 1063-1065, Berlin Heidelberg New York, 1994. Springer-Verlag. [ bib ] |
[498] | U. Ernst, K. Pawelzik, and T. Geisel. Synchonization induced by temporal delays in pulse-coupled oscillators. Phys. Rev. Lett., 74:1570-1573, 1995. [ bib ] |
[499] | U. Ernst, K. Pawelzik, and T. Geisel. Multistable feature binding with noisy integrate-and-fire nerons. preprint, page xx, 1995. [ bib ] |
[500] | E. Erwin and K. Miller. Correlation-based development of ocularly matched orientation and ocular dominance maps: determination of required input activities. J. Neuroscience, 18:9870-9895, 1998. [ bib ] |
[501] | E. Erwin, K. Obermayer, and K. Schulten. Models of orientation and ocular dominance columns in the visual cortex: a critcal comparison. Neural Comput., 7:425-468, 1995. [ bib ] |
[502] | E. N. Eskandar, B. J. Richmond, J. A. Hertz, L. M. Optican, and K. Troels. Decoding of neuronal signals in visual pattern recognition. In Advances in Neural Information Processing, volume 4, San Mateo CA, 1992. Morgan Kaufman. [ bib ] |
[503] | J. A. Esteban. Ampa receptor trafficking: a road map for synaptic plasticity. Mol Interv, 3(7):375-385, Oct 2003. [ bib | DOI | http ] |
[504] | A. Etienne, R. Maurer, J. Berlie, B. Reverdin, T.Rowe, J. Georgakopoulos, and V. Seguinot. Navitation through vector addition. Nature, 396:161-164, 1998. [ bib ] |
[505] | A. S. Etienne, R. Maurer, V. Boulens, A. Levy, and T. Rowe. Resetting the path integrator: a basic condition for route-based navigation. Journal of Experimental Biology, 207(Pt 9):1491-1508, Apr. 2004. [ bib ] |
[506] | C. W. Eurich, J. D. Cowan, and J. G. Milton. Hebbian delay adaptation in a network of intergrate-and-fire neurons. In W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, editors, Artificial Neural Networks, ICANN'97, pages 157-162. Springer-Verlag, Heidelberg, 1997. [ bib ] |
[507] | C. W. Eurich, U. Ernst, and K. Pawelzik. Continuous dynamics of neuronal delay adaptatiion. In L. Niklasson, M. Boden, and T. Ziemke, editors, ICANN'98, pages 355-360. Springer, 1998. [ bib ] |
[508] | C. W. Eurich, K. Pawelzik, U. Ernst, J. D. Cowan, and J. G. Milton. Dynamics of self-organized delay adaption. Phys. Rev. Lett., 82:1594-1597, 1999. [ bib ] |
[509] | M. Fahle. Perceptual learning: specificity versus generalization. Current Opinion in Neurobiology, 15:154-160, 2005. [ bib ] |
[510] | M. Fahle. Visual learning in humans. The Journal of Vision, 4:879-890, 2004. [ bib ] |
[511] | M. Fahle. Specificity of learning curvature, orientation, and vernier discriminations. Vision Research, 37:1885-1895, 1997. [ bib ] |
[512] | M. Fahle and S. Edelman. Long-term learning in vernier acuity: effects of stimulus orientation, range and of feedback. Vision Research, 33:397-412, 1993. [ bib ] |
[513] | A. L. Fairhall, G. Lewen, W. Bialek, and R. van Steveninck. Efficiency and ambiguity in an adaptive neural code. Nature, 412:787-792, 2001. [ bib ] |
[514] | A. L. Fairhall, G. D. Lewen, W. Bialek, and R. R. de Ruyter Van Steveninck. Efficiency and ambiguity in an adaptive neural code. Nature, 412(6849):787-792, 2001. [ bib | DOI ] |
[515] | M. Falconbridge, R. Stamps, and D. Badcock. A simple hebbian/anti-hebbian network learns the sparse, independent components of natural images. Neural Computation, 18:415-429, 2006. [ bib ] |
[516] | M. A. Farries and A. L. Fairhall. Reinforcement Learning With Modulated Spike Timing Dependent Synaptic Plasticity. J Neurophysiol, 98(6):3648-3665, 2007. [ bib | DOI | http ] |
[517] | H. J. S. Feder and J. Feder. Self-organized criticality in a stick-slip process. Phys. Rev. Lett., 66:2669-2672, 1991. [ bib ] |
[518] | J. D. Feigenbaum and E. T. Rolls. Allocentric and egocentric spatial information processing in the hippocampal formation of the behaving primate. Psychobiology, 19(1):21-40, 1991. [ bib ] |
[519] | D. Feldman. Timing-based ltp and ltd and vertical inputs to layer ii/iii pyramidal cells in rat barrel cortex. Neuron, 27:45-56, 2000. [ bib ] |
[520] | D. Feldman and M. Brecht. Map plasticity in somatosensory cortex. Science, 310:810 - 815, 2005. [ bib ] |
[521] | D. E. Feldman. Timing-based LTP and LTD at vertical input to layer II/III pyramidal cells in rat barrel cortex. Neuron, 27:45-56, July 2000. [ bib ] |
[522] | J. L. Feldman and J. D. Cowan. Large-scale activity in neural nets i: Theory with application to motoneuron pool responses. Biol. Cybern., 17:29-38, 1975. [ bib ] |
[523] | D. Felleman and D. van Essen. Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1:1-47, 1991. [ bib ] |
[524] | D. Felleman and D. Van Essen. Distributed Hierarchical Processing in the Primate Cerebral Cortex. Cerebral Cortex, 1(1):1-47, 2001. [ bib ] |
[525] | D. J. Felleman and D. C. Van Essen. Distributed Hierarchical Processing in the Primate Cerebral Cortex. Cereb. Cortex, 1(1):1-a-47, 1991. [ bib | DOI | http ] |
[526] | J. Fellous, A. Houweling, R. Modi, R. Rao, P. Tiesinga, and T. Sejnowski. Frequency Dependence of Spike Timing Reliability in Cortical Pyramidal Cells and Interneurons. Journal of Neurophysiology, 85(4):1782-1787, 2001. [ bib ] |
[527] | G. Felsen and Y. Dan. A natural approach to studying vision. Nat Neurosci, 8(12):1643-1646, Dec 2005. [ bib ] |
[528] | J. Feng. Is the integrate-and-fire model good enough - a review. Neural Networks, 14:955-975, 2001. [ bib ] |
[529] | J. Feng and D. Brown. Impact of correlated input on the output of the integrate-and-fire model. Neural Computation, 12:671-692, 2000. [ bib ] |
[530] | A. A. Fenton, G. Csizmadia, and R. U. Muller. Conjoint control of hippocampal place cell firing by two visual stimuli. I. the effects of moving the stimuli on firing fiel positions. Journal of general physiology, 116:191-209, 2000. [ bib ] |
[531] | A. A. Fenton, M. Wesierska, Y. Kaminsky, and J. Bures. Both here and there: simultaneous expression of autonomous spatial memories in rats. Proceedings of the National Academy of Sciences of the United States of America, 95(19):11493-11498, Sept. 1998. [ bib ] |
[532] | J. Ferbinteanu and M. L. Shapiro. Prospective and retrospective memory coding in the hippocampus. Neuron, 40(6):1227-1239, Dec. 2003. [ bib ] |
[533] | D. Ferster. Orientation selectivity of synaptic potentials in neurons of cat primary visual cortex. J . Neurophysiol., 6:1284-1301, 1986. [ bib ] |
[534] | E. E. Fetz and B. Gustafsson. Relation between shapes of post-synaptic potentials and changes in firing probability of cat motoneurones. J. Physiol., 341:387-410, 1983. [ bib ] |
[535] | J. Fiala, S. Grossberg, and D. Bullock. Metabotropic glutamate receptor activation in cerebellar purkinje cells as a substrate for adaptive timing of the classically conditioned eye-blink reflex. J. Neurosci., 16:3760-3774, 1996. [ bib ] |
[536] | I. Fiete and H. Seung. Gradient learning in spiking neural networks by dynamic perturbation of conductances. Physical Review Letters, 97:48104, 2006. [ bib ] |
[537] | I. Fiete and H. Seung. Gradient Learning in Spiking Neural Networks by Dynamic Perturbation of Conductances. Physical Review Letters, 97(4):48104, 2006. [ bib ] |
[538] | I. Fine and R. A. Jacobs. Comparing perceptual learning across tasks: a review. The Journal of Vision, 2:190-203, 2002. [ bib ] |
[539] | S. Fiori. Unsupervised learning on Lie groups. International Journal on Neural Systems, 12(3-4):259-277, 2002. [ bib ] |
[540] | F. P. Fischer, C. Köppld, and G. A. Manley. The basilar papilla of the barn owl tyto alba: a quantitative morphological sem analysis. Hearing Res., 34:87-102, 1988. [ bib ] |
[541] | R. FitzHugh. Impulses and physiological states in models of nerve membrane. Biophys. J., 1:445-466, 1961. [ bib ] |
[542] | D. C. Fitzpatrick, J. S. Kanwal, J. A. Butman, and N. Suga. Combination-sensitive neurons in the primary auditory cortex of the mustached bat. The Journal of Neuroscience, 13(3):931-940, 1993. [ bib ] |
[543] | D. Floreano and F. Mondada. Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot. In Husbands, Cliff, Meyer, and Wilson, editors, From Animals to Animats III. MIT Press, 1994. [ bib ] |
[544] | R. V. Florian. Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity. Neural Computation, 19:1468-1502, 2007. [ bib ] |
[545] | R. V. Florian. Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity. Neural Computation, 19(6):1468-1502, 2007. [ bib ] |
[546] | C. Fohlmeister, W. Gerstner, R. Ritz, and J. L. van Hemmen. Spontaneous excitations in the visual cortex: stripes, spirals, rings, and collective bursts. Neural Comput., 7:905 - 914, 1995. [ bib ] |
[547] | R. Fonseca, U. Nägerl, R. Morris, and T. Bonhoeffer. Competition for memory: Hippocampal ltp under the regimes of reduced protein synthesis. Neuron, 44:1011-1020, 2004. [ bib ] |
[548] | D. Foster, R. Morris, and P. Dayan. Models of hippocampally dependent navigation using the temporal difference learning rule. Hippocampus, 10:1-16, 2000. [ bib ] |
[549] | D. J. Foster and M. A. Wilson. Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature, 440(7084):680-683, Mar. 2006. [ bib ] |
[550] | T. C. Foster, C. A. Castro, and B. L. McNaughton. Spatial selectivity of rat hippocampal neurons: dependence on preparedness for movement. Science, 244(4912):1580-1582, June 1989. [ bib ] |
[551] | N. Fourcaud and N. Brunel. Dynamics of the firing probability of noisy integrate-and-fire neurons. Neural Computation, 14:2057-2110, 2002. [ bib ] |
[552] | N. Fourcaud-Trocme, D. Hansel, C. van Vreeswijk, and N. Brunel. How spike generation mechanisms determine the neuronal response to fluctuating inputs. N. Neuroscience, 23:11628-11640, 2003. [ bib ] |
[553] | N. Fourcaud-Trocme, D. Hansel, C. van Vreeswijk, and N. Brunel. How spike generation mechanisms determine the neuronal response to fluctuating input. J. Neuroscience, 23:11628-11640, 2003. [ bib ] |
[554] | N. Fourcaud-Trocme, D. Hansel, C. van Vreeswijk, and N. Brunel. How spike generation mechanisms determine the neuronal response to fluctuating inputs. J Neurosci, 23(37):11628-11640, 2003. [ bib ] |
[555] | K. Fox and N. Daw. A model for the action of NMDA conductances in the visual cortex. Neural Computation, 4:59-83, 1992. [ bib ] |
[556] | L. Franco, E. Rolls, N. Aggelopoulos, and J. Jerez. Neuronal selectivity, population sparseness, and ergodicity in the inferior temporal visual cortex. Biological Cybernetics, 96(6):547-560, 2007. [ bib ] |
[557] | L. M. Frank, G. B. Stanley, and E. N. Brown. Hippocampal plasticity across multiple days of exposure to novel environments. Journal of Neuroscience, 24(35):7681-7689, Sept. 2004. [ bib ] |
[558] | M. Franz and H. Mallot. Biomimetic robot navigation. Robotics and Autonomous Systems, 30:133-153, 2000. [ bib ] |
[559] | M. Franz, B. Schölkopf, H. Mallot, and H. Bülthoff. Learning View Graphs for Robot Navigation. Autonomous Robots, 5:111-125, 1998. [ bib ] |
[560] | M. Franzius. Slowness and Sparseness for Unsupervised Learning of Spatial and Object Codes from Naturalistic Data. PhD thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, Universitätsbibliothek, 2008. [ bib ] |
[561] | M. Franzius. Unüberwachtes Lernen von Texturen in einem hierarchischen Neuronalen Netzwerk mittels natürlicher Stimuli. Diplomarbeit, Lehrstuhl für Grafische Systeme, Brandenburgische Technische Universität Cottbus, 2003. [ bib ] |
[562] | M. Franzius, H. Sprekeler, and L. Wiskott. Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Computationl Biology, 3(8):e166, Aug 2007. [ bib | DOI | http ] |
[563] | M. Franzius, H. Sprekeler, and L. Wiskott. Slowness leads to place cells. In Proceedings CNS 2006, 2006. [ bib ] |
[564] | M. Franzius, H. Sprekeler, and L. Wiskott. Slowness leads to place cells. In Proc. Berlin Neuroscience Forum 2006, Bad Liebenwalde, June 8-10, page 42, Berlin, 2006. [ bib ] |
[565] | M. Franzius, R. Vollgraf, and L. Wiskott. From grids to places. Journal of Computational Neuroscience, 22(3):297-299, Jun 2007. [ bib | DOI | http ] |
[566] | M. Franzius, N. Wilbert, and L. Wiskott. Unsupervised learning of invariant 3D-object representations with slow feature analysis. In Proc. 3rd Bernstein Symposium for Computational Neuroscience, Göttingen, September 24-27, page 105. Bernstein Center for Computational Neuroscience (BCCN) Göttingen, 2007. [ bib ] |
[567] | W. Freeman. The physiology of perception. Scientific American, Feb.91:34-41, 1991. [ bib ] |
[568] | A. French and R. Stein. A flexible neural analog using integrated circuits. IEEE transactions on bio-medical engineering, 17(3):248-253, 1970. [ bib ] |
[569] | U. Frey and R. Morris. Synaptic tagging and long-term potentiation. Nature, 385:533 - 536, 1997. [ bib ] |
[570] | D. Fricker and R. Miles. Epsp amplification and the precision of spike timing in hippocampal neurons. Neuron, 29:559-569, 2000. [ bib ] |
[571] | N. Friedman, O. Mosenzon, N. Slonim, and N. Tishby. Multivariate information bottleneck. In Proceedings of Uncertainty in AI, 2001. [ bib ] |
[572] | R. Froemke and Y. Dan. Spike-timing dependent plasticity induced by natural spike trains. Nature, 416:433-438, 2002. [ bib ] |
[573] | R. Froemke and Y. Dan. Spike-timing-dependent synaptic modification induced by natural spike trains. Nature, 416(6879):433-8, 2002. [ bib ] |
[574] | R. Froemke, M. Merzenich, and C. Schreiner. A synaptic memory trace for cortical receptive field plasticity. Nature, 450(7168):425-9, 2007. [ bib ] |
[575] | R. C. Froemke, M. M. Merzenich, and C. E. Schreiner. A synaptic memory trace for cortical receptive field plasticity. Nature, 450:425-429, 2007. [ bib ] |
[576] | R. C. Froemke, M.-M. Poo, and Y. Dan. Spike-timing-dependent synaptic plasticity depends on dendritic location. Nature, 434:221-225, 2005. [ bib ] |
[577] | R. C. Froemke, M. Poo, and Y. Dan. Spike-timing-dependent plasticity depends on dendritic location. Nature, 434:221-225, 2005. [ bib ] |
[578] | R. C. Froemke, I. Tsay, M. Raad, J. Long, and Y. Dan. Contribution of individual spikes in burst-induced long-term synaptic modification. J. Neurophysiology, 95:1620-1629, 2006. [ bib ] |
[579] | B. Frost and H. Mouritsen. The neural mechanisms of long distance animal navigation. Current Opinions in Neurobiololy, 16(4):481-8, 2006. [ bib ] |
[580] | F. Hermens, G. Luksys, W. Gerstner, and M. H. Herzog. Modeling spatial and temporal aspects of visual backward masking. Psych. Rev., 115:83-100, 2008. [ bib ] |
[581] | F. Fröhlich, M. Bazhenov, and T. J. Sejnowski. Pathological effect of homeostatic synaptic scaling on network dynamics in diseases of the cortex. Journal of Neuroscience, 28(7):1709-1720, 2008. [ bib | DOI | http ] |
[582] | Y.-X. Fu, K. Djupsund, H. Gao, B. Hayden, K. Shen, and Y. Dan. Temporal specificity in the cortical plasticity of visual space representation. Science, 296:1999-2003, 2002. [ bib ] |
[583] | U. Fuentes. Einflußder schicht- und arealstruktur auf die informationsverarbeitung im cortex. Master's thesis, Technische Universität München, 1993. [ bib ] |
[584] | U. Fuentes, R. Ritz, W. Gerstner, and J. L. van Hemmen. Vertical signal flow and oscillations in a 3-layer model of the cortex. J. Comput. Neurosci., 3:125-136, 1996. [ bib ] |
[585] | M. Fuhs, S. VanRhoads, A. Casale, B. McNaughton, and D. Touretzky. Influence of path integration versus environmental orientation on place cell remapping between visually identical environments. Journal of Neurophysiology, 94(4):2603-2613, 2005. [ bib ] |
[586] | M. C. Fuhs, A. D. Redish, and D. S. Touretzky. A visually driven hippocampal place cell model. Proceedings of the Sixth Annual Conference on Computational Neuroscience : Trends in Research, 1998, pages 101-106, 1998. [ bib ] |
[587] | M. C. Fuhs and D. S. Touretzky. A spin glass model of path integration in rat medial entorhinal cortex. J Neurosci, 26(16):4266-4276, Apr 2006. [ bib | DOI | http ] |
[588] | M. Fuortes and F. Mantegazzini. Interpretation of the repetitive firing of nerve cells. J. General Physiology, 45:1163-1179, 1962. [ bib ] |
[589] | C. S. Furmanski, D. Schluppeck, and S. A. Engel. Learning strengthens the response of primary visual cortex to simple patterns. Current Biology, 14:573-578, 2004. [ bib ] |
[590] | S. Fusi. Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol. Cybern., 87:459-470, 2002. [ bib ] |
[591] | S. Fusi and L. Abbott. Limits on the memory storage capacity of bounded synapses. Nature Neuroscience, 10:485-493, 2007. [ bib ] |
[592] | S. Fusi, M. Annunziato, D. Badoni, A. Salamon, and D.J.Amit. Spike-driven synaptic plasticity: theory, simulation, vlsi implementation. Neural Computation, 12:2227-2258, 2000. [ bib ] |
[593] | S. Fusi, P. Drew, and L. Abbott. Cascade models of synaptically stored memories. Neuron, 45:599-611, 2005. [ bib ] |
[594] | S. Fusi, P. Drew, and L. Abbott. Cascade Models of Synaptically Stored Memories. Neuron, 45(4):599-611, 2005. [ bib ] |
[595] | S. Fusi and M. Mattia. Collective behavior of networks with linear (vlsi) integrate and fire neurons. Neural Computation, 11:633-652, 1999. [ bib ] |
[596] | M. Fyhn, T. Hafting, A. Treves, M. Moser, and E. Moser. Hippocampal remapping and grid realignment in entorhinal cortex. Nature, 446(7132):190-4, 2007. [ bib ] |
[597] | M. Fyhn, S. Molden, M. P. Witter, E. I. Moser, and M.-B. Moser. Spatial representation in the entorhinal cortex. Science, 305(5688):1258-1264, Aug. 2004. [ bib ] |
[598] | P. Földiàk. Learning invariance from transformation sequences. Neural Computation, 3:194-200, 1991. [ bib ] |
[599] | P. Földiàk. Adaptive network for optimal linear feature extraction. In Proceedings of the IEEE/INNS International Joint Conference on Neural Networks, pages 401-405, New York, 1989. IEEE Press. [ bib ] |
[600] | G. Buracas, A. Zador, M. DeWeese, and T. D. Albright. Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron, 20:959-969, 1998. [ bib ] |
[601] | F. Gabbiani and C. Koch. Principles of spike train analysis. In C. Koch and I. Segev, editors, Methods in Neuronal Modeling, chapter 9, pages 312-360. MIT press, 2nd edition, 1998. [ bib ] |
[602] | F. Gabbiani and C. Koch. Coding of time-varying signals in spike trains of integrate-and-fire neurons with random threshold. Neural Computation, 8:44-66, 1996. [ bib ] |
[603] | A. Gabrielov. Abelian avalanches and tutte polynomials. Physica A, 195:253-274, 1993. [ bib ] |
[604] | A. Gabrielov, W. I. Newman, and L. Knopoff. Lattice models of failure: Sensitivity to the local dynamics. Phys. Rev. E, 50:188-197, 1994. [ bib ] |
[605] | M. Galarreta and S. Hestrin. Electrical and chemical synapses among parvalbumin fast-spiking gabaergic interneurons in adult mouse neocortex. PNAS, 99:12438-12443, 2004. [ bib ] |
[606] | J. L. Gallant, C. E. Connor, S. Rakshit, J. W. Lewis, and D. C. V. Essen. Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. Journal of Neurophysiology, 76(4):2718-2739, Oct. 1996. [ bib ] |
[607] | E. Gamble and C. Koch. The dynamics of free calcium in dendritic spines in response to repetitive synaptic input. Science, 236:1311-1315, 1987. [ bib ] |
[608] | L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni. Stochastic resonance. Rev Mod Phys, 70:223-287, 1998. [ bib ] |
[609] | L. Gammaitoni, F. Marchesoni, and S. Santucci. Stochastic resonance as a bona fide resonance. Physical Review Letters, 74:1052-1055, 1995. [ bib ] |
[610] | C. W. Gardiner. Handbook of stochastic methods for physics, chemistry and the natural sciences. Springer-Verlag, Berlin & New York, 2nd edition, 1985. [ bib ] |
[611] | E. Gardner. The space of interactions in neural network models. J. Phys. A, 21:257-270, 1988. [ bib ] |
[612] | L. Garrido. Statistical Mechanics of Neural Networks. Springer, Berlin, 1990. [ bib ] |
[613] | S. Gasparini, M. Migliore, and J. C. Magee. On the initiation and propagation of dendritic spikes in ca1 pyramidal neurons. J Neurosci, 24(49):11046-11056, 2004. [ bib | DOI ] |
[614] | R. Gaudoin, W. Gerstner, and J. L. van Hemmen. Multiple synfire chains in simultaneous action. In N. Elsner and R. Menzel, editors, Goettingen Neurobiology Report 1995; Proceedings of the 23rd Goettingen Neurobiology Conference 1995, volume 2, page 898. Georg Thieme Verlag, 1995. [ bib ] |
[615] | P. Gaussier, A. Revel, J. Banquet, and V. Babeau. From view cells and place cells to cognitive map learning: processing stages of hippocampal systems. Biol. Cybern., 86:15-28, 2002. [ bib ] |
[616] | V. V. Gavrilov, S. I. Wiener, and A. Berthoz. Discharge correlates of hippocampal complex spike neurons in behaving rats passively displaced on a mobile robot. Hippocampus, 8(5):475-490, 1998. [ bib ] |
[617] | T. Gawne and B. Richmond. How independent are the messages carried by adjacent inferior temporal cortical neurons? J. Neuroscience, 13:2758 - 2771, 1993. [ bib ] |
[618] | T. J. Gawne, B. J. Richmond, and L. M. Optican. Interactive effects among several stimulus paramters on the response of striate cortical complex cells. J. Neurophysiology, 66(2):379-389, 1991. [ bib ] |
[619] | C. Geisler and J. Goldberg. A stochastic model of repetitive activity of neurons. Biophys. J., 6:53-69, 1966. [ bib ] |
[620] | G. Genaro and W. Schmidek. Exploratory activity of rats in three different environments. Ethology, 106(9):849-859, 2000. [ bib ] |
[621] | P. Georges-Francois, E. T. Rolls, and R. G. Robertson. Spatial view cells in the primate hippocampus: allocentric view not head direction or eye position or place. Cerebral Cortex, 9(3):197-212, Apr. 1999. [ bib ] |
[622] | A. P. Georgopoulos, A. Schwartz, and R. E. Kettner. Neuronal population coding of movement direction. Science, 233:1416-1419, 1986. [ bib ] |
[623] | G. L. Gerstein, P. Bedenbrough, and A. M. J. H. Aertsen. Neuronal assemblies. IEEE Trans. on Biomed. Engineering, 36:4-14, 1989. [ bib ] |
[624] | G. L. Gerstein, M. J. Bloom, I. E. Espinosa, S. Evanczuk, and M. R. Turner. Design of a laboratory for multineuron studies. IEEE Trans. Syst. Man. Cybern., SMC-13:668-676, 1983. [ bib ] |
[625] | G. L. Gerstein and D. H. Perkel. Mutual temporal relations among neuronal spike trains. Biophys. J., 12:453-473, 1972. [ bib ] |
[626] | W. Gerstner. Associative memory in a network of 'biological' neurons. In R. P. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, pages 84-90, San Mateo CA, 1991. Morgan Kaufmann Publishers. [ bib ] |
[627] | W. Gerstner. Spiking neurons. In W. Maass and C. M. Bishop, editors, Pulsed Neural Networks, chapter 1, pages 3-53. MIT-Press, 1998. [ bib ] |
[628] | W. Gerstner. Populations of spiking neurons. In W. Maass and C. M. Bishop, editors, Pulsed Neural Networks, chapter 10, pages 261-295. MIT-Press, 1998. [ bib ] |
[629] | W. Gerstner. A framework for spiking neuron models - the spike response model. In F. Moss and S. Gielen, editors, Handbook of Biological Physics, volume 4, chapter 12, pages 469-516. Elsevier, Amsterdam, 2001. [ bib ] |
[630] | W. Gerstner. Coding properties of spiking neurons: reverse- and cross-correlations. Neural Networks, 14:599-610, 2001. [ bib ] |
[631] | W. Gerstner. Coding properties of spiking neurons: reverse and cross-correlations. Neural Networks, 14(6-7):599-610, 2001. [ bib ] |
[632] | W. Gerstner. Population dynamics of spiking neurons: fast transients, asynchronous states and locking. Neural Computation, 12:43-89, 2000. [ bib ] |
[633] | W. Gerstner. Population dynamics for spiking neurons: fast transients, asynchronous states and locking. Neural Computation, to appear, 12:43-89, 2000. [ bib ] |
[634] | W. Gerstner. Population dynamics of spiking neurons: fast transients, asynchronous states and locking. Neural Computation, 12:43-89, 2000. [ bib ] |
[635] | W. Gerstner. Rapid signal transmission by a population of spiking neurons. In ICANN'99 Artificial Neural Networks, volume 470, pages 7-12. IEE Conference Publication, 1999. [ bib ] |
[636] | W. Gerstner. Rapid phase locking in systems of pulse-coupled oscillators with delays. Phys. Rev. Lett., 76:1755-1758, 1996. [ bib ] |
[637] | W. Gerstner. Time structure of the activity in neural network models. Phys. Rev. E, 51(1):738-758, 1995. [ bib ] |
[638] | W. Gerstner. A framework for spiking model neurons: The spike response method. preprint, TU-Muenchen, xx:xx, 1995. [ bib ] |
[639] | W. Gerstner. Kodierung und Signalübertragung in Neuronalen Systemen: Assoziative Netzwerke mit stochastisch feuernden Neuronen, volume 15 of Reihe Physik. Harri-Deutsch Verlag, Frankfurt/Main, 1993. Dissertation Nov. 1992, TU München. [ bib ] |
[640] | W. Gerstner and L. F. Abbott. Learning navigational maps through potentiation and modulation of hippocampal place cells. Journal of Comput. Neurosci., 4:79-94, 1997. [ bib ] |
[641] | W. Gerstner and J. L. van Hemmen. How to describe neural activity - spikes, rates, or assemblies? In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems 6, pages 463-470. Morgan Kaufmann Publishers, San Francisco, CA, 1994. [ bib ] |
[642] | W. Gerstner and J. L. van Hemmen. Coding and information processing in neural networks. In E. Domany, J. L. van Hemmen, and K. Schulten, editors, Models of neural networks II, pages 1-93, New York, 1994. Springer-Verlag. [ bib ] |
[643] | W. Gerstner and J. L. van Hemmen. Spikes or rates? - stationary, oscillatory, and spatio-temporal states in an associative network of spiking neurons. In S. Gielen and B. Kappen, editors, ICANN'93, Proceedings of the International Conference on Artificial Neural Networks, Amsterdam, 13-16 September 1993, pages 633-638. Springer-Verlag, London, 1993. [ bib ] |
[644] | W. Gerstner and J. L. van Hemmen. Coherence and incoherence in a globally coupled ensemble of pulse emitting units. Phys. Rev. Lett., 71(3):312-315, 1993. [ bib ] |
[645] | W. Gerstner and J. L. van Hemmen. Associative memory in a network of `spiking' neurons. Network, 3:139-164, 1992. [ bib ] |
[646] | W. Gerstner and J. L. van Hemmen. Universality in neural networks: The importance of the mean firing rate. Biol. Cybern., 67:195-205, 1992. [ bib ] |
[647] | W. Gerstner, J. L. van Hemmen, and J. D. Cowan. What matters in neuronal locking. Neural Comput., 8:1653-1676, 1996. [ bib ] |
[648] | W. Gerstner, R. Kempter, J. van Hemmen, and H. Wagner. A developmental learning rule for coincidence tuning in the barn owl auditory system. In J. Bower, editor, Computational Neuroscience: trends in research 1997, pages 665-669. Plenum Press, New York, 1997. [ bib ] |
[649] | W. Gerstner, R. Kempter, J. van Hemmen, and H. Wagner. A neuronal learning rule for sub-millisecond temporal coding. Nature, 383(6595):76-78, 1996. [ bib ] |
[650] | W. Gerstner, R. Kempter, and J. L. van Hemmen. Hebbian learning of pulse timing in the barn owl auditory system. In W. Maass and C. M. Bishop, editors, Pulsed Neural Networks, chapter 14, pages 353-377. MIT-Press, 1998. [ bib ] |
[651] | W. Gerstner, R. Kempter, J. L. van Hemmen, and H. Wagner. A neuronal learning rule for sub-millisecond temporal coding. Nature, 383:76-78, 1996. [ bib ] |
[652] | W. Gerstner and W. Kistler. Spiking neuron models. Cambridge University Press New York, 2002. [ bib ] |
[653] | W. Gerstner and W. Kistler. Mathematical formulations of Hebbian learning. Biological Cybernetics, 87(5):404-415, 2002. [ bib ] |
[654] | W. Gerstner and W. K. Kistler. Spiking Neuron Models. Cambridge University Press, Cambridge UK, 2002. [ bib ] |
[655] | W. Gerstner and W. K. Kistler. Mathematical formulations of hebbian learning. Biological Cybernetics, 87:404-415, 2002. [ bib ] |
[656] | W. Gerstner and W. M. Kistler. Spiking neuron models : single neurons, populations, plasticity. Cambridge University Press, Cambridge, U.K., 2002. [ bib | .html ] |
[657] | W. Gerstner, A. Kreiter, H. Markram, and A. Herz. Neural codes: firing rates and beyond. Proc. Natl. Acad. Sci. USA, 94:12740-12741, 1997. [ bib ] |
[658] | W. Gerstner, R. Ritz, and J. L. van Hemmen. A biologically motivated and analytically soluble model of collective oscillations in the cortex: I. theory of weak locking. Biol. Cybern., 68:363-374, 1993. [ bib ] |
[659] | W. Gerstner, R. Ritz, and J. L. van Hemmen. Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biol. Cybern., 69:503-515, 1993. [ bib ] |
[660] | W. Gerstner, A. Schiegg, R. Ritz, and J. L. van Hemmen. Long term potentiation in dendritic spines: a model study. In N. Elsner and R. Menzel, editors, Goettingen Neurobiology Report 1995; Proceedings of the 23rd Goettingen Neurobiology Conference 1995, volume 1, page 121. Georg Thieme Verlag, 1995. [ bib ] |
[661] | G. Gestri. Dynamics of a model for the variability of the interspike intervals in a retinal neuron. Biological Cybernetics, 31:97-98, 1978. [ bib ] |
[662] | T. Geszti. Physical Models of Neural Networks. World Scientific, Singapore, 1990. [ bib ] |
[663] | G. M. Ghose. Learning in mammalian sensory cortex. Current Opinion in Neurobiology, 14:513-518, 2004. [ bib ] |
[664] | G. M. Ghose and J. H. R. Maunsell. Perceptual learning can selectively alter neural responses in primate v1. Society for Neuroscience Abstracts, 23:1544, 1997. [ bib ] |
[665] | G. M. Ghose, T. Yang, and J. H. R. Maunsell. Physiological correlates of perceptual learning in monkey v1 and v2. The Journal of Neurophysiology, 87:1867-1888, 2002. [ bib ] |
[666] | E. J. Gibson. Perceptual learning. Annual Reviews in Psychology, 14:29-56, 1963. [ bib ] |
[667] | J. Gibson, M. Beierlein, and B. Connors. Functional properties of electrical synapses between inhibitory interneurons of neocortical layer 4. J. Neurophysiol., 93:467-480, 2005. [ bib ] |
[668] | C. D. Gilbert. Adult cortical dynamics. Physiological Reviews, 78:467-485, 1998. [ bib ] |
[669] | C. D. Gilbert, M. Sigman, and R. E. Crist. The neural basis of perceptual learning. Neuron, 31:681-697, 2001. [ bib ] |
[670] | A. Gillies and G. Arbuthnott. Computational models of the basal ganglia. Movement Disorders, 15(5):762-770, 2000. [ bib | http ] |
[671] | S. Gillner and H. Mallot. Navigation and acquisition of spatial knowledge in a virtual maze. J. Cognitive Neurosciencie, 10:445-463, 1998. [ bib ] |
[672] | V. Giorno, A. G. Nobile, and L. M.Ricciardi. Instantaneous return processes and neuronal firings. In R. Trappl, editor, Cybernetics and Systems Research, Vol 1., pages 829-236. World Scientific Press, 1992. [ bib ] |
[673] | L. Glass and M. Mackey. A simple model for phase locking in biological oscillators. J. Mathematical Biol., 7:339-352, 1979. [ bib ] |
[674] | M. Gluck, M. Meeter, and C. Myers. Computational models of the hippocampal region: linking incremental learning and episodic memory. TRENDS in Cognitive Sciences, 7(6), 2003. [ bib ] |
[675] | B. Gluss. A model of neuron firing with exponential decay of potential resulting in diffusion equations for the probability density. Bull. Math. Biophysics, 29:233-243, 1967. [ bib ] |
[676] | J. Gold, P. J. Bennett, and A. B. Sekuler. Signal but not noise changes with perceptual learning. Nature, 402:176-178, 1999. [ bib ] |
[677] | J. I. Gold and M. F. Bear. A model of dendritic spike Ca2+ concentration exploring possible basis for sliding synaptic modification threshold. Proc. Natl. Acad. Sci. USA, 91:3941-3945, 1994. [ bib ] |
[678] | J. Goldberg, H. Adrian, and F. Smith. Response of neurons of the superior olivary complex of cat to acoustic stimuli of long duration. J. Neurophysiology, 27:706-749, 1964. [ bib ] |
[679] | J. Goldberg, K. Holthoff, and R. Yuste. A problem with hebb and local spikes. Trends in Neurosciences, 25:433-435, 2002. [ bib ] |
[680] | J. M. Goldberg and P. B. Brown. Response of binaural neurons of dog Superior Olivary Complex to dichotic tonal stimuli: Some physiological mechanisms of sound localization. J. Neurophysiol., 32:613-636, 1969. [ bib ] |
[681] | N. L. Golding, N. P. Staff, and N. Spruston. Dendritic spikes as a mechanism for cooperative long-term potentiation. Nature, 418(6895):326-331, 2002. [ bib | DOI ] |
[682] | M. S. Goldman, J. Golowasch, E. Marder, and L. F. Abbott. Global structure, robustness, and modulation of neuronal models. J Neurosci, 21(14):5229-5238, 2001. [ bib ] |
[683] | M. S. Goldman, J. H. Levine, G. Major, D. W. Tank, and H. S. Seung. Robust persistent neural activity in a model integrator with multiple hysteretic dendrites per neuron. Cereb Cortex, 13(11):1185-1195, 2003. [ bib ] |
[684] | E. Goles and J. Olivos. Comportement périodique des fonctions à seuil binaires et applications. Discr. Appl. Math., 3:93-105, 1981. [ bib ] |
[685] | E. Goles and Y. Vichniac. Lyapunov functions for parallel neural networks. In J. S. Denker, editor, Neural networks for computing, pages 165-181, New York, 1986. American Institute of Physics. [ bib ] |
[686] | E. J. Golob and J. S. Taube. Head direction cells and episodic spatial information in rats without a hippocampus. Proceedings of the National Academy of Sciences of the United States of America, 94(14):7645-7650, July 1997. [ bib ] |
[687] | D. Golomb and G. Ermentrout. Slow excitation supports propagation of slow pulses in networks of excitatory and inhibitory populations. Phys. Rev. E, 65:061911, 2002. [ bib ] |
[688] | D. Golomb and G. B. Ermentrout. Bistability in pulse propagation in networks of excitatory and inhibitory populations. Physical Review Letters, 86:4179-4182, 2001. [ bib ] |
[689] | D. Golomb, D. Hansel, and G. Mato. Mechanisms of synchrony of neural activity in large networks, pages 887-968. Elsevier Science, 2001. [ bib ] |
[690] | D. Golomb, D. Hansel, B. Shraiman, and H. Sompolinsky. Clustering in globally coupled phase oscillators. Phys. Rev. A, 45:3516-3530, 1992. [ bib ] |
[691] | D. Golomb and J. Rinzel. Clustering in globally coupled inhibitory neurons. Physica D, 72:259-282, 1994. [ bib ] |
[692] | F. Gomez, J. Schmidhuber, and R. Miikkulainen. Efficient non-linear control through neuroevolution. In ECML 2006: Proceedings of the 17th European Conference on Machine Learning. Springer, 2006. [ bib ] |
[693] | L. V. Gool, T. Moons, E. Pauwels, and A. Oosterlinck. Vision and lie's approach to invariance. Image and Vision Computing, 13(4):259-277, 1995. [ bib ] |
[694] | U. Gordon, A. Polsky, and J. Schiller. Plasticity compartments in basal dendrites of neocortical pyramidal neurons. J. Neurosci., 26(49):12717-12726, Dec. 2006. [ bib | http ] |
[695] | K. Gothard, W. Skaggs, and B. McNaughton. Dynamics of mismatch correction in the hippocampal ensemble code for space: Interaction between path integration and environmental cues. Journal of Neuroscience, 16:8027-8040, 1996. [ bib ] |
[696] | K. M. Gothard, W. E. Skaggs, and B. L. McNaughton. Dynamics of mismatch correction in the hippocampal ensemble code for space: interaction between path integration and environmental cues. Journal of Neuroscience, 16(24):8027-8040, Dec. 1996. [ bib ] |
[697] | K. M. Gothard, W. E. Skaggs, K. M. Moore, and B. L. McNaughton. Binding of hippocampal CA1 neural activity to multiple reference frames in a landmark-based navigation task. Journal of Neuroscience, 16(2):823-835, Jan. 1996. [ bib ] |
[698] | M. Graupner and N. Brunel. Stdp in a bistable synapse model based on CaMKII and associate signaling pathways. PLOS Comput. Biol., 3:e 221 doi:10.1371/journal.pcbi.0030221, 2007. [ bib ] |
[699] | C. Gray. Synchronous oscillations in neuronal systems: mechanisms and functions. Comput. Neurosci., 1(1-2):11-38, 1994. [ bib ] |
[700] | C. M. Gray, P. König, A. K. Engel, and W. Singer. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, 338:334-337, 1989. [ bib ] |
[701] | C. M. Gray and W. Singer. Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl. Acad. Sci. USA, 86:1698-1702, 1989. [ bib ] |
[702] | D. M. Green and J. A. Swets. Signal Detection Theory and Psychophysics. New York: John Wiley, Inc., 1996. [ bib ] |
[703] | D. Grensing and R. Kühn. Random site spin glass models. J. Phys. A, 19:L1153-L1157, 1986. [ bib ] |
[704] | S. Grillner, J. Hellgren, A. Ménard, L. Saitoh, and M. Wigström. Mechanisms for selection of basic motor programs - roles of the striatum and pallidum. Trends in Neurosciences, 28:364-370, 2005. [ bib ] |
[705] | S. Grossberg. The Adaptive Brain I. Elsevier, 1987. [ bib ] |
[706] | S. Grossberg. How does the brain build a cognitive code. Psychological Review, 87:1-57, 1980. reprinted in Anderson and Rosenfeld, 1990. [ bib ] |
[707] | S. Grossberg. Adaptive pattern classification and universal recoding i: Parallel development and coding of neuronal feature detectors. Biol. Cybern., 23:121-134, 1976. reprinted in Anderson and Rosenfeld, 1990. [ bib ] |
[708] | S. Grossberg. Contour enhancement, short term memory and constancies in reverberating neural networks. Studies in Applied Mathematics, 52:217-257, 1973. [ bib ] |
[709] | S. Grossberg and J. Merrill. The hippocampus and cerebellum in adaptively timed learning, recognition, and movement. J. Cogn. Neuroscience, 8:257-277, 1996. [ bib ] |
[710] | S. Grossberg and J. Merrill. A neural network model of adaptively timed reinforcement learning and hippocampal dynamics. Cogn. Brain Research, 1:3-38, 1992. [ bib ] |
[711] | S. Grossberg and E. Mingolla. Neural dynamics of perceptural grouping: texture boundaries and emergent segmentations. Percept. Psychophys., 38:141-171, 198. [ bib ] |
[712] | A. Guazzelli, B. Bota, and M. A. Arbib. Competitive hebbian learning and the hippocampal place cell system: Modeling the interaction of visual and path integration cues. Hippocampus, 11(3):216-239, 2001. [ bib ] |
[713] | J. Guckenheimer and P. Holmes. Nonlinear oscillations, dynamical systems, and bifurcations of vector fields. Springer Verlag, New York, 1983. [ bib ] |
[714] | A. T. Gulledge and G. J. Stuart. Action potential initiation and propagation in layer 5 pyramidal neurons of the rat prefrontal cortex: absence of dopamine modulation. J Neurosci, 23(36):11363-11372, 2003. [ bib ] |
[715] | A. Gupta, Y. Wang, and H. Markram. Organizing principles for a diversity of gabaergic interneurons and synapses in the neocortex. Science, 287:273-278, 2000. [ bib ] |
[716] | B. Gustafsson, H. Wigstrom, W. C. Abraham, and Y.-Y. Huang. Long-term potentiation in the hippocampus using depolarizing current pulses as the conditioning stimulus. J. Neurosci., 7:774-780, 1987. [ bib ] |
[717] | B. Gutenberg and C. F. Richter. xx. Ann. di Geofis., 9:1, 1956. [ bib ] |
[718] | Y. Gutfreund, W. Zheng, and E. Knudsen. Gated visual input to the central auditory system. Science, 297:1556-1559, 2002. [ bib ] |
[719] | R. Gutig and H. Sompolinsky. The tempotron: a neuron that learns spike timing-based decisions. Nat Neurosci, 9(3):420-428, Mar. 2006. [ bib | http ] |
[720] | B. Gutkin and G. B. Ermentrout. Dynamics of membrane excitability determine inter-spike interval variability: a link between spike generation mechansim and cortical spike train statistics. Neural Computation, 10:1047-1065, 1998. [ bib ] |
[721] | B. S. Gutkin, G. B. Ermentrout, and A. D. Reyes. Phase-response curves give the responses of neurons to transient inputs. J. Neurophysiology, 94:1623-1635, 2005. [ bib ] |
[722] | I. Guyon and P. S. P. Wang. Advances in Pattern Recognition Systems using Neural Networks. World Scientific, Singapore, 1993. [ bib ] |
[723] | R. Guyonneau, R. VanRullen, and S. Thorpe. Neurons tune to the earliest spikes through stdp. Neural Computation, 17(4):859-879, 2005. [ bib ] |
[724] | R. Gütig, R. Aharonov, S. Rotter, and H. Sompolinsky. Learning input correlations through non-linear temporally asymmetric hebbian plasticity. J. Neuroscience, 23:3697-3714, 2003. [ bib ] |
[725] | R. Gütig, S. Aharonov, S. Rotter, and H. Sompolinsky. Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. Journal of Neuroscience, 23(9):3697-3714, 2003. [ bib ] |
[726] | M. H. H. Dedieu, M.P. Kennedy. Chaos shift keying: modulation and demodulation of a chaotic carrier using self-synchronizing chua's circuits. IEEE Trans. Circ. Syst., Part II, 40:636-642, 1993. [ bib ] |
[727] | H.Abarbanel, M.Rabinovitch, A.Selverston, M.Bazhenov, R.Huerta, M.Sushchik, and L.Rubinskii. Synchronisation in neural networks. Uspekhi Fizicheskikh Nauk, 166:363-390, 1996. Russian Journal, but article in English. [ bib ] |
[728] | H.Fujisaka and T.Yamada. Stability theory of synchronized motion in coupled oscillator systems. Progr. Theor. Phys., 69:32, 1983. [ bib ] |
[729] | Hüning, Glünder, and G. Palm. Synaptic delay learning in pulse-coupled neurons. Neural Computation, 10:555-565, 1998. [ bib ] |
[730] | T. Hafting, M. Fyhn, S. Molden, M. Moser, and E. I. Moser. Microstructure of a spatial map in the entorhinal cortex. Nature, 436(7052):801-806, Aug. 2005. [ bib | http ] |
[731] | R. H. Hahnloser. Emergence of neural integration in the head-direction system by visual supervision. Neuroscience, 120:877-891, 2003. [ bib ] |
[732] | H. Haken. Neural and Synergetic Computers. Springer, Berlin, 1988. [ bib ] |
[733] | H. Haken. Synergetics, An Introduction. Springer Series in Synergetics. Springer, 1983. [ bib ] |
[734] | J. K. Hale and H. Koçac. Dynamics and Bifurcations. Number 3 in Text in Applied Mathematics. Springer, Berlin, 1991. [ bib ] |
[735] | B. C. Hall. Lie Groups, Lie Algebras, and Representations. Springer, 2003. [ bib ] |
[736] | X. Han and E. Boyden. Multiple-color optical activation, silencing, and desynchronization of neural activity, with single-spike temporal resolution. PLoS ONE, 2(3):e299, 2007. [ bib ] |
[737] | Y. Han and H. S. Colburn. Point-neuron model for binaural interaction in MSO. Hear. Res., 68:115-130, 1993. [ bib ] |
[738] | A. Hanazawa and H. Komatsu. Influence of the direction of elemental luminance gradients on the responses of V4 cells to textured surfaces. Journal of Neuroscience, 21(12):4490-4497, June 2001. [ bib ] |
[739] | D. Hansel and G. Mato. Asynchronous states and the emergence of synchrony in large networks of interacting excitatory and inhibitory. Neural Computation, 15:1-56, 2003. [ bib ] |
[740] | D. Hansel and G. Mato. Existence and stability of persistent states in large neuronal networks. Phys. Rev. Letters, 86:4175-4178, 2001. [ bib ] |
[741] | D. Hansel, G. Mato, and C. Meunier. Synchrony in excitatory neural networks. Neural Comput., 7:307-337, 1995. [ bib ] |
[742] | D. Hansel and H. Sompolinski. Synchronization and computation in a chaotic neural network. Phys. Rev. Lett., 68:718-721, 1992. [ bib ] |
[743] | D. Hansel and H. Sompolinsky. Chaos and synchrony in a model of a hypercolumn in visual cortex. J. Comput. Neurosci., 3:7-34, 1996. [ bib ] |
[744] | S. J. Hanson. Advances in Neural Information Processing Systems, volume 5. Morgan Kaufmann Publishers, San Mateo, 1993. [ bib ] |
[745] | E. Hargreaves, G. Rao, I. Lee, and J. Knierim. Major dissociation between medial and lateral entorhinal input to dorsal hippocampus. Science, 308:1792-1794, 2005. [ bib ] |
[746] | S. Harmeling, A. Ziehe, M. Kawanabe, and K.-R. Müller. Kernel-based nonlinear blind source separation. Neural Computation, 15:1089-1124, 2003. [ bib ] |
[747] | K. Harris. Stability of the fittest: Organizing learning through retroaxonal signals. Trends in Neurosciences, pages 130-136, 2008. [ bib ] |
[748] | K. D. Harris. Stability of the fittest: organizing learning through retroaxonal signals. Trends in Neurosciences, 31(3):130-136, Mar. 2008. [ bib | http ] |
[749] | T. Hartley and N. Burgess. Complementary memory systems: competition, cooperation and compensation. Trends n Neurosciences, 28(4):169-170, 2005. [ bib ] |
[750] | H. K. Hartline. Rhe receptive fields of optic nerve fibers. Am. J. Physiol., 130:690-699, 1940. [ bib ] |
[751] | G. Hartmann and R. Wehner. The ant's path integration system: a neural architecture. Biol. Cybern., 73:483-497, 1995. [ bib ] |
[752] | C. D. Harvey and K. Svoboda. Locally dynamic synaptic learning rules in pyramidal neuron dendrites. Nature, 450(7173):1195-1200, Dec 2007. [ bib | DOI | http ] |
[753] | W. Hashimoto. Quadratic forms in natural images. Network: Computation in Neural Systems, 14(4):765-788, 2003. [ bib ] |
[754] | M. Hasler. Engineering chaos for encryption and broadband communication. Philosophical Transactions of the Royal Society of London - A, 353:115-126, 95. [ bib ] |
[755] | M. Hasler and Y. Maistrenko. An introduction to the synchronization of chaotic systems : coupled skew tent maps. CAS Transactions, part I, special issue on Chaos, Synchronization, Control and Applications, 44:856-866, 1997. [ bib ] |
[756] | M. Hausser and B. Mel. Dendrites: bug or feature? Curr Opin Neurobiol, 13(3):372-383, 2003. [ bib ] |
[757] | A. G. Hawkes. Spectra of some self-exciting and mutually exciting processes. Biometrika, 58:83-90, 1971. [ bib ] |
[758] | A. Hayer and U. S. Bhalla. Molecular switches at the synapse emerge from receptor and kinase traffic. Plos Computational Biology, 1(2):e20, 2005. [ bib ] |
[759] | A. Hayes, A. Martinoli, and R. Goodman. warm robotic odor localization: Off-line optimization and validation with real robots. Robotica, 21:427-441, 2003. [ bib ] |
[760] | S. Haykin. Neural Networks. Prentice Hall, Upper Saddle River, NJ, 1994. [ bib ] |
[761] | D. O. Hebb. The Organization of Behavior. Wiley, New York, 1949. [ bib ] |
[762] | R. M. Hecht and N. Tishby. Extraction of relevant speech features using the information bottleneck method. In Proceedings of InterSpeech, 2005. [ bib ] |
[763] | D. Heck. Rat cerebellar cortex in vitro responds specifically to moving stimuli. Neuroscience Letters, 157:95-98, 1993. [ bib ] |
[764] | D. H. Heeger, E. Simoncelli, and J. Movshon. Computational models of cortical visual processing. Proc. National Academy of Sci. USA, 93:623-627, 1996. [ bib ] |
[765] | J. Hegde and D. C. V. Essen. Strategies of shape representation in macaque visual area V2. Visual Neuroscience, 20(3):313-328, May 2003. [ bib ] |
[766] | J. Hegde and D. C. V. Essen. Selectivity for complex shapes in primate visual area V2. Journal of Neuroscience, 20(5):RC61:1-6, Mar. 2000. [ bib ] |
[767] | P. Heggelund and K. Albus. Response variability and orientation discrimination of single cells in striate cortex of cat. Experimental Brain Research, 32:197-211, 1978. [ bib ] |
[768] | W. Heiligenberg. Neural Nets in Electric Fish. MIT Press, Cambridge, 1991. [ bib ] |
[769] | F. Helmchen, K. Svoboda, W. Denk, and D. W. Tank. In vivo dendritic calcium dynamics in deep-layer cortical pyramidal neurons. Nature Neuroscience, 2:989 - 996, 1999. [ bib ] |
[770] | J. van Hemmen and W. Senn. Hebb in perspective. Biol. Cybernetics, 87:5-6, 2002. [ bib ] |
[771] | J. L. van Hemmen. Hebbian learning and unlearning. In Neural networks and spin glasses, pages 91-114, Singapore, 1990. World Scientific. [ bib ] |
[772] | J. L. van Hemmen. Nonlinear neural networks near stauration. Phys. Rev. A, 36:1959-1962, 1988. [ bib ] |
[773] | J. L. van Hemmen, W. Gerstner, A. V. M. Herz, R. Kühn, B. Sulzer, and M. Vaas. Encoding and decoding of patterns which are correlated in space and time. In G. Dorffner, editor, Konnektionismus in Artificial Intelligence und Kognitionsforschung, pages 153-162, Berlin Heidelberg New York, 1990. Springer. [ bib ] |
[774] | J. L. van Hemmen, W. Gerstner, and R. Ritz. A `microscopic' model of collective oscillations in the cortex. In J. G. Taylor, E. K. Caianiello, R. N. J. Cotterell, and J. W. Clark, editors, Neural network dynamics., pages 250-257, Berlin Heidelberg New York, 1992. Springer. [ bib ] |
[775] | J. L. van Hemmen, D. Grensing, A. Huber, and R. Kühn. Nonlinear neural networks i and ii. J. Stat. Phys., 50:231-257 and 259-293, 1988. [ bib ] |
[776] | J. L. van Hemmen, D. Grensing, A. Huber, and R. Kühn. Elementary solution of classical spin glass models. Z. Phys. B, 65:53-63, 1986. [ bib ] |
[777] | J. L. van Hemmen, L. B. Ioffe, R. Kühn, and M. Vaas. Increasing the efficiency of a neural network through unlearning. Physica A, 163:386-392, 1990. [ bib ] |
[778] | J. L. van Hemmen and R. Kühn. Collective phenomena in neural networks. In E. Domany, J. L. van Hemmen, and K.Schulten, editors, Models of neural networks., Berlin Heidelberg New York, 1991. Springer. [ bib ] |
[779] | J. L. van Hemmen and R. Kühn. Nonlinear neural networks. Phys. Rev. Lett., 57:913-916, 1986. [ bib ] |
[780] | J. L. van Hemmen and R. Ritz. Neural coding: A theoretical vista of mechanisms, techniques, and applications. In S. Andersson, editor, Analysis of dynamical and cognitive systems., pages 75-119, Berlin, Heidelberg, New York, 1995. Springer. [ bib ] |
[781] | J. L. van Hemmen and W. F. Wreszinski. Lyapunov function for the Kuramoto model of nonlinearly coupled oscillators. J. Stat. Phys., 72:145-166, 1993. [ bib ] |
[782] | D. A. Henze, N. N. Urban, and G. Barrionuevo. The multifarious hippocampal mossy fiber pathway: A review. Neuroscience, 98(3):407-427, 2000. [ bib ] |
[783] | D. Henze and G. Buzsáki. Action potential threshold of hippocampal pyramidal cells in vivo is increased by recent spiking activity. Neuroscience, 105(1):121-130, 2001. [ bib ] |
[784] | A. Herrmann and W. Gerstner. Noise and the psth response to current transients: I. General theory and application to the integrate-and-fire neuron. J. Computational Neuroscience, 11:135-151, 2001. [ bib ] |
[785] | A. Herrmann and W. Gerstner. Noise and the psth response to current transients II. Iintegrate-and-fire model with slow recovery and application to motoneuron data. J. Computational Neuroscience, 12:83-95, 2001. [ bib ] |
[786] | A. Herrmann and W. Gerstner. Effect of noise on neuron transient response. Neurocomputing, 32-33:147-154, 2000. [ bib ] |
[787] | A. Herrmann and W. Gerstner. Understanding the psth response to synaptic input. In ICANN'99 Artificial Neural Networks, volume 470, pages 1012-1017. IEE Conference Publication, 1999. [ bib ] |
[788] | M. Herrmann, J. A. Hertz, and A. Prügel-Bennett. Mean field analysis of synfire chains. Network, 6:403-414, 1995. [ bib ] |
[789] | J. Hertz, A. Krogh, and R. G. Palmer. Introduction to the Theory of Neural Computation. Addison-Wesley, Redwood City CA, 1991. [ bib ] |
[790] | J. Hertz and A. PrugelBennet. Learning short synfire chains by self-organization. Network, 7:357-363, 1996. [ bib ] |
[791] | A. V. M. Herz, T. Gollisch, C. K. Machens, and D. Jaeger. Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science, 314(5796):80-85, 2006. [ bib | DOI ] |
[792] | A. V. M. Herz and J. J. Hopfield. Earthquake cycles and neural reverberations: Collective oscillations in systems with coupled threshold elements. Phys. Rev. Lett., 75:1222-1225, 1995. [ bib ] |
[793] | A. V. M. Herz, Z. Li, and J. L. van Hemmen. Statistical mechanics of temporal association in neural networks with transmission delays. Phys. Rev. Lett., 66:1370-1373, 1991. [ bib ] |
[794] | A. V. M. Herz, B. Sulzer, R. Kühn, and J. L. van Hemmen. Hebbian learning reconsidered: Representation of static and dynamic objects in associative neural nets. Biol. Cybern., 60:457-467, 1989. [ bib ] |
[795] | A. V. M. Herz, B. Sulzer, R. Kühn, and J. L. van Hemmen. The Hebb rule: Representation of static and dynamic objects in neural nets. Europhys. Lett., 7:663-669, 1988. [ bib ] |
[796] | M. Herzog, M.-A. Esfeld, and W. Gerstner. Consciousness and the small network argument. Neural Networks, 20:1054-1056, 2007. [ bib ] |
[797] | M. Herzog and M. Fahle. Top-down information and models of learning. In M. Fahle and T. Poggio, editors, Perceptual Learning, a textbook. MIT-Press, 2002. [ bib ] |
[798] | M. Herzog and M. Fahle. Modeling perceptual learning: difficulties and how they can be overcome. Biological Cybernetics, 78:107-117, 1998. [ bib ] |
[799] | M. H. Herzog and M. Fahle. The role of feedback in learning a vernier discrimination task. Vision Research, 37(15):2133-2141, 1997. [ bib ] |
[800] | N. A. Hessler, A. M. Shirke, and R. Malinow. The probability of transmitter release at a mammalian central synapse. Nature, 366:569-572, 1993. [ bib ] |
[801] | R. von der Heydt and E. Peterhans. Mechanisms of contour perception in monkey visual cortex. I) lines of pattern discontinuity. Journal of Neuroscience, 9(5):1731-1748, 1989. [ bib ] |
[802] | R. von der Heydt and E. Peterhans. Mechanisms of contour perception in monkey visual cortex. II) contour bridging gaps. J. Neuroscience, 9:1749-1763, 1989. [ bib ] |
[803] | P. Hiesinger, R. Zhai, Y. Zhou, T. Koh, S. Mehta, K. Schulze, Y. Cao, P. Verstreken, T. Clandinin, K. Fischbach, et al. Activity-independent prespecification of synaptic partners in the visual map of drosophila. Current Biology, 16(18):1835-1843, 2006. [ bib ] |
[804] | A. Hill. Excitation and accomodation in nerve. Proc. R. Soc. B, 119:305-355, 1936. [ bib ] |
[805] | B. Hille. Ionic channels of excitable membranes. Sinauer, Sunderland, 1992. [ bib ] |
[806] | J. L. Hindmarsh and R. M. Rose. A model of neuronal bursting using 3 coupled 1st order differential-equations. Proc. R. Soc. Lond., B 221:87-102, 1984. [ bib ] |
[807] | H. Hirsch and D. Spinelli. Modification of the distribution of receptive field orientation in cats by selective visual exposure during development. Experimental Brain Research, 12(5):509-527, 1971. [ bib ] |
[808] | N. Ho and A. Destexhe. Synaptic background activity enhances the responsiveness of neocortical pyramidal neurons. Journal of Neurophysiology, 84:1488-1496, 2000. [ bib ] |
[809] | S. Hochreiter and J. Schmidhuber. Feature extraction through LOCOCODE. Neural Computation, 11(3):679-714, 1999. [ bib ] |
[810] | S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735-1780, 1997. [ bib ] |
[811] | A. L. Hodgkin. The local electric changes associated with repetitive action in a non-medullated axon. J. Physiol. (London), 107:165-181, 1948. [ bib ] |
[812] | A. L. Hodgkin and A. F. Huxley. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol, 117(4):500-544, 1952. [ bib ] |
[813] | V. Hok, P. Lenck-Santini, S. Roux, E. Save, R. Muller, and B. Poucet. Goal-related activity in hippocampal place cells. Journal of Neurosciience, 27(3):472-82, 2007. [ bib ] |
[814] | A. V. Holden. Models of the stochastc activity of neurons, volume 12 of Lecture notes in Biomothematics. Springer, Berlin Heidelberg New York, 1976. [ bib ] |
[815] | J. Hollerman and W. Schultz. Dopamine neurons report an error in the temporal prediction of reward during learning. Nature Neuroscience, 1:304-309, 1998. [ bib ] |
[816] | S. A. Hollup, S. Molden, J. G. Donnett, M. B. Moser, and E. I. Moser. Place fields of rat hippocampal pyramidal cells and spatial learning in the watermaze. European Journal of Neuroscience, 13(6):1197-1208, Mar. 2001. [ bib ] |
[817] | W. R. Holmes and W. B. Levy. Insights into associative long-term potentiation from computational models of nmda receptor-mediated calcium influx and intracellular calcium concentration changes. J. Neurophysiol., 63:1148-1168, 1990. [ bib ] |
[818] | A. Holtmaat, L. Wilbrecht, G. Knott, E. Welker, and K. Svoboda. Experience-dependent and cell-type-specific spine growth in the neocortex. Nature, 441(7096):979-983, 2006. [ bib ] |
[819] | J. Honerkamp. Stochastical Dynamical Systems. VCH, Weinheim, 1993. [ bib ] |
[820] | J. J. Hopfield. Pattern recognition computation using action potential timing for stimulus representation. Nature, 376:33-36, 1995. [ bib ] |
[821] | J. J. Hopfield. Neurons with graded response have computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. USA, 81:3088-3092, 1984. [ bib ] |
[822] | J. J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA, 79:2554-2558, 1982. [ bib ] |
[823] | J. J. Hopfield and C. D. Brody. Learning rules and network repair in spike-timing-based computation networks. Proc. Natl. Acad. Sci. USA, 101:337-342, 2004. [ bib ] |
[824] | J. J. Hopfield and A. V. M. Herz. Rapid local synchronization of action potentials: towards computation with coupled integrate-and-fire networks. Proc. Natl. Acad. Sci. USA, 92:6655, 1995. [ bib ] |
[825] | S. Hopp and W. Timberlake. Odor cue determinants of urine marking in male rats (rattus norvegicus). Behavioral Neural Biology, 37(1):162-72, 1983. [ bib ] |
[826] | F. C. Hoppensteadt and E. M. Izhikevich. Weakly connected neural networks. Springer, 1997. [ bib ] |
[827] | E. Hori, Y. Nishio, K. Kazui, K. Umeno, E. Tabuchi, K. Sasaki, S. Endo, T. Ono, and H. Nishijo. Place-related neural responses in the monkey hippocampal formation in a virtual space. Hippocampus, 15(8):991-996, 2005. [ bib ] |
[828] | D. Horn, N. Levu, I. Meilijison, and E. Ruppin. Distributed synchrony of spiking neurons in a hebbian cell assembly. In Advances in Neural Information Processing 12, volume 12, page to appear. MIT-Press, 2000. [ bib ] |
[829] | D. Horn, N. Levy, I. Meilijson, and E. Ruppin. Distributed synchrony of spiking neurons in a Hebbian cell assembly. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 129-135. MIT Press, 2000. [ bib ] |
[830] | D. Horn, N. Levy, and E. Ruppin. Memory maintenance via neuronal regulation. Neural Computation, 10:1-18, 1998. [ bib ] |
[831] | D. Horn and L. S. Fast temporal encoding and decoding with spiking neurons. Neural Comput., 10:1705-1720, 1998. [ bib ] |
[832] | D. Horn and D. Sagi. Parallel activation of memories in an oscillatory neural network. Neural Computation, 3:31-43, 1991. [ bib ] |
[833] | D. Horn, D. Sagi, and M. Usher. Segmentation, binding, and illusory conjunctions. Neural Computation, 3:510-525, 1991. [ bib ] |
[834] | D. Horn and M. Usher. Oscillatory model of short term memory. In J. E. Moody, S. J. Hanson, and R. P. Lippmann, editors, Advances in Neural Information Processing Systems, volume 4, pages 125-132, San Mateo CA, 1992. Morgan Kaufmann. [ bib ] |
[835] | D. Horn and M. Usher. Neural networks with dynamical thresholds. Phys. Rev. A, 40:1036-1040, 1989. [ bib ] |
[836] | R. A. Horn and C. R. Johnson. Matrix analysis. Cambridge University Press, Cambridge, UK, 1985. [ bib ] |
[837] | J. Horton and D. Adams. The cortical column: a structure without a function. Philosophical Transactions: Biological Sciences, 360(1456):837-862, 2005. [ bib ] |
[838] | T. Hosoya, S. A. Baccus, and M. Meister. Dynamic predictive coding by the retina. Nature, 436(7047):71-77, July 2005. [ bib | http ] |
[839] | J. Houk, J. Adams, and A. Barto. A model of how the basal ganglia generate and use neural signals that predict reinforcement. In J. C. Houk, J. L. Davis, and D. G. Beiser, editors, Models on Information Processing in the Basal Ganglia, pages 249-270. MIT Press, Cambridge, 1995. [ bib ] |
[840] | D. Hubel and T. Wiesel. Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195:215-243, 1968. [ bib ] |
[841] | D. Hubel and T. Wiesel. Binocular interaction in striate cortex of kittens reared with artificial squint. The Journal of Neurophysiology, 28:1041-1059, 1965. [ bib ] |
[842] | D. Hubel and T. Wiesel. Receptive fields of cells in striate cortex of very young, visually inexperienced kittens. Journal of Neurophysiology, 26(6):994-1002, 1963. [ bib ] |
[843] | D. Hubel and T. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology, 160(1):106-154, 1962. [ bib ] |
[844] | D. Hubel and T. Wiesel. Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex. The Journal of Physiology (London), 160:106-154, 1962. [ bib ] |
[845] | D. H. Hubel. Eye, brain, and vision. W. H. Freeman, New York, 1988. [ bib ] |
[846] | D. H. Hubel and T. N. Wiesel. Functional architecture of macaque monkey visual cortex. Proc. R. Soc. B, 198:1-59, 1977. [ bib ] |
[847] | D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. (London), 160:106-154, 1962. [ bib ] |
[848] | D. H. Hubel and T. N. Wiesel. Receptive fields of single neurons in the cat's striate cortex. J. Physiol., 148:574-591, 1959. [ bib ] |
[849] | A. Hughes. The visual system in vertebrates. In F. Crescitelli, editor, The Handbook of Sensory Physiology, volume Vol VI1/5, pages 615-756. Springer, Berlin, 1977. [ bib ] |
[850] | A. Hughes. A schematic eye for the rat. Vision Research, 19:569-588, 1978. [ bib ] |
[851] | J. E. Humphreys. Introduction to Lie algebras and representation theory. Springer, 1994. [ bib ] |
[852] | M. D. Humphries and K. Gurney. Solution methods for a new class of simple model neurons. Neural Comput, 19(12):3216-3225, 2007. [ bib | DOI ] |
[853] | C. Hung, G. Kreiman, T. Poggio, and J. DiCarlo. Fast readout of object identity from macaque inferior temporal cortex. Science, 310:863 - 866, 2005. [ bib ] |
[854] | M. Hutter. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer, Berlin, 2004. (On J. Schmidhuber's SNF grant 20-61847). [ bib ] |
[855] | Q. J. M. Huys, M. B. Ahrens, and L. Paninski. Efficient estimation of detailed single-neuron models. J Neurophysiol, 96(2):872-890, 2006. [ bib | DOI ] |
[856] | A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analysis. Wiley, 2001. [ bib ] |
[857] | A. Hyvärinen and E. Oja. Independent component analysis: algorithms and applications. Neural Networks, 13(4-5):411-430, 2000. [ bib ] |
[858] | A. Hyvärinen. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks, 10:626-634, 1999. [ bib ] |
[859] | A. Hyvärinen. Survey on independent component analysis. Neural Computing Surveys, 2:94-128, 1999. [ bib ] |
[860] | A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analysis. Wiley, New York, 2001. [ bib ] |
[861] | A. Hyvärinen and P. Pajunen. Nonlinear independent component analysis: Existence and uniqueness results. Neural Networks, 12(3):429-439, 1999. [ bib ] |
[862] | P. Häfliger, M. Mahowald, and L. Watts. A spike based learning neuron in analog vlsi. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems, volume 9, pages 692-698. The MIT Press, 1997. [ bib ] |
[863] | M. Häusser, G. Major, and G. J. Stuart. Differential shunting of epsps by action potentials. Science, 291:138-141, 2001. [ bib ] |
[864] | M. Häusser and B. Mel. Dendrites: Bug or feature? Current Opinion in Neurobiology, 13:372-282, 2003. [ bib ] |
[865] | C. Hölscher, A. Schnee, H. Dahmen, L. Setia, and H. Mallot. Rats are able to navigate in virtual environments. Journal of Experimental Biology, 208:561-569, 2005. [ bib ] |
[866] | K. Ibata, Q. Sun, and G. G. Turrigiano. Rapid synaptic scaling induced by changes in postsynaptic firing. Neuron, 57(6):819-826, Mar 2008. [ bib | DOI | http ] |
[867] | M. A. P. Idiart and L. F. Abbott. Propagation of excitation in neural network models. Network, 4:285-294, 1993. [ bib ] |
[868] | Y. Ikegaya, G. Aaron, D. Aranov, I. Lampl, D. Ferster, and R. Yuste. Synfire chains and cortical songs: Temporal modules of cortical activity. Science, 304:559-564, 2004. [ bib ] |
[869] | N. Intrator and L. Cooper. Objective function formulation of the bcm theory of visual cortical plasticity - statistical connections, stability conditions. Neural Networks, 5:3-17, 1992. [ bib ] |
[870] | J. T. Isaac, R. A. Nicoll, and R. C. Malenka. Evidence for silent synapses: Implications for the expression of ltp. Neuron, 15:427-434, 1995. [ bib ] |
[871] | M. Ito, H. Tamura, I. Fujita, and K. Tanaka. Size and position invariance of neuronal responses in monkey inferotemporal cortex. Journal of Neurophysiology, 73:218-226, 1995. [ bib ] |
[872] | E. Izhikevich. Solving the distal reward problem through linkage of stdp and dopamine signaling. Cerebral Cortex, 17:2443-2452, 2007. [ bib ] |
[873] | E. Izhikevich. Solving the Distal Reward Problem through Linkage of STDP and Dopamine Signaling. Cerebral Cortex, 17(10):2443-2452, 2007. [ bib ] |
[874] | E. Izhikevich. Polychronization: Computation with Spikes. Neural Computation, 18(2):245-282, 2005. [ bib ] |
[875] | E. Izhikevich. Which model to use for cortical spiking neurons? IEEE Transactions on Neural Networks, 15:1063-1070, 2004. [ bib ] |
[876] | E. Izhikevich. Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14:1569-1572, 2003. [ bib ] |
[877] | E. Izhikevich. Synchronization of elliptic bursters. SIAM Review, 43:315-344, 2001. [ bib ] |
[878] | E. Izhikevich. Resonate-and-fire neurons. Neural Networks, 14:883-894, 2001. [ bib ] |
[879] | E. Izhikevich. Neural excitability, spiking, and bursting. Int. J. of Bif. and Chaos, 10:1171-1266, 2000. [ bib ] |
[880] | E. Izhikevich. Class 1 neural excitability, conventional synapses, weakly connected networks, and mathematical foundations of pulse-coupled models. IEEE Transactions on Neural Networks, 10:499-507, 1999. [ bib ] |
[881] | E. Izhikevich and N. Desai. Relating stdp to bcm. Neural Computation, 15:1511-1523, 2003. [ bib ] |
[882] | E. M. Izhikevich. Dynamical systems in neuroscience : the geometry of excitability and bursting. MIT Press, Cambridge, Mass., 2007. [ bib ] |
[883] | E. M. Izhikevich. Simple model of spiking neurons. IEEE Trans Neural Netw, 14(6):1569-1572, 2003. [ bib | DOI ] |
[884] | E. M. Izhikevich and G. M. Edelman. Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Sciences, page 0712231105, 2008. [ bib | DOI | http ] |
[885] | J.O'Keefe and M. Recce. Phase relationship between hippocampal place units and the hippocampal theta rhythm. Hippocampus, 3:317-330, 1993. [ bib ] |
[886] | T. Jaakkola, M. I. Jordan, and S. P. Singh. On the convergence of stochastic iterative dynamic programming algorithms. Neural Computation, 6(6):1185-1201, November 1994. [ bib ] |
[887] | J. J. B. Jack, D. Noble, and R. W. Tsien. Electric current flow in excitable cells. Clarendon Press, Oxford, 1975. [ bib ] |
[888] | J. Jackson. Classical Electrodynamics. Wiley, 1962. [ bib ] |
[889] | G. H. JACOBS, J. A. FENWICK, and G. A. WILLIAMS. CONE-BASED VISION OF RATS FOR ULTRAVIOLET AND VISIBLE LIGHTS. Journal of Experimental Biology, 204:2439-2446, 2001. [ bib ] |
[890] | H. Jaeger and H. Haas. Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304:78-80, 2004. [ bib ] |
[891] | W. James. Psychology (Briefer Course), ch. 16. Holt, New York, 1890. [ bib ] |
[892] | M. Jazayeri and J. A. Movshon. Optimal representation of sensory information by neural populations. Nature Neuroscience, 9(5):690-696, 2006. [ bib ] |
[893] | K. J. Jeffery. Self-localization and the entorhinal-hippocampal system. Curr Opin Neurobiol, 17(6):684-691, Dec 2007. [ bib | DOI | http ] |
[894] | K. J. Jeffery and J. M. O'Keefe. Learned interaction of visual and idiothetic cues in the control of place field orientation. Experimental Brain Research, 127:151-161, 1999. [ bib ] |
[895] | L. A. Jeffress. A place theory of sound localisation. J. Comp. Physiol. Psychol., 41:35-39, 1948. [ bib ] |
[896] | O. Jensen and J. Lisman. Hippocampal ca3 region predicts memory sequences: accounting for the phase precession of place cells. Learning and Memory, 3:279-287, 1996. [ bib ] |
[897] | S. R. Jodogne and J. H. Piater. Closed-loop learning of visual control policies. J. Artificial Intelligence Research, 28:349-391, 2007. [ bib ] |
[898] | D. Joel, Y. Niv, and E. Ruppin. Actor-critic models of the basal ganglia: new anatomical and computational perspectives. Neural Networks, 15(4-6):535-547, 2002. [ bib ] |
[899] | P. Johannesma. Diffusion models of the stochastic acticity of neurons. In Neural Networks, pages 116-144, Berlin, 1968. Springer. [ bib ] |
[900] | P. Johannesma, A. Aertsen, H. van den Boogaad, J. Eggermont, and W. Epping. From synchrony to harmony: Ideas on the function of neural assemblies and the interpretation of neural synchrony. In G. Palm and A. Aertsen, editors, Brain Theory, pages 25-47, Berlin Heidelberg New York, 1986. Springer-Verlag. [ bib ] |
[901] | R. Johansson and I. Birznieks. First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nature Neuroscience, 7:170-177, 2004. [ bib ] |
[902] | A. Johnson and A. Redish. Hippocampal replay contributes to within session learning in a temporal difference reinforcement learning model. Neural Networks, 18(9):1163-1171, 2005. [ bib ] |
[903] | L. Joliffe. Principal Component Analysis. Springer-Verlag,, 1986. [ bib ] |
[904] | R. Jolivet and W. Gerstner. Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival. J Physiol Paris, 98(4-6):442-451, 2004. [ bib | DOI ] |
[905] | R. Jolivet and W. Gerstner. Predicting spike times of a detailed conductance- based neuron model driven by stochastic spike arrival. J. Physiol. Paris, 98:442-451, 2004. [ bib ] |
[906] | R. Jolivet, R. Kobayashi, A. Rauch, R. Naud, S. Shinomoto, and W. Gerstner. A benchmark test for a quantitative assessment of simple neuron models. J Neurosci Methods, 2007. [ bib | DOI ] |
[907] | R. Jolivet, T. Lewis, and W. Gerstner. The spike response model: A framework to predict neuronal spike trains. In O. Kaynak, E. Alpaydin, E. Oja, and L. Xu, editors, Proceedings of the Joint International Conference ICANN/ICONIP 2003, pages 846-853, Heidelberg, 2003. Springer-Verlag. [ bib ] |
[908] | R. Jolivet, T. Lewis, and W. Gerstner. Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. J. Neurophysiol., 92:959-976, 2004. [ bib ] |
[909] | R. Jolivet, T. Lewis, and W. Gerstner. Generalized Integrate-and-Fire Models of Neuronal Activity Approximate Spike Trains of a Detailed Model to a High Degree of Accuracy. Journal of Neurophysiology, 92(2):959-976, 2004. [ bib ] |
[910] | R. Jolivet, A. Rauch, H.-R. Lüscher, and W. Gerstner. Integrate-and-fire models with adaptation are good enough. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18, pages 595-602. MIT Press Cambridge, 2006. [ bib ] |
[911] | R. Jolivet, A. Rauch, H.-R. Lüscher, and W. Gerstner. Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J. Computational Neuroscience, 21:35-49, 2006. [ bib ] |
[912] | R. Jolivet, A. Rauch, H.-R. Luscher, and W. Gerstner. Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J Comput Neurosci, 21(1):35-49, 2006. [ bib | DOI ] |
[913] | K. Jones and P. Bawa. Computer simulation of the response of human motoneurons to composite 1a epfsp: effects of background firing rate. J. Neurophysiol., 77:405-420, 1997. [ bib ] |
[914] | A. W. Joseph and R. L. Hyson. Coincidence detection by binaural neurons in the chick brain stem. J. Neurophysiol., 69(4):1197-1211, 1993. [ bib ] |
[915] | J. C. Judd. Neural Network Design and the Complexity of Learning. MIT Press, Cambridge, 1990. [ bib ] |
[916] | M. Jung, S. Wiener, and B. McNaughton. Comparison of spatial firing characteristics of units in dorsal and ventral hippocampus of the rat. Journal of Neuroscience, 14:7347-7356, 1994. [ bib ] |
[917] | M. W. Jung and B. L. McNaughton. Spatial selectivity of unit activity in the hippocampal granular layer. Hippocampus, 3(2):165-182, Apr. 1993. [ bib ] |
[918] | P. Jung. Stochastic resonance and optimal design of threshold detectors. Physics Letters A, 207:93-104, 1995. [ bib ] |
[919] | P. Jung. Periodically driven stochastic systems. Physics Reports, 234:175-295, 1993. [ bib ] |
[920] | C. Jutten and J. Karhunen. Advances in nonlinear blind source separation. Proc. of the 4th Int. Symp. on Independent Component Analysis and Blind Signal Separation (ICA2003), pages 245-256, 2003. [ bib ] |
[921] | P. König, A. K. Engel, and W. Singer. Integrator or coincidence detector? The role of the cortical neuron revisited. TINS, 19(4):130-137, 1996. [ bib ] |
[922] | R. Kühn, S. Bös, and J. L. van Hemmen. Statistical mechanics for networks of graded-response neurons. Phys. Rev. A, 43:2084-2087, 1991. [ bib ] |
[923] | P. König and T. B. Schillen. Stimulus-dependent assembly formation of oscillatory responses: I. synchronization. Neural Computation, 3:155-166, 1991. [ bib ] |
[924] | L. Kaelbling, M. Littman, and A. Moore. Reinforcement learning, a survey. J. Artificial Intelligence Research, 4:237-285, 1996. [ bib ] |
[925] | L. P. Kaelbling, M. L. Littman, and A. W. Moore. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4:237, 1996. [ bib | http ] |
[926] | S. Kali and P. Dayan. The involvement of recurrent connections in area ca3 in establishing the properties of place. J. of Neuroscience, 20:7463-7477, 2000. [ bib ] |
[927] | B. Kampa, J. Letzkus, and G. Stuart. Dendritic mechanisms controlling spike-timing-dependent synaptic plasticity. Trends in Neurosciences, 30(9):456-463, 2007. [ bib ] |
[928] | N. G. van Kampen. Stochastic processes in physics and chemistry. North-Holland, Amsterdam, 2nd edition, 1992. [ bib ] |
[929] | E. Kandel. The molecular biology of memory storage: a dialogue between genes and synapses. Science, 294:1030-1038, 2001. [ bib ] |
[930] | E. C. Kandel and J. H. Schwartz. Principles of Neural Science. Elsevier, New York, 3rd edition, 1991. [ bib ] |
[931] | I. Kanter and H. Sompolinsky. Mean-field theory of spin-glasses with finite coordination-number. Phys. Rev. Lett., 58:164-167, 1987. [ bib ] |
[932] | P. Kanverva. Sparse distributed memory. MIT Press, 1988. [ bib ] |
[933] | P. Kara, P. Reinagel, and R. Reid. Low response variability in simultaneously recorded retinal, thalamic, and cortical neuron. Neuron, 27:635-646, 2002. [ bib ] |
[934] | Y. Karklin and M. S. Lewicki. A hierarchical Bayesian model for learning non-linear statistical regularities in non-stationary natural signals. Neural Computation, 17(2):397-423, 2005. [ bib ] |
[935] | U. Karmarkar and D. Buonomano. A model of spike-timing dependent plasticity: one or two coincidence detectors. J. Neurophysiology, 88:507-513, 2002. [ bib ] |
[936] | U. Karmarkar and Y. Dan. Experience-dependant plasticity in adult visual cortex. Neuron, 52:577-585, 2006. [ bib ] |
[937] | U. Karmarkar, M. Najarian, and D. Buonomano. Mechanisms and significance of spike-timing dependent plasticity. Biol. Cybernetics, 87:373-382, 2002. [ bib ] |
[938] | A. Karni and G. Bertini. Learning perceptual skills: behavioral probes into adult cortical plasticity. Current Opinion in Neurobiology, 7:530-535, 1997. [ bib ] |
[939] | A. Karni and D. Sagi. The time course of learning a visual skill. Nature, 365:250-252, 1993. [ bib ] |
[940] | A. Karni and D. Sagi. Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. PNAS, 88:4966-4970, 1991. [ bib ] |
[941] | H. Kasai, M. Matsuzaki, J. Noguchi, N. Yasumatsu, and H. Nakahara. Structure-stability-function relationship of dendritic spines. Trends in Neurosciences, 26:360-368, 2003. [ bib ] |
[942] | R. E. Kass and V. Ventura. A spike-train probability model. Neural Computation, 13:1713-1720, 2001. [ bib ] |
[943] | S. Kastner, P. D. Weerd, M. A. Pinsk, M. I. Elizondo, R. Desimone, and L. G. Ungerleider. Modulation of sensory suppression: implications for receptive field sizes in the human visual cortex. Journal of Neurophysiology, 86(3):1398-1411, Sept. 2001. [ bib ] |
[944] | L. C. Katz and C. J. Shatz. Synaptic activity and the construction of cortical circuits. Science, 274:1133-1138, 1996. [ bib ] |
[945] | J. Kay. Information-theoretic neural networks for unsupervised learning: mathematical and statistical considerations. Technical Report: Scottish agricultural statistics service, 1994. [ bib ] |
[946] | G. Kayaert, I. Biederman, and R. Vogels. Shape tuning in macaque inferior temporal cortex. Journal of Neuroscience, 23(7):3016-3027, Apr. 2003. [ bib ] |
[947] | C. Kayser, W. Einhäuser, O. Dümmer, K. Körding, and P. König. Extracting slow subspaces from natural videos leads to complex cells. In Proc. Int. Conf. on Artif. Neural Networks (ICANN) Springer: Lecture Notes in Computer Science, volume 2130, pages 1075-1079, 2001. [ bib ] |
[948] | J. Keat, P. Reinagel, R. Reid, and M. Meister. Predicting every spike: A model for the responses of visual neurons. Neuron, 30:803-817, 2001. [ bib ] |
[949] | J. Keat, P. Reinagel, R. C. Reid, and M. Meister. Predicting every spike: a model for the responses of visual neurons. Neuron, 30(3):803-817, 2001. [ bib ] |
[950] | S. R. Kelso, A. H. Ganong, and T. H. Brown. Hebbian synapses in hippocampus. Proc. Natl. Acad. Sci. USA, 83:5326-5330, 1986. [ bib ] |
[951] | R. Kempter. Hebbsches Lernen zeitlicher Codierung: Theorie der Schallortung im Hörsystem der Schleiereule. Naturwissenschaftliche Reihe, Bd. 17, Darmstadt, 1997. [ bib ] |
[952] | R. Kempter, W. Gerstner, and J. van Hemmen. How the threshold of a neuron determines its capacity for coincidence detection. BioSystems, 48:105-112, 1998. [ bib ] |
[953] | R. Kempter, W. Gerstner, and J. L. van Hemmen. Spike-based compared to rate-based hebbian learning. In M. Kearns, S. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 125-131. MIT-Press, 1999. [ bib ] |
[954] | R. Kempter, W. Gerstner, and J. L. van Hemmen. Intrinsic stabilization of output rates by spike-based hebbian learning. Neural Computation, 13:2709-2741, 2001. [ bib ] |
[955] | R. Kempter, W. Gerstner, and J. L. van Hemmen. Hebbian learning and spiking neurons. Phys. Rev. E, 59:4498-4514, 1999. [ bib ] |
[956] | R. Kempter, W. Gerstner, and J. L. van Hemmen. How the threshold of a neuron determines its capacity for coincidence detection. Biosystems, 48(1-3):105-112, 1998. [ bib ] |
[957] | R. Kempter, W. Gerstner, J. L. van Hemmen, and H. Wagner. The quality of coincidence detection and itd-tuning: a theoretical framework. In T. Dau, V. Hohmann, and B. Kollmeier, editors, Psychophysics, Physiology and Models of Hearing, pages 185-192. World Scientific, Singapore, 1999. [ bib ] |
[958] | R. Kempter, W. Gerstner, J. L. van Hemmen, and H. Wagner. Extracting oscillations: Neuronal coincidence detection with noisy periodic spike input. Neural Comput., 10:1987-2017, 1998. [ bib ] |
[959] | R. Kempter, W. Gerstner, J. L. van Hemmen, and H. Wagner. Temporal coding in the sub-millisecond range: Model of barn owl auditory pathway. In Advances in Neural Information Processing Systems 8, pages 124-130, Cambridge, MA, 1996. MIT Press,. [ bib ] |
[960] | R. Kempter, W. Gerstner, H. Wagner, and J. van Hemmen. Model of map formation in the barn owl. in preparation, 1995. [ bib ] |
[961] | T. Kenet, D. Bibibtchkov, M. Tsodyks, and A. G. andA. Arieli. Spontaneously emerging cortical representations of visual attributes. Nature, 425:954-956, 2003. [ bib ] |
[962] | A. Kepecs, M. van Rossum, S. Song, and J. Tegner. Spike-timing-dependent plasticity: Common themes and divergent vistas. Biological Cybernetics, 87(5):446-458, 2002. [ bib ] |
[963] | A. Kepecs, M. C. van Rossum, S. Song, and J. Tegner. Spike-timing-dependent plasticity: common themes and divergent vistas. Biol. Cybern., 87:446-458, 2002. [ bib ] |
[964] | T. B. Kepler, L. F. Abbott, and E. Marder. Reduction of conductance-based neuron models. Biol. Cybern., 66:381-387, 1992. [ bib ] |
[965] | N. Keren, N. Peled, and A. Korngreen. Constraining compartmental models using multiple voltage recordings and genetic algorithms. J Neurophysiol, 94(6):3730-3742, 2005. [ bib | DOI ] |
[966] | D. Kernell and H. Sjöholm. Repetitive impulse firing: comparison between neuron models based on 'voltage clamp equations' and spinal motoneurons. Acta Physiol. Scand., 87:40-56, 1973. [ bib ] |
[967] | M. Kerszberg and A. Zippelius. Synchronization in neural assemblies. Physica Scripta, T33:54-64, 1990. [ bib ] |
[968] | R. P. Kesner and J. Rogers. An analysis of independence and interactions of brain substrates that subserve multiple attributes, memory systems, and underlying processes. Neurobiology of Learning and Memory, 82:199-215, 2004. [ bib ] |
[969] | C. Keysers, D. K. Xiao, P. Foldiak, and D. I. Perrett. The speed of sight. J. Cognitive Neuroscience, 13:90-101, 2001. [ bib ] |
[970] | V. Khimenko. Behavior of a derivative at moments of the crossing of a given level by a random process. Radiofizika, 25(7):797-804, 1982. [ bib ] |
[971] | A. Kirkwood, M. Rioult, and M. Bear. Experience-dependent modification of synaptic plasticity in visual cortex. Nature, 381:526-528, 1996. [ bib ] |
[972] | P. Kirkwood and P. Sears. The synaptic connexions to intercostal motoneurones as revealed by the average common excitation potential. J. Physiology, 275:103-134, 1978. [ bib ] |
[973] | W. M. Kistler. Spike-timing dependent plasiticity: a phenomenological framework. Biological Cybernetics, xx:xx, 2002. [ bib ] |
[974] | W. M. Kistler. Stability properties of solitary waves and perodic wave trains in a two-dimensional network of spiking neurons. Phys. Rev. E, 62:8834-8837, 2000. [ bib ] |
[975] | W. M. Kistler and C. I. De Zeeuw. Dynamical working memory and timed responses: The role of reverberating loops in the olivo-cerebellar system. Neural Comput., pages 2597-2626, 2002. [ bib ] |
[976] | W. M. Kistler and W. Gerstner. Stable propagation of activity pulses in populations of spiking neurons. Neural Computation, 14:987-997, 2002. [ bib ] |
[977] | W. M. Kistler, W. Gerstner, and J. L. van Hemmen. Reduction of Hodgkin-Huxley equations to a single-variable threshold model. Neural Comput., 9:1015-1045, 1997. [ bib ] |
[978] | W. M. Kistler and J. L. van Hemmen. An analytically solvable model of collectiv excitation patterns in cortical tissue. In J. Parisi, S. C. Müller, and W. Zimmermann, editors, A perspective look at nonlinear media in physics, chemistry, and biology. Springer, 1998. [ bib ] |
[979] | W. M. Kistler and J. L. van Hemmen. Modeling synaptic plasticity in conjunction with the timing of pre- and postsynaptic potentials. Neural Comput., 12:385-405, 2000. [ bib ] |
[980] | W. M. Kistler and J. L. van Hemmen. Modeling synaptic plasticity in conjunction with the timing of pre- and postsynaptic action potentials. Neural Computation, 12:385, 2000. [ bib ] |
[981] | W. M. Kistler, R. Seitz, and J. L. van Hemmen. Modelling collective excitations in cortical tissue. Physica D, 114:273-295, 1998. [ bib ] |
[982] | T. Kitajima and K. Hara. A generalized hebbian rule for activity-dependent synaptic modifications. Neural Networks, 13:445-454, 2000. [ bib ] |
[983] | T. Kitajima and K. Hara. A model of the mechanisms of long-term potentiation in the hippocampus. Biol. Cybern., 64:33-39, 1990. [ bib ] |
[984] | C. Kittel et al. Introduction to solid state physics. Wiley, New York, 1986. [ bib ] |
[985] | T. W. Kjaer, J. A. Hertz, and B. J. Richmond. Decoding cortical neuronal signals: network models, information estimation and spatial tuning. J. Comput. Neuroscience, 1:109-139, 1994. [ bib ] |
[986] | D. Kleinfeld. Sequential state generation by model neural networks. Proc. Natl. Acad. Sci. USA, 83:9469-9473, 1986. [ bib ] |
[987] | A. Klopf. A neuronal model of classical conditioning. Psychobiology, 16:85-125, 1988. [ bib ] |
[988] | A. Klopf. The hedonistic neuron: a theory of memory, learning, and intelligence. Hemisphere, 1982. [ bib ] |
[989] | J. Knierim. Neural representations of location outside hippocampus. Learning & Memory, 13:405-415, 2006. [ bib ] |
[990] | J. J. Knierim. Dynamic interactions between local surface cues, distal landmarks, and intrinsic circuitry in hippocampal place cells. Journal of Neuroscience, 22(14):6254-6264, 2002. [ bib ] |
[991] | J. J. Knierim, H. S. Kudrimoti, and B. L. McNaughton. Interactions between idiothetic cues and external landmarks in the control of place cells and head direction cells. Journal of Neurophysiology, 80(1):425-446, July 1998. [ bib ] |
[992] | J. J. Knierim, H. S. Kudrimoti, and B. L. McNaughton. Place cells, head direction cells, and the learning of landmark stability. Journal of Neuroscience, 15(3 Pt 1):1648-1659, Mar. 1995. [ bib ] |
[993] | J. J. Knierim and G. Rao. Distal landmarks and hippocampal place cells: effects of relative translation versus rotation. Hippocampus, 13(5):604-617, 2003. [ bib ] |
[994] | B. W. Knight. Dynamics of encoding in neuron populations: some general mathematical features. Neural Computation, 12:473-518, 2000. [ bib ] |
[995] | B. W. Knight. Dynamics of encoding in a population of neurons. J. Gen. Physiology, 59:734-766, 1972. [ bib ] |
[996] | B. W. Knight. The relationship between the firing rate of a single neuron and the level of activity in a population of neurons. J. Gen. Physiology, 59:767-778, 1972. [ bib ] |
[997] | G. Knott, A. Holtmaat, L. Wilbrecht, E. Welker, and K. Svoboda. Spine growth precedes synapse formation in the adult neocortex in vivo. Nature Neuroscience, 9:1117-1124, 2006. [ bib ] |
[998] | C. K. Knox. Cross-corrlation functions for a neuronal model. Biophysical J., 14:567-582, 1974. [ bib ] |
[999] | E. Knudsen, S. DuLac, and E. D. Esterly. Computational maps in the brain. Annu. Rev. Neurosci., 10:41-65, 1987. [ bib ] |
[1000] | E. I. Knudsen, G. G. Blasdel, and M. Konishi. Sound localization by the barn owl (tyto alba) measured with the search coil technique. J. Comp. Physiol., 133:1-11, 1979. [ bib ] |
[1001] | E. Kobatake and K. Tanaka. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. Journal of Neurophysiology, 71(3):856-867, Mar. 1994. [ bib ] |
[1002] | T. Kobayashi, H. Nishijo, M. Fukuda, J. Bures, and T. Ono. Task-dependent representations in rat hippocampal place neurons. Journal of Neurophysiology, 78(2):597-613, Aug. 1997. [ bib ] |
[1003] | C. Koch. Biophysics of Computation. Oxford University Press, New York, Oxford, 1999. [ bib ] |
[1004] | C. Koch. Biophysics of computation : information processing in single neurons. Oxford University Press, New York, 1999. [ bib | .html ] |
[1005] | C. Koch, Ö. Bernander, and R. Douglas. Do neurons have a voltage or a current threshold for action potential initiation? J. Comput. Neurosci., 2:63-82, 1995. [ bib ] |
[1006] | C. Koch, M. Rapp, and I. Segev. A brief history of time constants. Cerebral cortex, 6:93-101, 1996. [ bib ] |
[1007] | C. Koch, M. Rapp, and I. Segev. A brief history of time (constants). Cerebral Cortex, 6:92-101, 1996. [ bib ] |
[1008] | C. Koch and I. Segev. The role of single neurons in information processing. Nature Neuroscience, 3(Supp):1171-1177, 2000. [ bib ] |
[1009] | C. Koch and I. Segev. Methods in Neuronal Modeling. MIT Press, 1989. [ bib ] |
[1010] | A. F. Kohn. Dendritic transformations on random synaptic inputs as measured from a neurons spike train - modeling and simulation. IEEE transactions on biomedical engineering, 16:44-54, 1989. [ bib ] |
[1011] | T. Kohonen. Physiological interpretation of the self-organizing map algorithm. Neural Networks, 6:895-905, 1993. [ bib ] |
[1012] | T. Kohonen. The self-organizing map. Proceedings of the IEEE, 78:1464-1480, 1990. [ bib ] |
[1013] | T. Kohonen. Self-organization and associative memory, 3rd edition. Springer-Verlag, Berlin Heidelberg New York, 1989. [ bib ] |
[1014] | T. Kohonen. Self-Organization and Associative Memory. Springer-Verlag, Berlin Heidelberg New York, 1984. [ bib ] |
[1015] | T. Kohonen. Correlation matrix memories. IEEE trans. comp., C-21:353-359, 1972. [ bib ] |
[1016] | M. H. P. Kole, S. Hallermann, and G. J. Stuart. Single ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output. J Neurosci, 26(6):1677-1687, 2006. [ bib | DOI ] |
[1017] | C. Kolodziejski, B. Porr, and F. Wörgötter. Mathematical properties of neuronal td-rules and differential hebbian learning: a comparison. Biol. Cybern., page 0, 2008. [ bib ] |
[1018] | C. Kolodziejski, B. Porr, and F. Wörgötter. Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison. Biol. Cybern., 98(3):259-272, 2008. [ bib | DOI ] |
[1019] | H. Kondgen, C. Geisler, S. Fusi, X. Wang, H. Luscher, and M. Giugliano. The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cereb Cortex, 2008. [ bib | DOI ] |
[1020] | P. Konig, A. K. Engel, and W. Singer. Integrator or coincidence detector? the role of the cortical neuron revisited. Trends Neurosci, 19(4):130-137, 1996. [ bib ] |
[1021] | M. Konishi. Listening with two ears. Scientific American, 268:34-41, April 1993. [ bib ] |
[1022] | M. Konishi. Centrally synthesized maps of sensory space. Trends in Neurosciences, 9(4):163-168, April 1986. [ bib ] |
[1023] | N. Kopell. Symmetry and phase locking in chains of weakly coupled oscillators. Communications on pure and applied mathematics, 39:623-660, 1986. [ bib ] |
[1024] | N. Kopell. Phase methods for coupled oscillators and related topics: An annnotated bibliography. J. Stat. Phys., 44:1035-1042, 1986. [ bib ] |
[1025] | H. Kopka. Latex, vol. 1 and 2. Addison-Wesley, Deutschland, 1991. [ bib ] |
[1026] | A. Kossel, T. Bonhoeffer, and J. Bolz. Non-Hebbian synapses in rat visual cortex. NeuroReport, 1:115-118, 1990. [ bib ] |
[1027] | A. Koulakov, S. Raghavachari, A. Kepecs, and J. Lisman. Model for a robust neural integrator. Nature Neuroscience, 5:775-782, 2002. [ bib ] |
[1028] | Z. Kourtzi and J. DiCarlo. Learning and neural plasticity in visual object recognition. Current Opinion in Neurobiology, 16(2):152-158, 2006. [ bib ] |
[1029] | J. Krüger. Neuronal Cooperativity. Springer, Berlin Heidelberg New York, 1991. [ bib ] |
[1030] | J. Krüger. Simultaneous individual recordings from many cerebral neurons: Techniques and results. Rev. Physiol. Biochem. Pharmacol., 98:177-233, 1983. [ bib ] |
[1031] | J. Krüger and F. Aiple. Multimicroelectrode investigation of monkey striate cortex: spike train correlations in the infragranular layers. J. Neurophysiol., 60:798-828, 1988. [ bib ] |
[1032] | J. Krüger and J. D. Becker. Recognizing the visual stimulus from neuronal discharges. TINS, 14:282-286, 1991. [ bib ] |
[1033] | W. Krauth and M. Mézard. Learning algorithms with optimal stability in neural networks. Phys. Rev. A, 20:L745-L752, 1987. [ bib ] |
[1034] | A. K. Kreiter and W. Singer. Oscillatory neuronal responses in the visual cortex of the awake macaque monkey. Eur. J. Neurosci., 4:369-375, 1992. [ bib ] |
[1035] | J. Kretzberg, M. Egelhaaf, and A.-K. Zarzecha. Membrane potential fluctuations determine the precision of spike timing and synchronous activity: a model study. J. Comput. Neuroscience, 10:79-97, 2001. [ bib ] |
[1036] | G. Krone, H. Mallot, G. Palm, and A. Schütz. Spatiotemporal receptive fields: a dynamical model derived from cortical interactions. Proc. Roy. Soc. London, Ser. B., 226:421-444, 1986. [ bib ] |
[1037] | S. W. Kuffler, J. G. Nicholls, and A. R. Martin. From neuron to brain. Sinauer, Sunderland Mass., 2nd edition, 1984. [ bib ] |
[1038] | D. Kullmann and K.P.Lamsa. Long-term synaptic plasticity in hippocampal interneurons. Nat. Rev. Neurosci., 8:687-699, 2007. [ bib ] |
[1039] | D. M. Kullmann. Silent synapses: what are they telling us about long-term potentiation? Phil. Trans. R. Soc. Lond B: Biological Sciences, 358:727 - 733, 2003. [ bib ] |
[1040] | A. Kumar, J. Kremkov, S. Rotter, and A. Aertsen. Synaptic integration in a 3-compartment model of layer 5 pyramidal neurons. FENS abstract, 2:A014.27, 2004. [ bib ] |
[1041] | Y. Kuramoto. Self-entrainment of a population of coupled nonlinear oscillators. In H. Araki, editor, International symposium on mathematical problems in theoretical physics, pages 420-422, Berlin Heidelberg New York, 1975. Springer-Verlag. [ bib ] |
[1042] | Y. Kuramoto. Collective sunchronization of pulse-coupled oscillators and excitable units. Physica D, 50:15-30, 1991. [ bib ] |
[1043] | Y. Kuramoto. Chemical Oscillations, Waves, and Turbulence. Springer, Berlin Heidelberg New York, 1984. 68-77. [ bib ] |
[1044] | Y. Kuramoto. Cooperative dynamics of oscillator community. Progress of theoretical physics Suppl., 79:223-240, 1984. [ bib ] |
[1045] | Y. Kuramoto and I. Nishikawa. Statistical macrodynamics of large dynamical systems. case of a phase transition in oscillatory communities. J. Stat. Phys., 49:569-605, 1987. [ bib ] |
[1046] | C. Kurrer, B. Nieswand, and K. Schulten. A model for synchroneous activity in the visual cortex. In B. A., editor, Self-Organiztion, emerging properties and learning., pages 81-85, New York, 1990. Plenum Press. [ bib ] |
[1047] | C. Kurrer and K. Schulten. Noise-induced oscillations. preprint, University of Illinois, 1994. [ bib ] |
[1048] | C. K"oppl. Phase locking at high frequencies in the barn owl's auditory nerve. Abstracts of the eighteenth midwinter research meeting of the Association for Research in Otolaryngology in St. Petersburg Beach, Florida, 1995. [ bib ] |
[1049] | K. Körding, C. Kayser, W. Einhäuser, and P. König. How are complex cell properties adapted to the statistics of natural stimuli? Journal of Neurophysiology, 91(1):206-212, 2004. [ bib ] |
[1050] | K. Körding and P. König. Neurons with two sites of synaptic integration learn invariant representations. Neural Computation, 13:2823-2849, 2001. [ bib ] |
[1051] | K. Körding and P. König. A learning rule for dynamic recruitment and decorrelation. Neural Networks, 13:1-9, 2000. [ bib ] |
[1052] | K. Körding and P. König. A spike based learning rule for generation of invariant representations. J. Physiol. Paris, 94:539-548, 2000. [ bib ] |
[1053] | K. P. Körding and P. König. Neurons with two sites of synaptic integration learn invariant representations. Neural Computation, 13(12):2823-2849, Dec. 2001. [ bib ] |
[1054] | L.M.Pecora and T.L.Carroll. Synchronization in chaotic systems. Phys. Rev. Lett., 64:821-824, 1990. [ bib ] |
[1055] | G. La Camera, A. Rauch, H.-R. Lüscher, W. Senn, and S. Fusi. Minimal models of adpated neuronal responses to in-vivo like input currents. Neural Computation, 16:2101-2104, 2004. [ bib ] |
[1056] | C. R. Laing and C. C. Chow. Stationary bumps in a network of spiking neurons. Neural Computation, 13:1473-1494, 2001. [ bib ] |
[1057] | J. Lamperti. Probability. Benjamin, New York, 1966. Chapter 7. [ bib ] |
[1058] | B. Lancaster and P. R. Adams. Calcium-dependent current generating the afterhyperpolarization of hippocampal neurons. J. Neurophysiol., 55:1268-1282, 1986. [ bib ] |
[1059] | L. D. Landau and E. M. Lifshitz. Quantum Mechanics: Non-relativistic theory, volume 3 of Course of Theoretical Physics. Pergamon Press, 1977. [ bib ] |
[1060] | M. S. Landy and I. Oruc. Properties of second-order spatial frequency channels. Vision Research, 42(19):2311-2329, Sept. 2002. [ bib ] |
[1061] | E. D. Lange. Neuron models of the generic bifurcation type: network analysis and data modeling. Ph.D. thesis, EPFL, Lausanne, Switzerland, 2006. Dir. Martin Hasler. [ bib ] |
[1062] | E. D. Lange and I. Belykh. Phase locking and coincidence detection in threshold coupled neural oscillators. In IEEE, editor, Proceedings of the IEEE Int. Conference on Nonlinear Dynamics of Electronic Systems (NDES'2003), Scuol, Switzerland, pages 65-68, 2003. [ bib ] |
[1063] | E. D. Lange and I. Belykh. Phase locking and coincidence detection in threshold coupled neural oscillators. In Proc. NDES, pages 65-68, 2003. [ bib ] |
[1064] | C. Langton. Computation at the edge of chaos: Phase-transitions and emergent computation. Physica D, 42:12-37, 1990. [ bib ] |
[1065] | P. Lansky. Sources of periodical force in noisy integrate-and-fire models of neuronal dynamics. Phys. Rev. E, 55:2040-2043, 1997. [ bib ] |
[1066] | P. Lansky. On approximations of Stein's neuronal model. J. Theoretical Biol., 107:631-647, 1984. [ bib ] |
[1067] | P. Lansky and V. Lanska. Diffusion approximation of the neuronal model with synaptic reversal potentials. Biol. Cybern., 56:19-26, 1987. [ bib ] |
[1068] | P. Lansky and M. Musila. Variable initial depolarization in stein's neuronal model with synaptic reversal potentials. Biological Cybernetics, 64:285-291, 1991. [ bib ] |
[1069] | P. Lansky and J. Rospars. Ornstein-uhlenbeck model neuron revisited. Biol. Cybern., 72:397-406, 1995. [ bib ] |
[1070] | P. Lansky and L. Sacerdote. The Ornstein Uhlenbeck neuronal model with signal-dependent noise. Physics Letters A, 285:132-140, 2001. [ bib ] |
[1071] | P. Lansky and C. Smith. The effect of random initial value in neural first-passage-time models. Mathematical Biosciences, 93:191-215, 1989. [ bib ] |
[1072] | L. Lapicque. Recherches quantitatives sur l'excitation electrique des nerfs traitée comme une polarization. J. Physiol. Pathol. Gen., 9:620-635, 1907. Cited in H.C. Tuckwell, Introduction to Theoretic Neurobiology. (Cambridge Univ. Press, Cambridge, 1988). [ bib ] |
[1073] | M. Larkum, W. Senn, and H. Lüscher. Top-down dendritic input increases the gain of layer 5 pyramidal neurons. CEREBRAL CORTEX, 14:1059-1079, 2004. [ bib ] |
[1074] | M. Larkum, J. Zhu, and B. Sakmann. Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons. J. Physiology (London), 447-466, 2001. [ bib ] |
[1075] | M. E. Larkum, W. Senn, and H.-R. Luscher. Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cereb Cortex, 14(10):1059-1070, 2004. [ bib | DOI ] |
[1076] | M. E. Larkum, J. J. Zhu, and B. Sakmann. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature, 398(6725):338-341, 1999. [ bib | DOI ] |
[1077] | J. Larson and G. Lynch. Induction of synaptic potentiation in hippocampus by patterned stimualtion involves two events. Science, 232:985-988, 1986. [ bib ] |
[1078] | J.-M. Lassalle, T. Bataille, and H. Halley. Reversible inactivation of the hippocampal mossy fiber synapses in mice impaires spatial learning but neither consolidation nor memory retrieval in the Morris navigation task. Neurobiology of learning and memory, 243-257, 2000. [ bib ] |
[1079] | P. Latham and S. Nirenberg. Computing and stability in cortical networks. Neural Computation, 16:1385-1412, 2004. [ bib ] |
[1080] | P. E. Latham, B. Richmond, P. Nelson, and S. Nirenberg. Intrinsic dynamics in neuronal networks. I. Theory. J. Neurophysiology, 83:808-827, 2000. [ bib ] |
[1081] | P. E. Latham, B. Richmond, S. Nirenberg, and P. Nelson. Intrinsic dynamics in neuronal networks. II. Eperiments. J. Neurophysiology, 83:828-835, 2000. [ bib ] |
[1082] | S. Laughlin. A simple coding procedure enhances a neurons information capacity. Z. Naturforschung, 36:910-912, 1981. [ bib ] |
[1083] | S. Laughlin, R. R. deRuyter van Steveninck, and J. Anderson. The metabolic cost of neural information. Nature Neuroscience, 1(36-41), 1998. [ bib ] |
[1084] | G. Laurant and H. Davidowitz. Encoding of olfactory information with oscillating neural assemlies. Science, 265:1872-1875, 1994. [ bib ] |
[1085] | A. Lee, I. Manns, B. Sakmann, and M. Brecht. Whole-cell recordings in freely moving rats. Neuron, 51(4):399-407, 2006. [ bib ] |
[1086] | I. Lee, D. Yoganarasimha, G. Rao, and J. J. Knierim. Comparison of population coherence of place cells in hippocampal subfields CA1 and CA3. Nature, 430:456-459, 2004. [ bib ] |
[1087] | R. Legenstein, C. Naeger, and W. Maass. What can a neuron learn with spike-timing dependent plasticity. Neural Computation, 17:2337-2382, 2005. [ bib ] |
[1088] | R. Legenstein, C. Naeger, and W. Maass. What can a neuron learn with spike-timing-dependent plasticity? Neural Computation, 17(11):2337-2382, 2005. [ bib ] |
[1089] | R. Legenstein, D. Pecevski, and W. Maass. Theoretical analysis of learning with reward-modulated spike-timing-dependent plasticity. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, MA, 2008. [ bib ] |
[1090] | C. Leibold, A. Gundlfinger, R. Schmidt, K. Thurley, D. Schmitz, and R. Kempter. Temporal compression mediated by short-term synaptic plasticity. Proc Natl Acad Sci U S A, 105(11):4417-4422, Mar 2008. [ bib | DOI | http ] |
[1091] | C. Leibold and J. van Hemmen. Mapping time. Biol. Cybern., 87:428-439, 2002. [ bib ] |
[1092] | C. Leibold and R. Kempter. Sparseness constrains the prolongation of memory lifetime via synaptic metaplasticity. Cereb Cortex, 18(1):67-77, Jan 2008. [ bib | DOI | http ] |
[1093] | C. Leibold and R. Kempter. Memory capacity for sequences in a recurrent network with biological constraints. Neural Comput, 18(4):904-941, Apr 2006. [ bib | DOI | http ] |
[1094] | M. Lengyel, J. Kwag, O. Paulsen, and P. Dayan. Matching storage and recall: hippocampal spike timing-dependent plasticity and phase response curves. Nat. Neurosci., 8:1677-1683, 2005. [ bib ] |
[1095] | R. Lestienne. Determination of the precision of spike timing in the visual cortex of anaesthetised cats. Biol. Cybern., 74:55-61, 1996. [ bib ] |
[1096] | J. J. Letzkus, B. M. Kampa, and G. J. Stuart. Learning rules for spike timing-dependent plasticity depend on dendritic synapse location. J. Neurosci., 26(41):10420-10429, Oct. 2006. [ bib | http ] |
[1097] | S. Leutgeb, J. Leutgeb, M. Moser, and E. Moser. Place cells, spatial maps and the population code for memory. Current Opinion in Neurobiology, 15(6):738-46, 2005. [ bib ] |
[1098] | S. Leutgeb, J. K. Leutgeb, C. A. Barnes, E. I. Moser, B. L. McNaughton, and M.-B. Moser. Independent codes for spatial and episodic memory in hippocampal neuronal ensembles. Science, 309:619-623, 2005. [ bib ] |
[1099] | S. Leutgeb, J. K. Leutgeb, A. Treves, M.-B. Moser, and E. I. Moser. Distinct ensemble codes in hippocampal areas CA3 and CA1. Science, 305:1295-1298, 2004. [ bib ] |
[1100] | S. Leutgeb, K. E. Ragozzino, and S. J. Mizumori. Convergence of head direction and place information in the ca1 region of hippocampus. Neuroscience, 100(1):11-19, 2000. [ bib ] |
[1101] | C. Lever, N. Burgess, F. Cacucci, T. Hartley, and J. O'Keefe. What can the hippocampal representation of environmental geometry tell us about hebbian learning? Bio. Cybern., 87:356-372, 2002. [ bib ] |
[1102] | C. Lever, N. Burgess, F. Cacucci, T. Hartley, and J. O'Keefe. What can the hippocampal representation of environmental geometry tell us about hebbian learning? Biological Cybernetics, 87:356-372, 2002. [ bib ] |
[1103] | J. Levin and J. Miller. Broadband neural encoding in the cricket cercal sensory system enhanced by stochastic resonance. Nature, 380:165-168, 1996. [ bib ] |
[1104] | D. S. Levine. Introduction to Neural and Cognitive Modeling. Lawrence Erlbaum, Hillsdale, 1991. [ bib ] |
[1105] | J. B. Levitt, D. C. Kiper, and J. A. Movshon. Receptive fields and functional architecture of macaque V2. Journal of Neurophysiology, 71(6):2517-2542, June 1994. [ bib ] |
[1106] | W. Levy and R. Baxter. Energy-efficient neuronal computation via quantal synaptic failures. J. Neuroscience, 22:4746-4755, 2002. [ bib ] |
[1107] | W. B. Levy and D. Stewart. Temporal contiguity requirements for long-term associative potentiation/depression in hippocampus. Neurosci,, 8:791-797, 1983. [ bib ] |
[1108] | T. J. Lewis and W. Gerstner. Comparison of integrate-and-fire neurons: a case study. Internal report, xx:xx, 2001. [ bib ] |
[1109] | Z. Li. Contextual influences in v1 as a basis for pop out and asymmetry in visual search. Proc. Natl. Acad. Sci. USA, 96:10530-10535, 1999. [ bib ] |
[1110] | D. Liao, N. Hessler, and R. Malinow. Activation of postsynaptically silent synapses during pairing-induced ltp in ca1 region of hippocampal slice. Nature, 375:400-404, 1995. [ bib ] |
[1111] | D. Liao, R. H. Scannevin, and R. Huganir. Activation of silent synapses by rapid activity-dependent synaptic recruitment of ampa receptors. J. Neurosci., 21:6008 - 6017, 2001. [ bib ] |
[1112] | W. Lindemann. Extension of a binaural cross-correlation model by contralateral inhibition. I. Simulation of lateralization for stationary signals. J. Acoust. Soc. Am., 80(6):1608-1622, 1986. [ bib ] |
[1113] | D. J. Linden. The return of the spike: postsynaptic action potentials and the induction of ltp and ltd. Neuron, 22:661-666, 1999. [ bib ] |
[1114] | B. Lindner and L. Schimansky-Geier. Transmission of noise coded versus additive signals through a neuronal ensemble. Physical Review Letters, 86:2934-2937, 2001. [ bib ] |
[1115] | B. A. Linkenhofer and E. I. Knudsen. Incremental training increases the plasticity of the auditory space map in adult barn owls. Nature, 2002. [ bib ] |
[1116] | R. Linsker. A local learning rule that enables information maximization for arbitrary input distribution. Neural Computation, 9:1661-1665, 1997. [ bib ] |
[1117] | R. Linsker. Deriving receptive fields using an optimal encoding criterion. In Advances in Neural Information Processing Systems, volume 5, pages 953-960, San Mateo CA, 1993. Morgan Kaufmann. [ bib ] |
[1118] | R. Linsker. Local synaptic learning rules suffice to maximize mutual information in a linear network. Neural Computation, 4:691-702, 1992. [ bib ] |
[1119] | R. Linsker. How to generate ordered maps by maximizing the mutual information between input and output signals. Neural Computation, 1(3):402-411, 1989. [ bib ] |
[1120] | R. Linsker. Self-organization in a perceptual network. Computer, 21:105-117, 1988. [ bib ] |
[1121] | R. Linsker. From basic network principles to neural architecture: emergence of spatial-opponent cells. Proc. Natl. Acad. Sci. USA, 83:7508-7512, 1986. [ bib ] |
[1122] | R. Linsker. From basic network principles to neural architecture: emergence of orientation selective cells. Proc. Natl. Acad. Sci. USA, 83:8390-8394, 1986. [ bib ] |
[1123] | R. Linsker. From basic network principles to neural architecture: emergence of orientation columns. Proc. Natl. Acad. Sci. USA, 83:8779-8783, 1986. [ bib ] |
[1124] | R. P. Lippmann. Advances in Neural Information Processing Systems, volume 3. Morgan Kaufmann Publishers, San Mateo, 1991. [ bib ] |
[1125] | J. Lisman. The theta/gamma discrete phase code occuring during the hippocampal phase precession may be a more general brain coding scheme. Hippocampus, 15(7):913-922, 2005. [ bib | DOI | http ] |
[1126] | J. Lisman. Long-term potentiation: outstanding questions and attempted synthesis. Phil. Trans. R. Soc. Lond B: Biological Sciences, 358:829 - 842, 2003. [ bib ] |
[1127] | J. Lisman. Relating hippocampal circuitry to function: recall of memory sequences by reciprocal dentate-ca3 interactions. Neuron, 22:233-242, 1999. [ bib ] |
[1128] | J. Lisman. A mechanism for hebb and anti-hebb processes underlying learning and memory. Proc. Natl. Acad. Sci. USA, 86:9574-9578, 1989. [ bib ] |
[1129] | J. Lisman and N. Spruston. Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity. Nature Neuroscience, 8(7):839-841, 2005. [ bib ] |
[1130] | J. Lisman and A. Zhabotinsky. A model of synaptic memory: A CaMKII/PP1 switch that potentiates transmission by organizing an AMPA receptor anchoring assembly. Neuron, 31:191-201, 2001. [ bib ] |
[1131] | W. A. Little. The existence of persistent states in the brain. Math. Biosc., 19:101-120, 1974. [ bib ] |
[1132] | W. A. Little and G. L. Shaw. Analytical study of the memory storage capacity of a neural network. Math. Biosc., 39:281-290, 1978. [ bib ] |
[1133] | Z. Liu. Perceptual learning in motion discrimination that generalizes across motion directions. PNAS, 96(24):14085-14087, 1999. [ bib ] |
[1134] | J. Livet, T. A. Weissman, H. Kang, R. W. Draft, J. Lu, R. A. Bennis, J. R. Sanes, and J. W. Lichtman. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervour system. Nature, 450(7166):56-63, Nov 2007. [ bib ] |
[1135] | T. Ljunberg and P. A. amd W. Schultz. Responses of monkey dopamine neurons during learning of behavioral interactions. J. Neurophysiol., 67:145-163, 1992. [ bib ] |
[1136] | I. Llano, A. Marty, C. M. Armstrong, and A. Konnerth. Synaptic- and agonist-induced excitatory currents of purkinje cells in rat cerebellar slices. J Physiol, 434:183-213, 1991. [ bib ] |
[1137] | M. London and M. Häusser. Dendritic computation. Annual Reviews of Neuroscience, 28:503-532, 2005. [ bib ] |
[1138] | M. London and I. Segev. Synaptic scaling in vitro and in vivo. Nature Neuroscience, 4:853-854, 2001. [ bib ] |
[1139] | M. London, I. Segev, J. C. Magee, and E. P. Cook. Synaptic scaling in vivo and in vitro. Nature Neuroscience, 4(9):853-855, 2001. [ bib ] |
[1140] | A. Longtin. Stochastic resonance in neuron models. J. Stat. Phys., 70:309-327, 1993. [ bib ] |
[1141] | A. Losonczy, J. K. Makara, and J. C. Magee. Compartmentalized dendritic plasticity and input feature storage in neurons. Nature, 452(7186):436-441, 2008. [ bib | DOI ] |
[1142] | J. Lu, C. Li, J.-P. Zhao, M. ming Poo, and X. Zhang. Spike-timing-dependent plasticity of neocortical excitatory synapses on ibhibitory interneurons depends on target cell type. J. Neuroscience, 27:9711-9720, 2007. [ bib ] |
[1143] | J. Lu, C. Li, J. Zhao, M. Poo, and X. Zhang. Spike-timing-dependent plasticity of neocortical excitatory synapses on inhibitory interneurons depends on target cell type. Journal of Neuroscience, 27(36):9711, 2007. [ bib ] |
[1144] | N. Ludvig, H. Tang, B. Gohil, and J. Botero. Detecting location-specific neuronal firing rate increases in the hippocampus of freely-moving monkeys. Brain Research, 1014(1-2):97-109, 2004. [ bib ] |
[1145] | G. Luksys, J. Knuesel, D. Sheynikhovich, C. Sandi, and W. Gerstner. Effects of stress and genotype on meta-parameter dynamics in reinforcement learning. Advances in Neural Information Processing Systems 19, 19:937-944, 2007. [ bib ] |
[1146] | W. W. Lytton and T. J. Sejnowsky. Simulations of cortical pyramidal neurons synchronized by inhibitory interneurons. J. Neurophysiology, 66:1059-1079, 1991. [ bib ] |
[1147] | A. Lörincz and G. Buzsáki. Two-phase computational model training long-term memories in the entorhinal-hippocampal region. Annals of the New York Academy of Sciences, 911:83-111, 2000. [ bib ] |
[1148] | T. S. M. Hasler. Chaos communication over noisy channels. Intl. Journal of Bifurcations and Chaos, 10:719-736, 2000. [ bib ] |
[1149] | M. London, A. Schreibman, M. Häusser, M. E. Larkum, and I. Segev. The information efficacy of a synapse. Nature Neuroscience, 4:332-340, 2002. [ bib ] |
[1150] | M. P. Kilgard, P. K. Pandya, N. D. Engineer, and R. Moucha. Cortical network reorganization guided by sensory input features. Biol. Cybernetics, 87:333-343, 2002. [ bib ] |
[1151] | M. Müller and R. Wehner. Path integration in desert ants, Cataglyphis fortis. Proc. Nat. Acad. Sci., 85d:5287-5290, 1988. [ bib ] |
[1152] | B. Müller and J. Reinhard. Neural networks: An introduction. Springer-Verlag, Berlin Heidelberg New York, 1991. [ bib ] |
[1153] | W. Maas and T. Nathschläger. A model for fast analog computation based on unreliable synapses. Neural Computation, 12:1679-1704, 2000. [ bib ] |
[1154] | W. Maass. On the computational complexity of networks of spiking neurons. In xxx, editor, Advancs in Neural Information Processing Systems 7, pages 183-190. MIT-Press, 1995. [ bib ] |
[1155] | W. Maass. Computing with spiking neurons. In W. Maass and C. Bishop, editors, Pulsed Neural Networks, chapter 2, pages 55-85. MIT-Press, 1998. [ bib ] |
[1156] | W. Maass. Lower bounds for the computational power of spiking neurons. Neural Comput., 8:1-40, 1996. [ bib ] |
[1157] | W. Maass and C. Bishop. Pulsed Neural Networks. MIT-Press, 1998. [ bib ] |
[1158] | W. Maass, T. Natschläger, and H. Markram. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation, pages 2531-2560, 2002. [ bib ] |
[1159] | R. J. MacGregor. Neural and Brain Modeling. Academic Press, San Diego, 1987. [ bib ] |
[1160] | R. J. MacGregor and R. M. Oliver. A model for repetitive firing in neurons. Kybernetik, 16:53-64, 1974. [ bib ] |
[1161] | D. J. C. MacKay and K. D. Miller. Analysis of linsker's application of hebbian rules to linear networks. Network, 1:257-297, 1990. [ bib ] |
[1162] | D. M. MacKay and W. S. McCulloch. The limiting information capacity of a neuronal link. Bull. of Mathm. Biophysics, 14:127-135, 1952. [ bib ] |
[1163] | N. J. Mackintosh. A theory of attention: variations in the associability of stimulus with reinforcement. Psychol. Rev., 82:276-298, 1975. [ bib ] |
[1164] | J. C. Magee. Dendritic lh normalizes temporal summation in hippocampal ca1 neurons. Nat Neurosci, 2(6):508-514, 1999. [ bib | DOI ] |
[1165] | J. C. Magee and E. P. Cook. Somatic epsp amplitude is independent of synapse location in hippocampal pyramidal neurons. Nature Neuroscience, 3:895-903, 2000. [ bib ] |
[1166] | J. C. Magee and E. P. Cook. Somatic EPSP amplitude is independent of synapse location in hippocampal pyramidal neurons. Nature Neuroscience, 3(9):895-903, 2000. [ bib ] |
[1167] | J. C. Magee and D. Johnston. Plasticity of dendritic function. Current Opinion in Neurobiology, 15:334-342, 2005. [ bib ] |
[1168] | J. C. Magee and D. Johnston. A synaptically controlled associative signal for hebbian plastiticy in hippocampal neurons. Science, 275:209-213, 1997. [ bib ] |
[1169] | J. C. Magee and D. Johnston. A synaptically controlled, associative signal for hebbian plasticity in hippocampal neurons. Science, 275:209-213, 1997. [ bib ] |
[1170] | J. R. Magnus and H. Neudecker. Matrix Differential Calculus with Applications in Statistics and Econometrics. Wiley, New York, 1988. [ bib ] |
[1171] | E. Maguire, N. Burgess, J. Donnet, R. J. Frackowiak, C. Frith, and J. O'Keefe. Knowing where and getting there: A human navigation network. Science, 280:921-924, 1998. [ bib ] |
[1172] | Z. F. Mainen, J. Joerges, J. R. Huguenard, and T. J. Sejnowski. A model of spike initiation in neocortical pyramidal neurons. Neuron, 15(6):1427-1439, 1995. [ bib ] |
[1173] | Z. F. Mainen and T. J. Sejnowski. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382:363-366, 1996. [ bib ] |
[1174] | Z. F. Mainen and T. J. Sejnowski. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382(6589):363-366, 1996. [ bib | DOI ] |
[1175] | Z. F. Mainen and T. J. Sejnowski. Reliability of spike timing in neocortical neurons. Science, 268:1503-1506, 1995. [ bib ] |
[1176] | Z. F. Mainen and T. J. Sejnowski. Reliability of spike timing in neocortical neurons. Science, 268(5216):1503-1506, 1995. [ bib ] |
[1177] | Y. Maistrenko, O. Popovych, and M. Hasler. On strong and weak chaotic partial synchronization. Journal of Bifurcations and Chaos, 10:179-203, 2000. [ bib ] |
[1178] | R. Malenka and R. Nicoll. Long-Term Potentiation-A Decade of Progress? Science, 285(5435):1870-1874, 1999. [ bib ] |
[1179] | R. C. Malenka and M. F. Bear. LTP and LTD: An embarassment of riches. Neuron, 44:5-21, 2004. [ bib ] |
[1180] | R. C. Malenka, J. A. Kauer, D. J. Perkel, M. D. Mauk, and P. T. Kelly. An essential role for postsynaptic calmodulin and protein kinase activity in long-term potentiation. Nature, 340:554-557, 1989. [ bib ] |
[1181] | R. C. Malenka, J. Kauer, R. Zucker, and R. A. Nicoll. Postsynaptic calcium is sufficient for potentiation of hippocampal synaptic transmission. Science, 242:81-84, 1988. [ bib ] |
[1182] | R. C. Malenka, B. Lancaster, and R. S. Zucker. Temporal limits on the rise in postsynaptic calcium required for the induction of long-term potentiation. Neuron, 9:121-128, 1992. [ bib ] |
[1183] | R. C. Malenka and R. A. Nicoll. Long-term potentiation-a decade of progress? Science, 285:1870-1874, 1999. [ bib ] |
[1184] | R. C. Malenka and R. A. Nicoll. Nmda-receptor-dependent plasticity: multiple forms and mechanisms. Trends Neurosci., 16:480-487, 1993. [ bib ] |
[1185] | R. Malinow. Ampa receptor trafficking and long-term potentiation. Phil. Trans. R. Soc. Lond B: Biological Sciences, 358:707 - 714, 2003. [ bib ] |
[1186] | R. Malinow and J. P. Miller. Synaptic hyperpolarization during conditioning reversibly blocks induction of long-term potentiation. Nature, 320:529-530, 1986. [ bib ] |
[1187] | R. Malinow, H. Schulman, and R. W. Tsien. Inhibition of postsynaptic PKC or CaMKII blocks induction but not expression of ltp. Science, 245:862-866, 1989. [ bib ] |
[1188] | H. A. Mallot and S. Gillner. Route navigation without place recognition: What is recognized in recognition-triggered responses? Perception, 29:43-45, 2000. [ bib ] |
[1189] | C. von der Malsburg. The correltaion theory of brain function. In E. Domany, J. L. van Hemmen, and K. Schulten, editors, Models of neural networks II, pages 95-119, New York, 1994. Springer-Verlag. [ bib ] |
[1190] | C. von der Malsburg. Am i thinking assemblies? In G. Palm and A. Aertsen, editors, Brain theory, pages 161-176, Berlin Heidelberg New York, 1986. Springer-Verlag. [ bib ] |
[1191] | C. von der Malsburg. The correlation theory of brain function. Internal Report 81-2, MPI für Biophysikalische Chemie, Göttingen, 1981. Reprinted in Models of Neural Networks II, Domany et al. (Eds.), Springer, 1994, pp.95-119. [ bib ] |
[1192] | C. von der Malsburg. Self-organization of orientation selective cells in the striate cortex. Kybernetik, 14:85-100, 1973. [ bib ] |
[1193] | C. von der Malsburg and J. Buhmann. Sensory segmentation with coupled neural oscillators. Biol. Cybern., 67:233-242, 1992. [ bib ] |
[1194] | C. von der Malsburg and W. Schneider. A neural cocktail-party processor. Biol. Cybern., 54:29-40, 1986. [ bib ] |
[1195] | P. B. Manis and S. O. Marx. xxx. J. Neurosci., 11:2865-2800, 1991. [ bib ] |
[1196] | A. Manwani and C. Koch. Detecting and estimating signals in noisy cable structures, I: Neuronal noise sources. Neural Computation, 11:1797-1829, 1999. [ bib ] |
[1197] | A. Manwani and C. Koch. Detecting and estimating signals in noisy cable structures, II: Information theoretical analysis. NEURAL COMPUTATION, 11:1831-1873, 1999. [ bib ] |
[1198] | E. J. Mar, C. C. Chow, W. Gerstner, R. Adams, and J.J.Collins. Noise-shaping in populations of coupled model neurons. Proc. Natl. Acad. Sci. USA, 96:10450-10455, 1999. [ bib ] |
[1199] | C. M. Marcus and R. M. Westervelt. Stability of analog neural networks with delays. Phys. Rev. A, 39:347-359, 1989. [ bib ] |
[1200] | C. M. Marcus and R. M. Westervelt. Dynamics of iterated map networks. Phys. Rev. A, 40:501-504, 1989. [ bib ] |
[1201] | H. Markram. The blue brain project. Nat Rev Neurosci, 7(2):153-160, 2006. [ bib | DOI ] |
[1202] | H. Markram, J. Lübke, M. Frotscher, and B. Sakmann. Regulation of synaptic efficacy by coincidence of postysnaptic AP and EPSP. Science, 275:213-215, 1997. [ bib ] |
[1203] | H. Markram, J. Lübke, M. Frotscher, and B. Sakmann. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 275:213-215, 1997. [ bib ] |
[1204] | H. Markram, J. Lübke, M. Frotscher, and B. Sakmann. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs. Science, 275(5297):213-215, 1997. [ bib ] |
[1205] | H. Markram and B. Sakmann. Action potentials propagating back into dendrites trigger changes in efficacy of single-axon synapses between layer v pyramidal neurons. Soc. Neurosci. Abstr., 21(3):2007, 1995. [ bib ] |
[1206] | H. Markram, M. Toledo-Rodrgiguez, Y. Wang, A. Gupta, G. Silberberg, and C. Wu. Interneurons of the neocortical inhibitory system. Nature Review Neuroscienc, 5:793-807, 2004. [ bib ] |
[1207] | H. Markram, M. Toledo-Rodriguez, Y. Wang, A. Gupta, G. Silberberg, and C. Wu. Interneurons of the neocortical inhibitory system. Nat Rev Neurosci, 5(10):793-807, 2004. [ bib | DOI ] |
[1208] | H. Markram and M. Tsodyks. The information content of action potential trains: a synaptic basis. In W. Gerstner, A. Germond, M. Hasler, and J. d. Nicoud, editors, Artificial Neural Networks - ICANN'97, Lecture Notes in Computer Science, 1327. Springer, 1997. [ bib ] |
[1209] | H. Markram and M. Tsodyks. Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature, 382:807-810, 1997. [ bib ] |
[1210] | H. Markram and M. Tsodyks. Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature, 382:807-810, 1996. [ bib ] |
[1211] | H. Markram, Y. Wu, and M. Tosdyks. Differential signaling via the same axon of neocortical pyramidal neurons. Proc. Natl. Acad. Sci. USA, 95:5323-5328, 1998. [ bib ] |
[1212] | E. J. Markus, Y. L. Qin, B. Leonard, W. E. Skaggs, B. L. McNaughton, and C. A. Barnes. Interactions between location and task affect the spatial and directional firing of hippocampal neurons. Journal of Neuroscience, 15(11):7079-7094, 1995. [ bib ] |
[1213] | P. Marsalek, C. Koch, and J. Maunsell. On the relationship between synaptic input and spike output jitter in individual neurons. Proc. Natl. Acad. Sci. USA, 94:735-740, 1997. [ bib ] |
[1214] | S. Martinez-Conde, S. L. Macknik, and D. H. Hubel. The role of fixational eye movements in visual perception. Nature Reviews Neuroscience, 5(3):229-240, Mar 2004. [ bib | http ] |
[1215] | M. Mascaro and D. J. Amit. Effective neural response function for collective population states. Network, 10:351-373, 1999. [ bib ] |
[1216] | T. Masquelier, R. Guyonneau, and S. J. Thorpe. Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE, 3(1):e1377, 2008. [ bib | DOI | http ] |
[1217] | N. Masuda and H. Kori. Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity. Journal of Computational Neuroscience, 22(3):327-345, 2007. [ bib ] |
[1218] | J. Matas, M. Hamouz, K. Jonsson, J. Kittler, Y. Li, C. Kotropoulos, A. Tefas, I. Pitas, T. Tan, H. Yan, F. Smeraldi, J. Bigün, N. Capdevielle, W. Gerstner, S. Ben-Yacoub, Y. Abdeljaoued, and E. Mayoraz. Comparison of face verification results on the XM2VTS database. In Proceedings of the 15th International Conference on Pattern Recognition, Barcelona (Spain), September 2000, volume 4, pages 858-863. IEEE Comp. Soc. Order No. PR00750, September 2000. [ bib ] |
[1219] | G. Mato and H. Sompolinski. Neural network models of perceptual learning of angle discrimination. Neural Computation, 8:270-299, 1996. [ bib ] |
[1220] | N. Matsumoto and M. Okada. Self-regulation mechanism of temporally asymmetric Hebbian plasticity. Neural Computation, 14:2883-2902, 2002. [ bib ] |
[1221] | N. Matthews, Z. Liu, B. J. Geesaman, and N. Qyan. Perceptual learning on orientation and direction discrimination. Vision Research, 39:3692-3701, 1999. [ bib ] |
[1222] | N. Matthews, Z. Liu, and N. Qian. The effect of orientation learning on contrast sensitivity. Vision Research, 41:463-471, 2001. [ bib ] |
[1223] | N. Matthews and L. Welch. Velocity-dependent improvements in single-dot direction discrimination. Perception and Psychophysics, 59:60-72, 1997. [ bib ] |
[1224] | M. Mattia and P. Del Giudice. On the population dynamics of interacting spiking neurons. Phys. Rev. E, xx:xx, 2002. [ bib ] |
[1225] | M. Mattia and P. Del Guidice. Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses. Neural Computation, 12:2305-2329, 2000. [ bib ] |
[1226] | A. Mauro, F. Conti, F. Dodge, and R. Schor. Subthreshold behavior and phenomenological impedance of the squid giant axon. J Gen Physiol, 55(4):497-523, 1970. [ bib ] |
[1227] | M. L. Mayer, G. L. Westbrook, and P. B. Guthrie. Voltage-dependent block by Mg2+ of nmda responses in spinal cord neurones. Nature, 309:261-263, 1984. [ bib ] |
[1228] | J. Mayor and W. Gerstner. Online processing of multiple inputs in a sparsely-connected recurrent neural network. In O. Kaynak, E. Alpaydin, E. Oja, and L. Xu, editors, Proceedings of the Joint International Conference ICANN/ICONIP 2003, pages 839-845, Heidelberg, 2003. Springer-Verlag. [ bib ] |
[1229] | J. Mayor and W. Gerstner. Signal buffering in random networks of spiking neurons: microscopic vs. macroscopic phenomena. Phys. Rev. E, 72:051906, 2005. [ bib ] |
[1230] | J. Mayor and W. Gerstner. Transient information flow in a network of excitatory and inhibitory model neurons: role of noise and signal autocorrelation. Journal of Physiology (Paris), 98:417-428, 2004. [ bib ] |
[1231] | J. Mayor and W. Gerstner. Noise-enhanced computation in a model of a cortical column. Neuroreport, 16:1237-1240, 2004. [ bib ] |
[1232] | M. Mazurek and M. Shadlen. Limits to the temporal fidelity of cortical spike rate signals. Nature Neuroscience, 5:463-471, 2002. [ bib ] |
[1233] | J. L. McClelland, B. L. O'Reilly, and R. C. McNaughton. Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3):419-457, 1995. [ bib ] |
[1234] | D. A. McCormick. Membrane properties and neurotransmitter actions. In S. GM, editor, The synaptic organization of the brain., Oxford, 1990. Oxford Univ. Press. [ bib ] |
[1235] | D. A. McCormick, Z. Wang, and J. Huguenard. Neurotransmitter control of neocortical neuronal activity and excitability. Cereb Cortex, 3(5):387-398, 1993. [ bib ] |
[1236] | D. McCormick, Y. Shu1, and Y. Yu. Neurophysiology: Hodgkin and huxley model ? still standing? Nature, 445:E1-E2, 2007. [ bib ] |
[1237] | W. S. McCulloch and W. Pitts. A logical calculus of ideas immanent in nervous activity. Bulletin of mathematical Biophys., 5:115-133, 1943. [ bib ] |
[1238] | T. McLaughlin and D. O'Leary. Molecular gradients and development of retinotopic maps. Annual Reviews of Neuroscience, 28:327-355, 2005. [ bib ] |
[1239] | B. McNamara and K. Wiesenfeld. Theory of stochastic resonance. Physical Review A, 39:4854-4869, 1989. [ bib ] |
[1240] | B. McNaughton, C. Barnes, J. Gerrard, K. Gothard, M. Jung, J. Knierim, H. Kudrimoti, Y. Qin, W. Skaggs, M. Suster, and K. Weaver. Deciphering the hippocampal polyglot: the hippocampus as a path integration system. J. Experimental Biology, 199:173-185, 1996. [ bib ] |
[1241] | B. McNaughton, C. Barnes, J. Gerrard, K. Gothard, M. Jung, J. Knierim, H. Kudrimoti, Y. Qin, W. Skaggs, M. Suster, and K. Weaver. Deciphering the hippocampal polyglot: the hippocampus as a path integration system. Journal of Experimental Biology, 199(1):173-85, 1996. [ bib ] |
[1242] | B. McNaughton, C. Barnes, and J. O'Keefe. The contributions of position, direction, and velocity to single unit activity in the hippocampus of freely-moving rats. Experimental Brain Research, 52:41-49, 1983. [ bib ] |
[1243] | B. McNaughton, L. Chen, and E. Markus. Dead reckoning, landmark learning, and the sense of direction: A neurophysiological and computational hypothesis. Journal of Cognitive Neuroscience, 3:190, 1991. [ bib ] |
[1244] | B. L. McNaughton, F. P. Battaglia, O. Jensen, E. I. Moser, and M. B. Moser. Path integration and the neural basis of the 'cognitive map'. Nature Reviews Neuroscience, 7:663-678, 2006. [ bib ] |
[1245] | W. H. Mehaffey, B. Doiron, L. Maler, and R. W. Turner. Deterministic multiplicative gain control with active dendrites. Journal of Neuroscience, 25(43):9968-9977, 2005. [ bib ] |
[1246] | M. Mehta, M. Quirk, and M. Wilson. Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron, 25(3):707-715, 2000. [ bib ] |
[1247] | M. Mehta and M. Wilson. From hippocampus to v1: effect of ltp on spatio-temporal dynamics of receptive fields. Neurocomputing, 32:905-911, 2000. [ bib ] |
[1248] | M. R. Mehta. Neuronal dynamics of predictive coding. Neuroscientist, 7:490-495, 2001. [ bib ] |
[1249] | M. R. Mehta, C. A. Barnes, and B. L. McNaughton. Experience-dependent, asymmetric expansion of hippocampal place fields. PNAS, 94(16):8918-8921, 1997. [ bib | DOI | http ] |
[1250] | M. R. Mehta, A. K. Lee, and M. A. Wilson. Role of experience of oscillations in transforming a rate code into a temporal code. Nature, 417:741-746, 2002. [ bib ] |
[1251] | M. R. Mehta, M. Quirk, and M. Wilson. Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron, 25:707-715, 2000. [ bib ] |
[1252] | B. W. Mel. What the synapse tells the neuron. Science, 295:1845-1846, 2002. [ bib ] |
[1253] | B. W. Mel. Information processing in dendritic trees. Neural Comput., 6(1031-1085), 1994. [ bib ] |
[1254] | O. Melamed, W. Gerstner, W. Maass, M. Tsodyks, and H. Markram. Coding and learning of behavioral sequences. Trends in Neurosciences, 27:11-14, 2004. [ bib ] |
[1255] | O. Melamed, W. Gerstner, W. Maass, M. Tsodyks, and H. Markram. Coding and learning of behavioral sequences. Trends Neurosci, 27(1):11-14, 2004. [ bib ] |
[1256] | W. H. Merigan. Cortical area V4 is critical for certain texture discriminations, but this effect is not dependent on attention. Visual Neuroscience, 17(6):949-958, Nov. 2000. [ bib ] |
[1257] | M. Merzenich, R. Nelson, M. Stryker, M. Cynader, A. Schoppmann, and J. Zook. Somatosensory cortical map changes following digit amputation in adult monkeys. J. Comparative Neurology, 224:591-605, 1984. [ bib ] |
[1258] | C. Meunier and I. Segev. Playing the devil's advocate: is the hodgkin-huxley model useful? TRENDS IN NEUROSCIENCES, 25:558-563, 2002. [ bib ] |
[1259] | C. Meyer and C. van Vreeswijk. Temporal correlations in stochastic networks of spiking neurons. Neural Computation, 14:369-404, 2002. [ bib ] |
[1260] | M. Mezard and G. Parisi. Mean-field theory of randomly frustrated systems with finite connectivity. Europhys. Lett., 3:1067-1074, 1987. [ bib ] |
[1261] | J. C. Middlebrooks, A. E. Clock, L. Xu, and D. M. Green. A panoramic code for sound localization by cortical neurons. Science, 264:842-844, 1994. [ bib ] |
[1262] | A. A. Middleton and C. Tang. Self-organized criticality in nonconserved systems. Phys. Rev. Lett., 74:742-745, 1995. [ bib ] |
[1263] | M. Migliore and G. M. Shepherd. Emerging rules for the distributions of active dendritic conductances. Nat Rev Neurosci, 3(5):362-370, 2002. [ bib | DOI ] |
[1264] | R. Miikkulainen. Self-organizing process based on lateral inhibition and synaptic resource redistribution. In T. Kohonen, K. Mäkisara, . Simula, and J. Kangas, editors, Proceedings of the 1991 International Conference on Artificial Neural Networks, pages 415-420, Amsterdam: North-Holland, 1991. [ bib ] |
[1265] | R. Miikkulainen, J. A. Bednar, Y. Choe, and J. Sirosh. Self-organization, plasticity, and low-level visual phenomena in a laterally connected map model of the primary visual cortex. In R. L. Goldstone, P. G. Schyns, and D. L. Medin, editors, Perceptual Learning, volume 36 of Psychology of Learning and Motivation, pages 257-308. Academic Press, San Diego, CA, 1997. [ bib ] |
[1266] | R. Miikkulainen, J. A. Bednar, Y. Choe, and J. Sirosh. Computational maps in the visual cortex. New York: Springer, 2005. [ bib ] |
[1267] | J. Millán, F. Renkens, J. Mouriño, and W. Gerstner. Non-invasive brain-actuated control of a mobile robot. In Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1121-1126, Acapulco, Mexico, 2003. [ bib ] |
[1268] | J. del R. Millan, F. Renkens, J. Mourino, and W. Gerstner. Non-invasive brain actuated control of a mobile robot by human eeg. IEEE Transactions on Biomedical Engineering, 51:1026-1033, 2004. [ bib ] |
[1269] | J. del R. Millan, F. Renkens, J. Mourino, and W. Gerstner. Brain-actuated interaction. Artificial Intelligence, 159:241-259, 2004. [ bib ] |
[1270] | G. Miller. The magical number seven plus minus two. Psych. Rev., 63:81-97, 1956. [ bib ] |
[1271] | K. Miller, J. B. Keller, and M. P. Stryker. Ocular dominance column development: analysis and simulation. Science, 245:605-615, 1989. [ bib ] |
[1272] | K. D. Miller. Receptive fields and maps in the visual cortex: Models of ocular dominance and orientation columns. In E. Domany, J. L. van Hemmen, and K. Schulten, editors, Models of neural networks III, pages 55-78. Springer, New York, 1995. [ bib ] |
[1273] | K. D. Miller. A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity dependent competition between ON- and OFF-center inputs. J. Neurosci., 14:409-441, 1994. [ bib ] |
[1274] | K. D. Miller. Derivation of linear hebbian equations from a nonlinear model of synaptic plasticity. Neural Computation, 2:321-333, 1990. [ bib ] |
[1275] | K. D. Miller and D. J. C. MacKay. The role of constraints in hebbian learning. Neural Computation, 6:100-126, 1994. [ bib ] |
[1276] | M. I. Miller and K. Mark. A statistical study of cochlear nerve discharge patterns in reponse to complex speech stimuli. J. Acoust. Soc. Am., 92:202-209, 1992. [ bib ] |
[1277] | R. Miller and J. R. Wickens. Reward as fulfillement of motor intentions: a unifying concept for the function of mammalian striatum. Int. J. Neurosci., 46:23-24, 1989. [ bib ] |
[1278] | P. Milner. A model for visual shape recognition. Psychol. Rev., 81:521-535, 1974. [ bib ] |
[1279] | J. G. Milton, P. H. Chu, and J. D. Cowan. Spiral waves in integrate-and-fire neural networks. In J. D. Cowan and C. L. Giles, editors, Advances in Neural Information Processing Systems 5, pages 1001-1006. Morgan Kaufmann, San Mateo, CA, 1993. [ bib ] |
[1280] | A. Minai. Covariance learning of correlated patterns in competitive networks. Neural Computation, 9:667-681, 1997. [ bib ] |
[1281] | A. Minai and W. B. Levy. Sequence learning in a single trial. In Proc. of the INNS World Congress on Neural Networks, Portland, Oregon, Vol. II, pages 505-508. International Neural Network Society,, 1993. [ bib ] |
[1282] | M. L. Minsky and S. A. Papert. Perceptrons. MIT Press, Cambridge Mass., 1969. [ bib ] |
[1283] | J. Mirenowicz and W. Schultz. Preferential activation of midbrain dopamine neurons by appetitie rather than aversive stimuli. Nature, 379:449-451, 1996. [ bib ] |
[1284] | R. E. Mirollo and S. H. Strogatz. Synchronization of pulse coupled biological oscillators. SIAM J. Appl. Math., 50:1645-1662, 1990. [ bib ] |
[1285] | G. Mitchison. Removing time variation with the anti-Hebbian differential synapse. Neural Computation, 3:312-320, 1991. [ bib ] |
[1286] | Y. Miyashita. Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature, 335:817-820, 1988. [ bib ] |
[1287] | Y. Miyashita. Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature, 335(6193):817-820, Oct 1988. [ bib ] |
[1288] | A. Moiseff and M. Konishi. The owls interaural pathway is not involved in sound localization. J. Comp. Physiol., 144:299-304, 1981. [ bib ] |
[1289] | L. Molgedey and H. G. Schuster. Separation of a mixture of independent signals using time delayed correlations. Physical Review Letters, 72(23):3634-3637, 1994. [ bib ] |
[1290] | F. Mondada and D. Floreano. Evolution of neural control structures: some experiments on mobile robots. Robotics and Autonomous systems, 16:183-195, 1995. [ bib ] |
[1291] | P. Montague, P. Dayan, C. Person, and T. Sejnowski. Bee foraging in uncertain environments using predictive hebbian learning. Nature, 377:725-728, 1995. [ bib ] |
[1292] | P. Montague, P. Dayan, and T. Sejnowski. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16:1936-1947, 1996. [ bib ] |
[1293] | J. E. Moody. Advances in Neural Information Processing Systems, volume 4. Morgan Kaufmann Publishers, San Mateo, 1992. [ bib ] |
[1294] | G. Moore, J. Segundo, D. Perkel, and H. Levitan. Statistical signs of synaptic interaction in neurons. Biophysical Journal, 10:876-900, 1970. [ bib ] |
[1295] | R. Moreno, J. de la Rocha, A. Renart, and N. Parga. Response of Spiking Neurons to Correlated Inputs. Physical Review Letters, 89(28):288101, 2002. [ bib ] |
[1296] | R. Moreno-Bote and N. Parga. Auto-and Crosscorrelograms for the Spike Response of Leaky Integrate-and-Fire Neurons with Slow Synapses. Physical Review Letters, 96(2):28101, 2006. [ bib ] |
[1297] | R. Moreno-Bote and N. Parga. Role of Synaptic Filtering on the Firing Response of Simple Model Neurons. Physical Review Letters, 92(2):28102, 2004. [ bib ] |
[1298] | R. Moreno-Bote and N. Parga. Role of synaptic filtering on the firing response of simple model neurons. Physical Review Letters, 92:28102, 2004. [ bib ] |
[1299] | C. Morris and H. Lecar. Voltage oscillations in the barnacle giant muscle fiber. Biophys. J., 35:193-213, 1981. [ bib ] |
[1300] | R. Morris. Theories of hippocampal function. In The hippocampus book, pages 581-713. Oxford university press, 2007. [ bib ] |
[1301] | R. Morris. Long-term potentiation and memory. Phil. Trans. R. Soc. Lond B: Biological Sciences, 358:643 - 647, 2003. [ bib ] |
[1302] | R. Morris, P. Garrard, J. Rawlins, and J. O'Keefe. Place navigation impaired in rats with hippocampal lesions. Nature, 297:681-683, 1982. [ bib ] |
[1303] | R. Morris, E. I. Moser, G. Riedel, S. J. Martin, J. Sandin, M. Day, and C. O'Carrol. Elements of a neurobiological theory of the hippocampus: the role of activity-dependent synaptic plasticity in memory. Phil. Trans. R. Soc. Lond B: Biological Sciences, 358:773 - 786, 2003. [ bib ] |
[1304] | A. Morrison, A. Aertsen, and M. Diesmann. Spike-timing dependent plasticity in balanced random networks. Neural Computation, 19:1437-1467, 2007. [ bib ] |
[1305] | E. I. Moser, M. B. Moser, P. Pipa, M. Newton, F. P. C. A. Houston, C. A. Barnes, and B. L. McNaughton. A test of the reverberatory activity hypothesis for hippocampal place cells. Neuroscience, 130:519-526, 2005. [ bib ] |
[1306] | V. B. Mountcastle. Modality and topographic properties of single neurons of cat's somatosensory cortex. J. Neurophysiol., 20:408-434, 1957. [ bib ] |
[1307] | G. M. Muir and J. S. Taube. Head direction cell activity and behavior in a navigation task requiring a cognitive mapping strategy. Behavioral Brain Research, 153:249-253, 2004. [ bib ] |
[1308] | R. U. Muller. A quarter of a century of place cells. Neuron, 17:979-990, 1996. [ bib ] |
[1309] | R. U. Muller, E. Bostock, J. S. Taube, and J. L. Kubie. On the directional firing properties of hippocampal place cells. Journal of Neuroscience, 14(12):7235-7251, 1994. [ bib ] |
[1310] | R. U. Muller and J. L. Kubie. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. Journal of Neuroscience, 7(7):1951-1968, 1987. [ bib ] |
[1311] | R. U. Muller, J. L. Kubie, and J. B. Ranck Jr. Spatial firing patterns of hippocampal complex-spike cells in a fixed environment. Journal of Neuroscience, 7:1935-1950, 1987. [ bib ] |
[1312] | A. Murray and L. Tarassenko. Analogue Neural VLSI - a pulse-stream approach. Chapman Hall, London, 1994. [ bib ] |
[1313] | J. D. Murray. Mathematical Biology. Number 19 in Biomathematics Texts. Springer-Verlag, 2nd edition, 1993. [ bib ] |
[1314] | V. Murthy, T. Schikorski, C. Stevens, and Y. Zhu. Inactivity Produces Increases in Neurotransmitter Release and Synapse Size. Neuron, 32(4):673-682, 2001. [ bib ] |
[1315] | V. N. Murthy and E. E. Fetz. Coherent 25- to 35-hz oscillations in the sensorimotor cortex of awake behaving monkeys. Proc. Natl. Acad. Sci. USA, 89:5670-5674, 1992. [ bib ] |
[1316] | K. Nützel. The lenght of attractors in asymmetric random neural networks with deterministic dynamics. J. Phys. A.: Math. Gen., 24:L151-L157, 1994. [ bib ] |
[1317] | J.-P. Nadal and N. Parga. Redundancy reduction and independent component analysis: Conditions on cumulants and adaptive approaches. Neural Computation, 9:1421-1456, 1997. [ bib ] |
[1318] | J. Nagumo, S. Arimoto, and S. Yoshizawa. An active pulse transmission line simulating nerve axon. Proc. IRE, 50:2061-2070, 1962. [ bib ] |
[1319] | K. Nakazawa, T. J. McHugh, M. A. Wilson, and S. Tonegawa. NMDA receptors, place cells and hippocampal spatial memory. Nature Reviews. Neuroscience, 5(5):361-72, May 2004. [ bib ] |
[1320] | T. Natschlaeger and B. Ruf. Spatial and temporal pattern analysis via spiking neurons. Network: Computation in Neural Systems, 9:319-332, 1998. [ bib ] |
[1321] | B. Naundorf, T. Geisel, and F. Wolf. Action potential onset dynamics and the response speed of neuronal populations. J. Computational Neuroscience, 18:297-309, 2005. [ bib ] |
[1322] | B. Naundorf, F. Wolf, and M. Volgushev. Unique features of action potential initiation in cortical neurons. Nature, 440:1060-1063, 2006. [ bib ] |
[1323] | E. Nelson. Derivation of the Schroedinger Equation from Newtonian Mechanics. Physical Review, 150(4):1079-1085, 1966. [ bib ] |
[1324] | J. I. Nelson, P. A. Salin, M. H.-J. Munk, M. Arzi, and J. Bullier. Spatial and temporal coherence in cortico-cortical connections: A cross-correlation study in areas 17 and 18 in the cat. Visual Neuroscience, 9:21-37, 1992. [ bib ] |
[1325] | M. Nelson and J. Rinzel. The Hodgkin-Huxley model. In J. M. Bower and D. Beeman, editors, The book of Genesis, chapter 4, pages 27-51. Springer, New York, 1995. [ bib ] |
[1326] | S. B. Nelson, J. A. Varela, K. Sen, and L. Abbott. Functional significance of synaptic depression between cortical neurons. In J. Bower, editor, Computational Neuroscience - Trends in Research 1997, pages 429-434. Plenum Press, 1997. [ bib ] |
[1327] | L. Neltner, D. Hansel, G. Mato, and C. Meunier. Synchrony in heterogeneous networks of spiking neurons. Neural Computation, 12:1607-1641, 2000. [ bib ] |
[1328] | E. applications of Neural Networks. Special issue. In IEEE Transactions in Neural Networks, volume 8, pages 825-964, 1997. [ bib ] |
[1329] | H. Neven and A. Aertsen. Rate coherence and event coherence in the visual cortex: a neural model of object recognition. Biol. Cybern., 67:309-322, 1992. [ bib ] |
[1330] | T. Nevian, M. E. Larkum, A. Polsky, and J. Schiller. Properties of basal dendrites of layer 5 pyramidal neurons: a direct patch-clamp recording study. Nat Neurosci, 10(2):206-214, 2007. [ bib | DOI ] |
[1331] | T. Nevian and B. Sakmann. Spine ca2+ signaling in spike-timing-dependent plasticity. J. Neurosci., 26(43):11001-11013, 2006. [ bib | DOI | http ] |
[1332] | T. M. Newpher and M. D. Ehlers. Glutamate receptor dynamics in dendritic microdomains. Neuron, 58(4):472-497, May 2008. [ bib | DOI | http ] |
[1333] | A. Ngezahayo, M. Schachner, and A. Artola. Synaptic activation modulates the induction of bidirectional synaptic changes in adult mouse hippocamus. J. Neuroscience, 20:2451-2458, 2000. [ bib ] |
[1334] | J. G. Nicholls, A. R. Martin, and B. G. Wallace. From Neuron to Brain. Sinauer Associates, Inc., Sunderland, Massachusetts U.S.A., 3rd edition, 1992. [ bib ] |
[1335] | E. Niebur, D. M. Kammen, C. Koch, D. Rudermann, and H. G. Schuster. Phase-coupling in two dimensional networks of interacting oscillators. In R. P. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, pages 123-127, San Mateo CA, 1991. Morgan Kaufmann. [ bib ] |
[1336] | S. Nirenberg and P. Latham. Decoding neuronal spike trains: How important are correlations? Proc. Natl. Acad. Sci. USA, 100:7348-7353, 2003. [ bib ] |
[1337] | M. Nishiyama, K. Hong, K. M. nd M.M. Poo, and K. Kato. Calcium stores regulate the polarity and input specificity of synaptic modification. Nature, 408:584-588, 2000. [ bib ] |
[1338] | M. Nishiyama, K. Hong, K. Miskoshiba, M. Poo, and K. Kato. Calcium stores regulate the polarity and input specificity of synaptic modification. Nature, 208:584-588, 2000. [ bib ] |
[1339] | L. Nowak, P. Bregestovski, P. Asher, A. Herbet, and A. Prochiantz. Magnesium gates glutamate-activiated channels in mouse central neurons. Nature, 307:462-465, 1984. [ bib ] |
[1340] | T. Nowotny, A. Szucs, R. Levi, and A. I. Selverston. Models wagging the dog: are circuits constructed with disparate parameters? Neural Comput, 19(8):1985-2003, 2007. [ bib | DOI ] |
[1341] | D. Nykamp and D. Tranchina. A population density approach that facilitates large-scale modeling of neural networks: Analysis and application to orientation tuning. J. Computational Neuroscience, 8:19-50, 2000. [ bib ] |
[1342] | D. O'Connor, G. Wittenberg, and S. Wang. Dissection of Bidirectional Synaptic Plasticity Into Saturable Unidirectional Processes. Journal of Neurophysiology, 94(2):1565-1573, 2005. [ bib ] |
[1343] | D. H. O'Connor, G. M. Wittenberg, and S. S.-H. Wang. Graded bidirectional synaptic plasticity is composed of switch-like unitary events. PNAS, 102(26):9679-9684, 2005. [ bib ] |
[1344] | D. O'Connor, G. Wittenberg, and S.-H. Wang. Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proc. Natl. Acad. Sci. USA, 102:9679-9684, 2005. [ bib ] |
[1345] | J. O'Doherty, P. Dayan, K. Friston, H. Critchley, and R. Dolan. Temporal difference learning model accounts for responses in human ventral striatum and orbitofrontal cortex during pavlovian appetitive learning. Neuron, 38:329-337, 2003. [ bib ] |
[1346] | J. O'Doherty, P. Dayan, J. Schultz, R. Deischmann, K. Friston, and R. Dolan. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science, 304:452-454, 2004. [ bib ] |
[1347] | J. O'Keefe. Hippocampal neurophysiology in the behaving animal. In The hippocampus book, pages 475-548. Oxford university press, 2007. [ bib ] |
[1348] | J. O'Keefe and N. Burgess. Dual phase and rate coding in hippocampal place cells: theoretical significance and relationship to entorhinal grid cells. Hippocampus, 15:853-866, 2005. [ bib ] |
[1349] | J. O'Keefe and N. Burgess. Geometric determinants of the place fields of hippocampal= neurons. Nature, 381:425-428, 1996. [ bib ] |
[1350] | J. O'Keefe and J. Dostrovsky. The hippocampus as a spatial map: preliminary evidence from unit activity in the freely moving rat. Brain Research, 34:171-175, 1971. [ bib ] |
[1351] | J. O'Keefe and L. Nadel. The Hippocampus as a cognitive map. Clarendon Press, Oxford, 1978. [ bib ] |
[1352] | J. O'Keefe and L. Nada;. The hippocampus as a spatial map: preliminary evidence from unit activity in the freely-moving rat. Brain Res., 34:171-175, 1971. [ bib ] |
[1353] | R. C. O'Reilly and M. H. Johnson. Object recognition and sensitive periods: A computational analysis of visual imprinting. Neural Computation, 6(3):357-389, 1994. [ bib ] |
[1354] | R. C. O'Reilly and J. L. McClelland. Hippocampal conjunctive encoding, storage, and recall: avoiding a trade-off. Hippocampus, 4(6):661-682, Dec. 1994. [ bib ] |
[1355] | K. Obermayer, G. G. Blasdel, and K. Schulten. Statistical-mechanics analysis of self-organization and pattern formation during the development of visual maps. Phys. Rev. E, 45:7568-7589, 1992. [ bib ] |
[1356] | D. Oertel. Synaptic responses and electrical properties of cells in brain slices of the mouse anteroventral cochlear nucleus. The Journal of Neuroscience, 3(10):2043-2053, 1983. [ bib ] |
[1357] | E. Oja. A simplified neuron model as a principal component analyzer. J. Mathematical Biology, 15:267-273, 1982. [ bib ] |
[1358] | E. Oja. A simplified neuron as a principal component analyzer. Journal of Mathematical Biology, 15:267-273, 1982. [ bib ] |
[1359] | E. Oja and J. Karhunen. Signal separation by nonlinear Hebbian learning. Computational Intelligence: A Dynamic System Perspective, pages 83-97, 1995. [ bib ] |
[1360] | M. Okatan, M. Wilson, and E. Brown. Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Computation, 17:1927-1961, 2005. [ bib ] |
[1361] | Z. Olami, H. J. S. Feder, and K. Christensen. Self-organized criticality in a continuous, nonconservative cellular automaton modelling earthquakes. Phys. Rev. Lett., 68:1244-1247, 1992. [ bib ] |
[1362] | B. A. Olshausen. Principles of image representation in visual cortex. In L. Chalupa and J. Werner, editors, The Visual Neurosciences. MIT Press, 2003. [ bib ] |
[1363] | B. A. Olshausen and D. Field. Sparse coding of sensory inputs. Current Opinion in Neurobiology, 14(4):481-487, 2004. [ bib ] |
[1364] | B. A. Olshausen and D. J. Field. How Close Are We to Understanding V1? Neural Computation, 17:1665-1699, 2005. [ bib ] |
[1365] | B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37:3311-3325, 1997. [ bib ] |
[1366] | B. A. Olshausen and D. J. Field. Natural image statistics and efficient coding. Network: Computation in Neural Systems, 7:333-339, 1996. [ bib ] |
[1367] | B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381:607-609, 1996. [ bib ] |
[1368] | A. Omurtag, B. Knight, and L. Sirovich. On the simulation of a large population of neurons. J. Computational Neuroscience, 8:51-63, 2000. [ bib ] |
[1369] | A. van Ooyen. Activity-dependent neural network development. Network, 5:401-423, 1994. [ bib ] |
[1370] | L. M. Optican and B. J. Richmond. Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. 3. Information theoretic analysis. J. Neurophysiol., 57:162-178, 1987. [ bib ] |
[1371] | M. Oram, M. Wiener, R. Lestienne, and B. Richmond. Stochastic nature of precisely timed spike patterns in visual system neuronal responses. J. Neurophysiology, 81:3021-3033, 1999. [ bib ] |
[1372] | G. A. Orban and H. N. Nagel. Artificial and Biological Vision Systems. Springer Verlag, Berlin, 1992. [ bib ] |
[1373] | T. Otto and H. Eichenbaum. Neuronal activity in the hippocampus during delayed non-match to sample performance in rats: evidence for hippocampal processing in recognition memory. Hippocampus, 2(3):323-334, 1992. [ bib ] |
[1374] | E. M. Overholt, E. W. Rubel, and R. L. Hyson. A circuit for coding interaural time differences in the chick brainstem. The Journal of Neuroscience, 12(5):1698-1708, 1992. [ bib ] |
[1375] | P.Ashwin, J.Buescu, and I.Stewart. Bubbling of attractors and synchronization of chaotic oscillators. Phys. Lett. A, 193:126-139, 1994. [ bib ] |
[1376] | C. C. Pack, V. K. Berezovskii, and R. T. Born. Dynamic properties of neurons in cortical area MT in alert and anaesthetized macaque monkeys. Nature, 414(6866):905-908, Dec 2001. [ bib ] |
[1377] | G. Pagnoni, C. Zink, P. Montague, and G. Berns. Activity in human ventral striatum locked to errors in reward prediction. Nature Neuroscience, 5(2):97-98, 2002. [ bib ] |
[1378] | K. Pakdaman. The reliability of the stochastic active rotator. Neural Computation, 14:781-792, 2002. [ bib ] |
[1379] | K. Pakdaman and S. Tanabe. External noise synchronizes forced oscillators. Phys. Rev. E, 64:30901, 2001. [ bib ] |
[1380] | K. Pakdaman and S. Tanabe. Random dynamics of the hodgkin-huxley model. Phys. Rev. E, 64:50902, 2001. [ bib ] |
[1381] | K. Pakdaman, S. Tanabe, and T. Shimokawa. Coherence resonance and discharge reliability in neurons and neuronal models. Neural Networks, 14:895-905, 2001. [ bib ] |
[1382] | G. Palm. Brain Theory. Springer, Berlin, 1986. [ bib ] |
[1383] | G. Palm. On associative memory. Biol. Cybern., 36:19-31, 1980. [ bib ] |
[1384] | G. Palm, A. Aertsen, and G. L. Gerstein. On the significance of correlations among neuronal spike trains. Biol. Cybern., 59:1-11, 1988. [ bib ] |
[1385] | L. Paninski. The spike-triggered average of the integrate-and-fire cell driven by gaussian white noise. Neural Computation, 18:2592-2616, 2006. [ bib ] |
[1386] | L. Paninski. The most likely voltage path and large deviations approximations for integrate-and-fire neurons. J. Comput. Neuroscience, 21:71-87, 2006. [ bib ] |
[1387] | L. Paninski. Convergence properties of three spike-triggered analysis techniques. Network, 14:437-464, 2003. [ bib ] |
[1388] | L. Paninski, A. Haith, and G. Szirtes. Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model. J Comput Neurosci, 24(1):69-79, Feb 2008. [ bib | DOI | http ] |
[1389] | L. Paninski, J. Pillow, and E. Simoncelli. Maximum likelihood estimate of a stochastic integrate-and-fire neural encoding model. Neural computation, 16:2533-2561, 2004. [ bib ] |
[1390] | L. Paninski, J. Pillow, and E. Simoncelli. Comparing integrate-and-fire-like models estimated using intracellular and extracellular data. Neurocomputing, xx:xx, 2004. [ bib ] |
[1391] | L. Paninski, J. W. Pillow, and E. P. Simoncelli. Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Comput, 16(12):2533-2561, 2004. [ bib | DOI ] |
[1392] | S. Panzeri, R. Peterson, S. Schultz, M. Lebedev, and M. Diamond. The role of spike timing in the coding of stimulus location in rat somatosensory cortex. Neuron, 29:769-777, 2001. [ bib ] |
[1393] | S. Panzeri, E. T. Rolls, F. Battaglia, and R. Lavis. Speed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons. Network, 12:423-440, 2001. [ bib ] |
[1394] | A. Papoulis. Probability, random variables, and stochastic processes. McGraw-Hill, New York, 1991. [ bib ] |
[1395] | A. J. Parker and W. T. Newsome. Sense and the single neuron: probing the physiology of perception. Annual Reviews in Neuroscience, 21:227-277, 1998. [ bib ] |
[1396] | D. Paré, E. Shink, H. Gaudreau, A. Destexhe, and E. Lang. Impact of spontaneous synaptic activity on the resting properties of cat neocortical neurons in vivo. Journal of Neurophysiology, 79:1450-1460, 1998. [ bib ] |
[1397] | A. Pasupathy and C. E. Connor. Population coding of shape in area V4. Nature Neuroscience, 5(12):1332-1338, Dec. 2002. [ bib ] |
[1398] | A. Pasupathy and C. E. Connor. Shape representation in area V4: position-specific tuning for boundary conformation. Journal of Neurophysiology, 86(5):2505-2519, Nov. 2001. [ bib ] |
[1399] | E. D. et Patrick Naim. Des réseaux de neurones. Eyrolles, 2nd edition, 1993. [ bib ] |
[1400] | I. P. Pavlov. Conditioned reflexes. Oxford Univ. Press, 1927. [ bib ] |
[1401] | K. Pawelzik. this volume., chapter ??? [ bib ] |
[1402] | K. Pawelzik. Nichtlineare Dynamik und Hirnaktivität. Verlag Harri Deutsch, Frankfurt, 1991. [ bib ] |
[1403] | J. Pearce and G. Hall. A model of pavlovian conditioning: variations in the effectiveness of conditioned but not of unconditioned stimuli. Psychol. Rev., 87:532-552, 1980. [ bib ] |
[1404] | D. Pecevski, R. A. Legenstein, and W. Maass. Theoretical analysis of learning with reward-modulated spike-timing-dependent plasticity. In Proc. of NIPS 2007, Advances in Neural Information Processing Systems, volume 20. MIT Press, 2008. [ bib ] |
[1405] | H. C. Peng, L. F. Sha, Q. Gan, and Y. Wei. Energy function for learning invariance in multilayer perceptron. Electronics Letters, 34(3):292-294, 1998. [ bib ] |
[1406] | P. Peretto. An introduction to the modeling of neural networks. Cambridge Universtity Press, Cambridge UK, 1992. [ bib ] |
[1407] | P. Peretto. Collective properties of neural networks: A statistical physics approach. Biol. Cybern., 50:51-62, 1984. [ bib ] |
[1408] | I. Perez-Otano and M. D. Ehlers. Homeostatic plasticity and NMDA receptor trafficking. Trends in Neurosciences, 28(5):229-238, May 2005. [ bib | http ] |
[1409] | C. J. Perez-Vicente. Finite-size capacity of sparse-coding models. Europhys. Lett., 10(7):627-631, 1989. [ bib ] |
[1410] | C. J. Perez-Vicente and D. J. Amit. Sparse coding and information in hebbian neural networks. Europhys. Lett., 10:621-625, 1989. [ bib ] |
[1411] | D. H. Perkel, G. L. Gerstein, and G. P. Moore. Neuronal spike trains and stochastic point processes I. the single spike train. Biophys. J., 7:391-418, 1967. [ bib ] |
[1412] | D. H. Perkel, G. L. Gerstein, and G. P. Moore. Neuronal spike trains and stochastic point processes II. simultaneous spike trains. Biophys. J., 7:419-440, 1967. [ bib ] |
[1413] | J. Peters and S. Schaal. Policy gradient methods for robotics. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Beijing, China, 2006. [ bib ] |
[1414] | C. Petersen, R. Malenka, R. Nicoll, and J. Hopfield. All-or-none potentiation of ca3-ca1 synapses. Proc. Natl. Acad. Sci. USA, 95:4732-4737, 1998. [ bib ] |
[1415] | C. C. H. Petersen, R. C. Malenka, R. A. Nicoll, and J. J. Hopfield. All-or-none potentiation at CA3-CA1 synapses. Proceedings of the National Academy of Sciences, 95:4732-4737, 1998. [ bib ] |
[1416] | N. Petkov and P. Kruizinga. Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells. Biological Cybernetics, 76(2):83-96, Feb. 1997. [ bib ] |
[1417] | A. A. Petrov, B. A. Dosher, and Z.-L. Lu. The dynamics of perceptual learning: an incremental reweighting model. Psychological Review, 112(4):715-743, 2005. [ bib ] |
[1418] | R. R. Pfeiffer and Y. S. Kiang. Spike discharge patterns of spontaneous and continously stimulated activity in the cochlea nucleus. Biophys. J., 5:301-316, 1965. [ bib ] |
[1419] | J.-P. Pfister and W. Gerstner. Beyond pair-based stdp: a phenomenological rule for spike triplet and frequency effects. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18, pages 1083-1090. MIT Press Cambridge, 2006. [ bib ] |
[1420] | J.-P. Pfister and W. Gerstner. Triplets of spikes in a model of spike timing-dependent plasticity. J. Neuroscience, 26:9673-9682, 2006. [ bib ] |
[1421] | J. Pfister and W. Gerstner. Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity. Journal of Neuroscience, 26(38):9673, 2006. [ bib ] |
[1422] | J.-P. Pfister, T. Toyoizumi, D. Barber, and W. Gerstner. Optimal spike-timing dependent plasticity for precise action potential firing in supervised learning. Neural Computation, 18:1309-1339, 2006. [ bib ] |
[1423] | J.-P. Pfister, T. Toyoizumi, D. Barber, and W. Gerstner. Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Comp., 18(6):1318-1348, 2006. [ bib ] |
[1424] | J. Pham, K. Pakdaman, J. Champagnat, and J.-R. Vibert. Activity in sparsely connected excitatory neural networks: effect of connectivity. Neural Networks, 11:415-438, 1998. [ bib ] |
[1425] | R. Picard, C. Graczyk, S. Mann, J. Wachman, L. Picard, and L. Campbell. Vision texture. Downloaded from http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html, 2002. [ bib ] |
[1426] | A. Pikovsky. On the interaction of strange attractors. Z.Physik, B55:149, 1984. [ bib ] |
[1427] | J. Pillow, L. Paninski, and E. Simoncelli. Maximum likelihood estimation of a stochastic integrate-and-fire model. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems, volume 16, pages 1311-1318, 2004. [ bib ] |
[1428] | J. Pillow, L. Paninski, V. Uzzell, E. Simoncelli, and E.J.Chichilnisky. Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J. Neuroscience, 25:11003-11023, 2005. [ bib ] |
[1429] | J. W. Pillow, L. Paninski, V. J. Uzzell, E. P. Simoncelli, and E. J. Chichilnisky. Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J Neurosci, 25(47):11003-11013, 2005. [ bib | DOI ] |
[1430] | J. W. Pillow and E. P. Simoncelli. Biases in white-noise analysis due to non-poissonian spike generation. Neurocomputing, 52-54:109-115, 2003. [ bib ] |
[1431] | P. F. Pinsky and J. Rinzel. Intrinsic and network rhythmogenesis in a reduced traub model for ca3 neurons. J Comput Neurosci, 1(1-2):39-60, 1994. [ bib ] |
[1432] | D. J. Pinto, J. C. Brumberg, D. J. Simons, and G. B. Ermentrout. A quantitative population model of whiskers barrels: re-esamining the Wilson-Cowan equations. J. Comput. Neurosci., 3:247-264, 1996. [ bib ] |
[1433] | B. Pleger, H. R. Dinse, P. Ragert, P. Schwenkreis, J. P. Malin, and M. Tegenthoff. Shifts in cortical representations predict human discrimination improvement. PNAS, 98(21):12255-12260, 2001. [ bib ] |
[1434] | H. Plesser. Aspects of Signal Processing in Noisy Neurons. PhD thesis, Georg-August-Universität, Göttingen, 1999. [ bib ] |
[1435] | H. E. Plesser. The ModUhl software collection. Technical report, MPI für Strömungsforschung, Göttingen, 2000. http://www.chaos.gwgd.de/ plesser/ModUhl.htm. [ bib ] |
[1436] | H. E. Plesser et al. In preparation, March 1998. [ bib ] |
[1437] | H. E. Plesser and T. Geisel. Bandpass properties of integrate-fire neurons. Submitted to Computation in Neurosciences 1998, January 1998. [ bib ] |
[1438] | H. E. Plesser and W. Gerstner. Noise in integrate-and-fire models: from stochastic input to escape rates. Neural Computation, 12:367-384, 2000. [ bib ] |
[1439] | H. E. Plesser and W. Gerstner. Escape rate models for noisy integrate-and-fire neurons. Neurocomputing, 32-33:219-224, 2000. [ bib ] |
[1440] | H. E. Plesser and W. Gerstner. Noise in integrate-and-fire models: from stochastic input to escape rates. Neural Computation, 12:367-384, 2000. [ bib ] |
[1441] | H. E. Plesser and S. Tanaka. Stochastic resonance in a model neuron with reset. Phys. Lett. A, 225:228-234, 1997. [ bib ] |
[1442] | T. Poggio, M. Fahle, and S. Edelman. Fast perceptual learning in visual hyperacuity. Science, 256:1018-1021, 1992. [ bib ] |
[1443] | T. Poggio, R. Rifkin, S. Mukherjee, and P. Niyogi. General conditions for predictivity in learning theory. Nature, 428:419-422, 2004. [ bib ] |
[1444] | P. Poirazi, T. Brannon, and B. Mel. Pyramidal neuron as two-layer neural network. Neuron, 37:989-999, 2003. [ bib ] |
[1445] | P. Poirazi, T. Brannon, and B. W. Mel. Pyramidal neuron as two-layer neural network. Neuron, 37(6):989-999, 2003. [ bib ] |
[1446] | P. Poirazi and B. W. Mel. Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. Neuron, 29(3):779-796, 2001. [ bib ] |
[1447] | B. van der Pol. On relaxation oscillators. Phil. Mag., 2:978-992, 1926. [ bib ] |
[1448] | A. V. Poliakov, R. K. Powers, and M. C. Binder. Functional identification of input-output transforms of motoneurons in cat. J. Physiology, 504:401-424, 1997. [ bib ] |
[1449] | A. V. Poliakov, R. K. Powers, A. Sawczuk, and M. C. Binder. Effects of background noise on the response of rat and cat motoneurones to excitatory current transients. J. Physiology, 495:143-157, 1996. [ bib ] |
[1450] | D. B. Polley, E. Steinberg, and M. Merzenich. Perceptual learning directs auditory cortical map reorganization through top-down influences. The Journal of Neuroscience, 26(18):4970-4982, 2006. [ bib ] |
[1451] | A. Polsky, B. W. Mel, and J. Schiller. Computational subunits in thin dendrites of pyramidal cells. Nature Neuroscience, 7:621-627, 2004. [ bib ] |
[1452] | A. Polsky, B. W. Mel, and J. Schiller. Computational subunits in thin dendrites of pyramidal cells. Nature Neuroscience, 6(7):621-627, 2004. [ bib ] |
[1453] | B. Porr and F. Wörgötter. Temporal hebbian learning in rate-coded neural networks: A theoretical approach towards classical conditioning. In G. Dorffner, H. Bischof, and K. Hornik, editors, Artificial Neural Networks - ICANN 2001, volume 2130, pages 1115-1120, Berlin, 2001. Springer. [ bib ] |
[1454] | J. M. Porta and E. Celaya. Reinforcement learning for agents with many sensors and actuators acting in categorizable environments. Journal of Artificial Intelligence Research, 23:79-122, 2005. [ bib ] |
[1455] | M. Pospischil, Z. Piwkowska, M. Rudolph, T. Bal, and A. Destexhe. Calculating event-triggered average synaptic conductances from the membrane potential. J. Neurophysiology, 97:2544-2552, 2007. [ bib ] |
[1456] | B. Poucet, P. P. Lenck-Santini, V. E. Paz-Villagrán, and E. Save. Place cells, neocortex and spatial navigation: a short review. Journal of physiology (Paris), 97:537-546, 2003. [ bib ] |
[1457] | A. Pouget, P. Dayan, and R. Zemel. Inference and computation with population codes. Annual Reviews in Neuroscience, 26:381-410, 2003. [ bib ] |
[1458] | R. Powers and M. Binder. Experimental evaluation of input-output models of motoneuron discharges. J. Neurophysiology, 75:367-379, 1996. [ bib ] |
[1459] | K. G. Pratt, W. Dong, and C. D. Aizenman. Development and spike timing-dependent plasticity of recurrent excitation in the xenopus optic tectum. Nat Neurosci, 11(4):467-475, Apr 2008. [ bib | DOI | http ] |
[1460] | S. A. Prescott, S. Ratte, Y. De Koninck, and T. J. Sejnowski. Nonlinear interaction between shunting and adaptation controls a switch between integration and coincidence detection in pyramidal neurons. J Neurosci, 26(36):9084-9097, 2006. [ bib | DOI ] |
[1461] | A. A. Prinz, C. P. Billimoria, and E. Marder. Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol, 90(6):3998-4015, 2003. [ bib | DOI ] |
[1462] | A. A. Prinz, D. Bucher, and E. Marder. Similar network activity from disparate circuit parameters. Nat Neurosci, 7(12):1345-1352, 2004. [ bib | DOI ] |
[1463] | A. Prinz, L. Abbott, and E. Marder. The dynamic clamp comes of age. Trends in Neurosciences, 27:218-224, 2004. [ bib ] |
[1464] | A. Prinz, L. Abbott, and E. Marder. The dynamic clamp comes of age. Trends Neurosci, 27(4):218-224, 2004. [ bib ] |
[1465] | A. Prinz, C. Billimoria, and E. Marder. Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J. Neurophysiology, 90:3998-4015, 2003. [ bib ] |
[1466] | G. Pérez and H. Cerdeira. Extracting Messages Masked by Chaos. Physical Review Letters, 74(11):1970-1973, 1995. [ bib ] |
[1467] | R. Q. Quiroga, L. Reddy, G. Kreiman, C. Koch, and I. Fried. Invariant visual representation by single neurons in the human brain. Nature, 435:1102-1107, 2005. [ bib ] |
[1468] | M. Rabinovich, P. Varona, A. Selverston, and H. Abarbanel. Dynamical principles in neuroscience. Reviews of Modern Physics, 78(4):1213-1265, 2006. [ bib ] |
[1469] | N. Rachevsky. Mathematical Biophysics. University of Chicago Press; Reprinted by Dover, New York, 1960, 1938. [ bib ] |
[1470] | A. Rahimi, B. Recht, and T. Darrell. Learning appearance manifolds from video. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 868-875, 2005. [ bib ] |
[1471] | S. Raiguel, R. Vogels, S. G. Mysore, and G. A. Orban. Learning to see the difference specifically alters the most informative v4 neurons. The Journal of Neuroscience, 26(24):6589-6602, 2006. [ bib ] |
[1472] | W. Rall. Cable theory for dendritic neurons. In C. Koch and I. Segev, editors, Methods in Neuronal Modeling, pages 9-62, Cambridge, 1989. MIT Press. [ bib ] |
[1473] | W. Rall. Theoretical significance of dendritic trees for neuronal input-output relations. In R. F. Reiss, editor, Neural theory and modeling, pages 73-97, Stanford CA, 1964. Stanford University Press. [ bib ] |
[1474] | W. Rall, I. Segev, J. Rinzel, and G. M. Shepherd. The theoretical foundation of dendritic function : selected papers of Wilfrid Rall with commentaries. MIT Press, Cambridge, Mass., 1995. [ bib ] |
[1475] | S. Ramòn y Cajal. Histologie du système nerveux de l'homme et des vertébré. A. Maloine, Paris, 1909. [ bib ] |
[1476] | J. Ranck. Studies on single neurons in dorsal hippocampal formation and septum in unrestrained rats. I. behavioral correlates and firing repertoires. Experimental Neurology, 41(2):461-531, 1973. [ bib ] |
[1477] | J. B. Ranck. Head direction cells in the deep cell layer of dorsolateral pre-subiculum in freely moving rats. Society for Neuroscience Abstracts, 10:599, 1984. First article on head direction cells. [ bib ] |
[1478] | R. Rao and D. Ballard. Development of localized oriented receptive fields by learning a translation-invariant code for natural images. Network: Computation in Neural Systems, 9:219-234, 1998. [ bib ] |
[1479] | R. Rao and D. Ruderman. Learning Lie groups for invariant visual perception. In Neural Information Processing Systems, volume 1998, pages 810-816, 1998. [ bib ] |
[1480] | R. Rao and T. Sejnowski. Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning, 2001. [ bib ] |
[1481] | R. Rao and T. Sejnowski. Predictive sequence learning in recurrent neocortical circuits. In Neural Information Processing Systems, volume 2000, pages 164-170, 2000. [ bib ] |
[1482] | R. P. Rao and D. H. Ballard. Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1):79-87, Jan 1999. [ bib ] |
[1483] | R. P. Rao and T. J. Sejnowski. Spike-timing-dependent Hebbian plasticity as temporal difference learning. Neural Computation, 13(10):2221-2238, 2001. [ bib ] |
[1484] | R. P. N. Rao and T. J. Sejnowski. Spike-timing dependent Hebbian plasticity as temporal difference learning. Neural Computation, 13:2221-2237, 2001. [ bib ] |
[1485] | M. Rapp, Y. Yarom, and I. Segev. The impact of parallel fiber background activity pn the cable properties of cerebellar purkinje cells. Neural Comput., 4:518-533, 1992. [ bib ] |
[1486] | A. Rauch, G. Camera, H. Lüscher, W. Senn, and S. Fusi. Neocortical pyramidal cells respond as integrate-and-fire neurons to in-vivo-like input currents. J. Neurophysiology, 90:1598-1612, 2003. [ bib ] |
[1487] | A. Rauch, G. La Camera, H.-R. Luscher, W. Senn, and S. Fusi. Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. J Neurophysiol, 90(3):1598-1612, 2003. [ bib | DOI ] |
[1488] | G. H. Recanzone, W. M. Jenkins, G. T. Hradek, and M. M. Merzenich. Progressive improvement in discriminative abilities in adult owl monkeys performing a tactive frequency discrimination task. The Journal of Neurophysiology, 67:1015-1030, 1992. [ bib ] |
[1489] | G. H. Recanzone, C. E. Schreiner, and M. M. Merzenich. Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. The Journal of Neuroscience, 13:87-103, 1993. [ bib ] |
[1490] | M. Recce and K. Harris. Memory for places: a navigational model in support of Marr's theory of hippocampal function. Hippocampus, 6:735-748, 1996. [ bib ] |
[1491] | L. Reddy and N. Kanwisher. Coding of visual objects in the ventral stream. Current Opinions in Neurobiololy, 16(4):408-14, 2006. [ bib ] |
[1492] | A. Redish. Addiction as a computational process gone awry. Science, 306:1944-1947, 2004. [ bib ] |
[1493] | A. Redish. The hippocampal debate: are we asking the right questions? Behavioral Brain Research, 127(1-2):81-98, 2001. [ bib ] |
[1494] | A. D. Redish. Beyond the Cognitive Map, From Place Cells to Episodic Memory. MIT Press-Bradford Books, London, 1999. [ bib ] |
[1495] | A. D. Redish. Beyond the Cognitive Map - From Place Cells to Episodic Memory. MIT Press, 1999. [ bib ] |
[1496] | A. D. Redish, B. L. McNaughton, and C. A. Barnes. Place cell firing shows an inertia-like process. Neurocomputing, 32-33:235-241, 2000. [ bib ] |
[1497] | M. Reed and B. Simon. Methods of modern mathematical physics. 1. Functional analysis. Academic Press, 1972. [ bib ] |
[1498] | W. G. Regehr and D. W. Tank. Postsynaptic nmda receptor-mediated calcium accumulation in hippocampal ca1 pyramidal cell dendrites. Nature, 345(6278):807-810, 1990. [ bib | DOI ] |
[1499] | D. Reich, J. Victor, and B. Knight. The power ratio and the interval map: spiking models and extracellular recordings. J. of Neuroscience, 18(23):10090-10104, 1998. [ bib ] |
[1500] | D. Reich, J. Victor, B. Knight, T. Ozaki, and E. Kaplan. Response variability and timing precision of neuronal spike trains in vivo. J. Neurophysiology, 77:2836-2841, 1997. [ bib ] |
[1501] | W. Reichardt. Movement perception in insects. In W. Reichardt, editor, Processing of optical data by organisms and by machines. Academic Press, New York, 1969. [ bib ] |
[1502] | F. Reif and W. Muschik. Statistische Physik und Theorie der Wärme. de Gruyter, 1987. [ bib ] |
[1503] | P. Reinagel and R. C. Reid. Temporal coding of visual information in the thalamus. J. of Neuroscience, 20:5392-5400, 2002. [ bib ] |
[1504] | P. Reinagel and R. C. Reid. Precise firing events are conserved across neurons. J. of Neuroscience, 22:6837-6841, 2002. [ bib ] |
[1505] | R. F. Reiss. Neural theory and modeling: proceedings of the 1962 Ojai symposium. Stanford University Press, Stanford, Calif., 1964. [ bib ] |
[1506] | H. J. A. Reitboeck. A multi electrode matrix for studies of temporal signal correlations within neural assemblies. In B. E. et al., editor, Synergetics of the brain, pages 174-182, Berlin Heidelberg New York, 1983. Springer-Verlag. [ bib ] |
[1507] | A. Renart, P. Song, and X. J. Wang. Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks. Neuron, 38:473-485, 2003. [ bib ] |
[1508] | R. Rescorla and A. Wagner. A theory of pavlovian conditioning: variations in the effectiveness of reinforecement and nonreinforcement. In A. H. Black and W. Prokasy, editors, Classical Conditioning II: current research and theory, pages 64-99. Appleton Century Crofts, New York, 1972. [ bib ] |
[1509] | J. Reutimann, M. Giugliano, and S. Fusi. Event-driven simulation of spiking neurons with stochastic dynamics. Neural Computation, xx:xx, 2003. [ bib ] |
[1510] | A. D. Reyes, E. W. Rubel, and W. J. Spain. In vitro analysis of optimal stimuli for phase-locking and time-delayed modulation of firing in avian nucleus laminaris neuron. J. Neurosci, 16:993-1007, 1996. [ bib ] |
[1511] | A. D. Reyes, E. W. Rubel, and W. J. Spain. Membrane properties underlying the firing of neurons in the avian cochlear nucleus. J. Neurosci., 14(9):5352-5364, 1994. [ bib ] |
[1512] | J. N. J. Reynolds, B. I. Hyland, and J. R. Wickens. A cellular mechanism of reward-related learning. Nature, 413(6851):67-70, Sept. 2001. [ bib | http ] |
[1513] | W. S. Rhode and P. H. Smith. Encoding timing and intensity in the ventral cochlear nucleus of the cat. J. Neurophysiol., 56(2):261-286, 1986. [ bib ] |
[1514] | W. S. Rhode and P. H. Smith. Physiological studies on neurons in the dorsal cochlear nucleus of the cat. J. Neurophysiol., 56(2):287-307, 1986. [ bib ] |
[1515] | A. Rhodes-Morrison, A. Aertsen, and M. Diesmann. Spike-timing dependent plasticity in balanced random networks. Neural Computation, 19:1437-1467, 2007. [ bib ] |
[1516] | L. M. Ricciardi. Diffusion Processes and related topics in biology. Springer-Verlag, Berlin, 1977. [ bib ] |
[1517] | M. M. Rich and P. Wenner. Sensing and expressing homeostatic synaptic plasticity. Trends in Neurosciences, 30(3):119-125, Mar. 2007. [ bib | http ] |
[1518] | R. Kempter, C. Leibold, H. Wagner, and J. L. van Hemmen. Formation of temporal-feature maps by axonal propagation of synaptic learning. Proc. Natl. Academy of Sciences USA, 98:4166-4171, 2001. [ bib ] |
[1519] | M. Richardson. The effects of synaptic conductance on the voltage distribution and firing rate of spiking neurons. Physical Review E, 69:51918, 2004. [ bib ] |
[1520] | M. Richardson, N. Brunel, and V. Hakim. from subthreshold to firing-rate resonance. J. Neurophysiology, 89:2538-2554, 2003. [ bib ] |
[1521] | M. Richardson and W. Gerstner. Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise. Chaos, 16:26106, 2006. [ bib ] |
[1522] | M. Richardson and W. Gerstner. Synaptic shot noise and conductance fluctuations affect the membrane voltage with equal significance. Neural Computation, 17:923-947, 2005. [ bib ] |
[1523] | M. Richardson, O. Melamed, G. Silberberg, W. Gerstner, and H. Markram. Short-term-plasticity orchestrates the response of pyramidal cells and interneurons to population bursts. J. Computational Neuroscience, 18:323-331, 2005. [ bib ] |
[1524] | M. J. E. Richardson, N. Brunel, and V. Hakim. From subthreshold to firing-rate resonance. J Neurophysiol, 89(5):2538-2554, 2003. [ bib | DOI ] |
[1525] | B. J. Richmond, L. M. Optican, and H. Spitzer. Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. i. stimulus-response relations. Journal of Neuroscience, 64(2):351-369, 1990. [ bib ] |
[1526] | U. Riedel, R. Kühn, and J. L. van Hemmen. Temporal sequences and chaos in neural nets. Phys. Rev. A, 38:1105-1108, 1988. [ bib ] |
[1527] | K. C. Riegle and R. L. Meyer. Rapid homeostatic plasticity in the intact adult visual system. Journal of Neuroscience, 27(39):10556-10567, 2007. [ bib | DOI | http ] |
[1528] | F. Rieke. Spikes : exploring the neural code. MIT Press, Cambridge, Mass., 1997. [ bib ] |
[1529] | F. Rieke, D. Warland, R. de Ruyter van Steveninck, and W. Bialek. Spikes - Exploring the neural code. MIT Press, Cambridge, MA, 1996. [ bib ] |
[1530] | D. Ringach. On the origin of the functional architecture of the cortex. PLoS ONE, 2(2):e251, 2007. [ bib ] |
[1531] | D. Ringach, M. Hawken, and R. Shapley. Dynamics of orientation tuning in macaque primary visual cortex. Nature, 387(6630):281-284, 1997. [ bib ] |
[1532] | J. Rinzel. Excitation dynamics: insights from simplified membrane models. Theoretical Trends in Neuroscience: Federation Proceedings, 44(15):2944-2946, 1985. [ bib ] |
[1533] | J. Rinzel and G. B. Ermentrout. Analysis of neural excitability and oscillations. In C. Koch and I. Segev, editors, Methods in Neuronal Modeling, 2nd. ed., pages 251-291, Cambridge, 1998. MIT Press. [ bib ] |
[1534] | J. Rinzel, D. Terman, X. Wang, and B. Ermentrout. Propagating activity patterns in large-scale inhibitory neuronal networks. Science, 279:1351-1355, 1998. [ bib ] |
[1535] | H. Risken. The Fokker Planck equation: methods of solution and applications. Springer-Verlag, Berlin, 1984. [ bib ] |
[1536] | H. Ritter, T. Martinez, and K. Schulten. Neuronale Netze: eine Einf"uhrung in die Neuroinformatik selbstorganisierter Netzwerke. Addison-Wesley, 1990. [ bib ] |
[1537] | R. Ritz. Informationverarbeitung durch koh"arente Aktivit"at in neuronalen Systemen, volume 42 of Reihe Physik. Verlag Harri Deutsch, 1995. [ bib ] |
[1538] | R. Ritz. Kollektive oszillationen in neuronalen netzen. Diplomarbeit, Technische Universität München, 1991. [ bib ] |
[1539] | R. Ritz, C. Fohlmeister, W. Gerstner, and J. L. van Hemmen. Modeling spontaneous activity patterns of the visual cortex. In N. Elsner and R. Menzel, editors, Goettingen Neurobiology Report 1995; Proceedings of the 23rd Goettingen Neurobiology Conference 1995, volume 2, page 887. Georg Thieme Verlag, 1995. [ bib ] |
[1540] | R. Ritz, W. Gerstner, R. Gaudoin, and J. van Hemmen. Poisson-like neuronal firing due to multiple synfire chains in simultaneous action. In J. Bower, editor, Computational Neuroscience - Trends in research 1997, pages 801-806. Plenum Press, New York, 1997. [ bib ] |
[1541] | R. Ritz, W. Gerstner, and J. L. van Hemmen. Associative binding and segregation in a network of spiking neurons. In E. Domany, J. L. van Hemmen, and K. Schulten, editors, Models of neural networks II, pages 175-219, New York, 1994. Springer. [ bib ] |
[1542] | R. Ritz, W. Gerstner, and J. L. van Hemmen. Pattern segmentation in an associative neural network of spiking neurons. In N. Elsner and M. Heisenberg, editors, Gene - Brain - Behaviour., page 877, Stuttgart, New York, 1993. Georg Thieme Verlag. [ bib ] |
[1543] | R. Ritz, W. Gerstner, and J. L. van Hemmen. A biologically motivated and analytically soluble model of collective oscillations in the cortex: II. Application to binding and pattern segmentation. Biol. Cybern., 71:349-358, 1994. [ bib ] |
[1544] | R. Ritz and J. L. van Hemmen. Pattern segmentation and feature linking as simultaneous processes in an associative network of spiking neurons. In S. Gielen and B. Kappen, editors, ICANN '93, Proceedings of the International Conference on Artificial Neural Networks., pages 914-917, Berlin, Heidelberg, New York, 1993. Springer. [ bib ] |
[1545] | R. Ritz and T. Sejnowski. Synchronous oscillatory activity in sensory systems: new vistas on mechanisms. Current Opinion in Neurobiology, 7:536-546, 1997. [ bib ] |
[1546] | P. Roberts. Modeling inhibitory plasticity in the electrosensory system of mormyrid electric fish. J. Neurophysiology, 84:2035-2047, 2000. [ bib ] |
[1547] | P. Roberts. Dynamics of temporal learning rules. Physical Review E, 62:4077-4082, 2000. [ bib ] |
[1548] | P. Roberts. Computational consequences of temporally asymmetric learning rules: I. Differential Hebbian learning. J. Computational Neuroscience, 7:235-246, 1999. [ bib ] |
[1549] | P. Roberts and C. Bell. Spike timing dependent synaptic plasticity in biological systems. Biol. Cybernetics, 87:392-403, 2002. [ bib ] |
[1550] | P. Roberts and C. Bell. Computational consequences of temporally asymmetric learning rules: II. Sensory image cancellation. Computational Neuroscience, 9:67-83, 2000. [ bib ] |
[1551] | H. Robinson and A. Harsch. Stages of spike time variability during neuronal responses to transient inputs. Phys. Rev. E, 66:061902, 2002. [ bib ] |
[1552] | J. Roddey, B. Girish, and J. Miller. Assessing the performance of neural encoding models in the presence of noise. J. Compuational Neuroscience, 8:95-112, 2000. [ bib ] |
[1553] | P. Rodriguez and W. Levy. A model of hippocampal activity in trace conditioning: Where's the trace? Behavioral Neuroscience, 115:1224-1238, 2001. [ bib ] |
[1554] | R. Rojas. Neural networks: a systematic introduction. Springer, Berlin, Heidelberg, 1996. [ bib ] |
[1555] | R. Rojas. Theorie der neuronalen Netze. Springer, Berlin, Heidelberg, 1993. [ bib ] |
[1556] | E. T. Rolls. Neurophysiological and computational analyses of the primate presubiculum, subiculum and related areas. Behavioral Brain Research, 174:289-303, 2006. [ bib ] |
[1557] | E. T. Rolls. Spatial view cells and the representation of place in the primate hippocampus. Hippocampus, 9:467-480, 1999. [ bib ] |
[1558] | E. T. Rolls, N. C. Aggelopoulos, and F. Zheng. The receptive fields of inferior temporal cortex neurons in natural scenes. Journal of Neuroscience, 23(1):339-348, Jan. 2003. [ bib ] |
[1559] | E. T. Rolls, J. Xiang, and L. Franco. Object, space, and object-space representations in the primate hippocampus. Journal of Neurophysiology, 94:833-844, 2005. [ bib ] |
[1560] | R. Romand. Survey of intracellular recording in the cochlear nucleus of the cat. Brain Research, 148:43-65, 1978. [ bib ] |
[1561] | R. Romo and W. Schultz. Dopamine neurons of the monkey midbrain: Contingencies of responses to active touch during self-initiated arm movements. J. Neurophysiol., 62:532-606, 1990. [ bib ] |
[1562] | G. Rose and W. Heiligenberg. Temporal hyperacuity in the electric sense of fish. Nature, 318:178-180, 1985. [ bib ] |
[1563] | R. M. Rose and J. L. Hindmarsh. A model of a thalamic neuron. Proc. R. Soc. Lond., B 225:161-193, 1985. [ bib ] |
[1564] | D. J. Rosen, D. E. Rummelhart, and E. I. Knudsen. A connectionist model of the owl's sound localizing system. In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Neural Information Processing Systems, volume 6, pages 606-613, San Francisco, 1994. Morgan Kaufmann. [ bib ] |
[1565] | F. Rosenblat. The perceptron: a probabilistic model for information storage and organization in the brain. Psych. Review, 65:386-408, 1958. [ bib ] |
[1566] | F. Rosenblatt. Principles of neurodynamics. Spartan books, Washington, 1962. [ bib ] |
[1567] | M. Rosenblum, A. Pikovsky, and J. Kurths. Phase synchronization of chaotic oscillators. Phys. Rev. Lett., 76:1804-1807, 1996. [ bib ] |
[1568] | E. Rosenzweig, A. Redish, B. McNaughton, and C. Barnes. Hippocampal map realignment and spatial learning. Nature Neuroscience, 6(6):609-15, 2003. [ bib ] |
[1569] | J. P. Rospars and P. Lansky. Stochastic model neuron without resetting of dendritic potential: application to the olfactory system. Biol. Cybern., 69:283-294, 1993. [ bib ] |
[1570] | M. C. W. van Rossum, G. Bi, and G. G. Turrigiano. Stable Hebbian learning from spike timing-dependent plasticity. Journal of Neuroscience, 20(23):8812-8821, 2000. [ bib ] |
[1571] | M. C. W. van Rossum, G. Q. Bi, and G. G. Turrigiano. Stable Hebbian learning from spike timing-dependent plasticity. J. Neuroscience, 20:8812-8821, 2000. [ bib ] |
[1572] | M. W. van Rossum, B. O'Brian, and R. Smith. Effects of noise on the spike timing precision of retinal ganglion cells. J. Neurophysiology, 89:2406-2419, 2003. [ bib ] |
[1573] | A. Rotenberg and R. U. Muller. Variable place-cell coupling to a continuously viewed stimulus: evidence that the hippocampus acts as a perceptual system. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 352:1505-1513, 1997. [ bib ] |
[1574] | A. Roth and M. Häusser. Compartmental model of rat cerebellar Purkinje cells based on simultaneous somatic and dendritic patch-clamp recordings. J. Physiology (London), 535:445-472, 2001. [ bib ] |
[1575] | S. Rotter. Wechselwirkende stochastische Punktprozesse als Modell für neuronale Aktivität im Neocortex der Säugetiere, volume 21 of Reihe Physik. Harri Deutsch, Frankfurt, 1994. [ bib ] |
[1576] | S. Royer and D. Pare. Conservation of total synaptic weight through balanced synaptic depression and potentiation. Nature, 422:518-522, 2003. [ bib ] |
[1577] | J. Rubin, R. Gerkin, G.-Q. Bi, and C. Chow. Calcium time course as a signal for spike-timing-dependent plasticity. J. Neurophysiology, 93:2600-2613, 2005. [ bib ] |
[1578] | J. Rubin, D. D. Lee, and H. Sompolinsky. Equilibrium properties of temporally asymmetric Hebbian plasticity. Physical Review Letters, 86:364-367, 2001. [ bib ] |
[1579] | J. Rubin, D. D. Lee, and H. Sompolinsky. Equilibrium properties of temporally asymmetric Hebbian learning. Physical Review Letters, 86(2):364-367, 2001. [ bib ] |
[1580] | J. Rubner and P. Tavan. A self-organizing network for principal-component analysis. Europhysics Letters, 10(7):693-698, 1989. [ bib ] |
[1581] | D. L. Ruderman and W. Bialek. Statistics of natural images: Scaling in the woods. Physical Review Letters, 73(6):814-817, Aug 1994. [ bib | DOI ] |
[1582] | W. Rudin. Real and Complex Analysis. McGraw-Hill, New York, 1974. p. 63. [ bib ] |
[1583] | M. Rudolph and A. Destexhe. Tuning neocortical pyramidal neurons between integrators and coincidence detectors. J Comput Neurosci, 14(3):239-251, 2003. [ bib ] |
[1584] | B. Ruf and M. Schmitt. Unsupervised learning in networks of spiking neurons using temporal coding. In W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, editors, Artificial neural networks - ICANN'97. Springer-Verlag, Heidelberg, 1997. [ bib ] |
[1585] | D. E. Rumelhard, J. McClelland, and the PDP research group. Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations. MIT Press, Cambridge Mass., 1986. [ bib ] |
[1586] | G. Rummery. Problem solving with reinforcement learning. Cambridge University, 1995. [ bib ] |
[1587] | C. Rumsey and L. Abbott. Equalization of synaptic efficacy by activity- and timing-dependent synaptic plasticity. J. Neurophysiology, xx:xx, 2003. in press. [ bib ] |
[1588] | C. C. Rumsey and L. F. Abbott. Synaptic democracy in active dendrites. Journal of Neurophysiology, 96(5):2307-2318, Nov 2006. [ bib ] |
[1589] | M. F. S. Rushworth and T. E. J. Behrens. Choice, uncertainty and value in prefrontal and cingulate cortex. Nat Neurosci, 11(4):389-397, Apr. 2008. [ bib | http ] |
[1590] | R. R. de Ruyter van Stevenick and W. Bialek. Real-time performance of a movement-sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences. Proc. R. Soc. B, 234:379-414, 1988. [ bib ] |
[1591] | R. R. de Ruyter van Steveninck and W. Bialek. Reliability and statistical efficiency of a blowfly movement-sensitive neuron. Phil. Trans. R. Soc. Lond. Ser. B, 348:321-340, 1995. [ bib ] |
[1592] | R. R. de Ruyter van Steveninck, G. D. Lowen, S. P. Strong, R. Koberle, and W. Bialek. Reproducibility and variability in neural spike trains. Science, 275:1805, 1997. [ bib ] |
[1593] | J. Saarinen and D. M. Levi. Perceptual learning in vernier acuity: what is learned? Vision Research, 35:519-527, 1995. [ bib ] |
[1594] | N. H. Sabah and K. N. Leibovic. Subthreshold oscillatory responses of the hodgkin-huxley cable model for the squid giant axon. Biophys J, 9(10):1206-1222, 1969. [ bib ] |
[1595] | H. Sakaguchi, S. Shinomoto, and Y. Kuramoto. Local and global self-entrainments in oscillator lattices. Progr. Theor. Phys., 77:1005-1010, 1987. [ bib ] |
[1596] | B. Sakmann and E. Neher. Single-channel recording. Plenum Press, New York, 2nd ed edition, 1995. [ bib | .html ] |
[1597] | E. Salinas. How behavioral constraints may determine optimal sensory representations. PLoS Biology, 4(12):e387-, Dec. 2006. [ bib | http ] |
[1598] | E. Salinas and L. Abbott. Invariant visual responses from attentional gain fields. J. of Neurophysiology, 77:3267-3272, 1997. [ bib ] |
[1599] | E. Salinas and L. Abbott. A model of multiplicative neural responses in parietal cortex. Proc. Natl. Academy Sci. USA, 93:11956-11961, 1996. [ bib ] |
[1600] | E. Salinas and L. Abbott. Transfer of coded information from sensory to motor networks. J. of Neuroscience, 15:6461-6476, 1995. [ bib ] |
[1601] | E. Salinas and L. Abbott. Vector reconstruction from firing rates. J. Computat. Neurosci., 1:89-107, 1994. [ bib ] |
[1602] | E. Salinas and T. Sejnowski. Integrate-and-fire neurons driven by correlated stochastic input. Neural Computation, 14:2111-2155, 2002. [ bib ] |
[1603] | E. Salinas and T. Sejnowski. Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. J. of Neuroscience, 20:6193-6209, 2000. [ bib ] |
[1604] | K. Samejima, Y. Ueda, K. Doya, and M. Kimura. Representation of action-specific reward value in the striatum. Science, 310:1337-1340, 2005. [ bib ] |
[1605] | G. Sampath and S. K. Srinivasan. Stochastic models for spike trains of single neurons. Springer, Berlin Heidelberg New York, 1977. [ bib ] |
[1606] | A. Samsonovich and B. McNaughton. Path integration and cognitive mapping in a continuous attractor neural network model. Journal of Neuroscience, 17:5900-5920, 1997. [ bib ] |
[1607] | J. A. Sanders and F. Verhulst. Averaging methods in nonlinear dynamics systems. Springer-Verlag, New York, 1985. [ bib ] |
[1608] | O. Sandfuchs, F. Kaiser, and M. Belić. Self-organization and Fourier selection of optical patterns in a nonlinear photorefractive feedback system. Physical Review A, 64(6):63809, 2001. [ bib ] |
[1609] | T. D. Sanger. Optimal unsupervised learning predicts the internal representation of barn owl head movements. In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Neural Information Processing Systems, volume 6, pages 614-621, San Francisco, 1994. Morgan Kaufmann. [ bib ] |
[1610] | T. D. Sanger. Neural population codes. Current Opinion in Neurobiology, 13:238-249, 2003. [ bib ] |
[1611] | T. D. Sanger. Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks, 2:459-473, 1989. [ bib ] |
[1612] | F. Sargolini, M. Fyhn, T. Hafting, B. L. McNaughton, M. P. Witter, M.-B. Moser, and E. I. Moser. Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science, 312(5774):758-762, May 2006. [ bib ] |
[1613] | K. Sathian and A. Zangaladze. Tactile learning is task-specific but transfers between fingers. Perceptual Psychophysics, 59:119-128, 1997. [ bib ] |
[1614] | A. Saudargiene, B. Porr, and F. Wörgötter. How the shape of pre- and postsynaptic signals can influence stdp: A biophysical model. Neural Computation, 16:595-626, 2003. [ bib ] |
[1615] | E. Save, L. Nerad, and B. Poucet. Contribution of multiple sensory information to place field stability in hippocampal place cells. Hippocampus, 10(1):64-76, 2000. [ bib ] |
[1616] | S. Scarpetta, L. Zhaoping, and J. Hertz. Hebbian imprinting and retrieval in oscillatory neural networks. Neural Computation, 14:2371-2396, 2002. [ bib ] |
[1617] | B. Schölkopf and H. Mallot. View-based cognitive mapping and path planning. Adaptive Behavior, 3:311-348, 1995. [ bib ] |
[1618] | B. Schölkopf and A. Smola. Learning with kernels: support vector machines, regularization, optimization, and beyond,. MIT Press Cambridge, 2002. [ bib ] |
[1619] | A. T. Schaefer, M. E. Larkum, B. Sakmann, and A. Roth. Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. J Neurophysiol, 89(6):3143-3154, 2003. [ bib | DOI ] |
[1620] | C. Scheier and R. Pfeifer. Exploiting embodiment for category learning. In J.-A. M. R. Pfeifer, P. Blumberg and S. Wilson, editors, From Animals to Animats 5, pages 32-37. MIT-Press, 1998. [ bib ] |
[1621] | O. Scherf, K. Pawelzik, F. Wolf, and T. Geisel. Unification of complementary feature map models. In M. Marinaro and P. G. Morasso, editors, ICANN 94, pages 338-341. Springer, 1994. [ bib ] |
[1622] | A. Schiegg. Dynamik des intrazellulären Calciums bei der Induktion von Long-Term-Potentiation. Diplomarbeit, Technische Universität München, 1994. [ bib ] |
[1623] | A. Schiegg, W. Gerstner, and J. van Hemmen. Intracellular Ca2+ stores can account for the time course of LTP induction: A model of Ca2+ dynamics in dendritic spines. J. Neurophysiol., 74:1046-1055, 1995. [ bib ] |
[1624] | T. B. Schillen and P. König. Stimulus-dependent assembly formation of oscillatory responses: Ii. desynchronization. Neural Computation, 3:167-178, 1991. [ bib ] |
[1625] | J. Schiller, Y. Schiller, G. Stuart, and B. Sakmann. Calcium action potentials restricted to distal apical dendrites of rat neocortical pyramidal neurons. J Physiol, 505(3):605-616, 1997. [ bib ] |
[1626] | T. Schimming and M.Hasler. Optimal detection of differential chaos shift keying. IEEE Trans. Circuits and Systems I., page to appear, 2001. [ bib ] |
[1627] | M. Schindler, P. Talkner, and P. Hanggi. Firing time statistics for driven neuron models: Analytic expressions versus numerics. Phys. Rev. Letters, 93:48102, 2004. [ bib ] |
[1628] | J. Schmidhuber. Gödel machines: fully self-referential optimal universal problem solvers. In B. Goertzel and C. Pennachin, editors, Artificial General Intelligence, pages 199-226. Springer Verlag, 2006. Variant available as arXiv:cs.LO/0309048. [ bib ] |
[1629] | J. Schmidhuber. The Speed Prior: a new simplicity measure yielding near-optimal computable predictions. In J. Kivinen and R. H. Sloan, editors, Proceedings of the 15th Annual Conference on Computational Learning Theory (COLT 2002), Lecture Notes in Artificial Intelligence, pages 216-228. Springer, Sydney, Australia, 2002. [ bib ] |
[1630] | J. Schmidhuber. Simple algorithmic principles of discovery, subjective beauty, selective attention, curiosity & creativity. In Proc. 18th Intl. Conf. on Algorithmic Learning Theory (ALT 2007), LNAI 4754, pages 32-33. Springer, 2007. Joint invited lecture for ALT 2007 and DS 2007, Sendai, Japan, 2007. [ bib ] |
[1631] | J. Schmidhuber. Overview of work on hierarchical learning and automatic subgoal generation, with a dozen publications, 2004. http://www.idsia.ch/ juergen/subgoals.html. [ bib ] |
[1632] | J. Schmidhuber. Curious model-building control systems. In Proceedings of the International Joint Conference on Neural Networks, Singapore, volume 2, pages 1458-1463. IEEE press, 1991. [ bib ] |
[1633] | J. Schmidhuber, M. Eldracher, and B. Foltin. Semilinear predictability minimization produces well-known feature detectors. Neural Computation, 8(4):773-786, 1996. [ bib ] |
[1634] | J. Schmidhuber, A. Graves, F. Gomez, S. Fernandez, and S. Hochreiter. Sequence Learning with Artificial Recurrent Neural Networks. Invited by Cambridge University Press, 2007. In preparation (aiming to become the definitive textbook on RNN). [ bib ] |
[1635] | E. Schneidman, B. Freedman, and I. Segev. Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Computation, 10:1679-1703, 1998. [ bib ] |
[1636] | E. Schneidman, M.J.Berry, R. Segav, and W. Bialek. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440:1007-1012, 2006. [ bib ] |
[1637] | A. Schoups, R. Vogels, and G. A. Orban. Effects of perceptual learning in orientation discrimination on orientation coding in v1. Invest Ophtalmol Vis Sci Suppl, 39:684, 1998. [ bib ] |
[1638] | A. Schoups, R. Vogels, and G. A. Orban. Human perceptual learning in identifying the oblique orientation: retinotopy, orientation specificity, and monocularity. The Journal of Physiology, 483(3):797-810, 1995. [ bib ] |
[1639] | A. Schoups, R. Vogels, N. Quian, and G. A. Orban. Practising orientation identification improves orientation coding in v1 neurons. Nature, 412:549-553, 2001. [ bib ] |
[1640] | E. Schrödinger. Zur Theorie der Fall- und Steigversuche and Teilchen mit Brownscher Bewegung. Physikalische Zeitschrift, 16:289-295, 1915. [ bib ] |
[1641] | N. N. Schraudolph, M. Eldracher, and J. Schmidhuber. Processing images by semi-linear predictability minimization. Network: Computation in Neural Systems, 10(2):133-169, 1999. [ bib ] |
[1642] | S. Schultz and S. Panzeri. Temporal correlations and neural spike train entropy. Phys. Rev. Letters, xx:xx, 2001. [ bib ] |
[1643] | W. Schultz. Reward responses of dopamine neurons: A biological reinforcement signal. In W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, editors, Artificial Neural Networks - ICANN'97, Lecture Notes in Computer Science 1327, pages 3-12. Springer, 1997. [ bib ] |
[1644] | W. Schultz. Behavioral dopamine signals. Trends in Neurosciences, 30(5):203-210, May 2007. [ bib | http ] |
[1645] | W. Schultz. Dopamine neurons and their role in reward mechanisms. Curr. Op. Neurobiol., 7:191-197, 1997. [ bib ] |
[1646] | W. Schultz, P. Apicella, E. Scarnati, and T. Ljungberg. Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. J. Neuroscience, 13:900-913, 1993. [ bib ] |
[1647] | W. Schultz, P. Apicella, E. Scarnati, and T. Ljungberg. Neuronal activity in monkey ventral striatum related to the expectation of reward. J. Neuroscience, 12:4595-4610, 1992. [ bib ] |
[1648] | W. Schultz, P. Dayan, and R. Montague. A neural substrate for prediction and reward. Science, 275:1593-1599, 1997. [ bib ] |
[1649] | W. Schultz and A. Dickinson. Neuronal coding of prediction errors. Annual Reviews of Neuroscience, 23:472-500, 2000. [ bib ] |
[1650] | W. Schultz and R. Romo. Dopamine neurons of the monkey midbrain: Contingencies of responses to stimuli eliciting immediate behavioral reaction. J. Neurophysiol., 63:607-624, 1990. [ bib ] |
[1651] | D. J. Schulz, J.-M. Goaillard, and E. Marder. Variable channel expression in identified single and electrically coupled neurons in different animals. Nat Neurosci, 9(3):356-362, 2006. [ bib | DOI ] |
[1652] | H. G. Schuster. Applications of Neuronal Networks. Nonlinear Systems 3. VCH Weinheim, 1992. [ bib ] |
[1653] | H. G. Schuster. Nonlinear Dynamics and Neuronal Networks. Nonlinear Systems 2. VCH Weinheim, 1991. [ bib ] |
[1654] | H. G. Schuster and P. Wagner. A model for neuronal oscillations in the visual cortex 1. mean-field theory and derivation of the phase equations. Biol. Cybern., 64:77-82, 1990. [ bib ] |
[1655] | H. G. Schuster and P. Wagner. A model for neuronal oscillations in the visual cortex 2. phase description and feature dependent synchronization. Biol. Cybern., 64:83-85, 1990. [ bib ] |
[1656] | E. D. Schutter. Cerebellar long term depression may normalize excitation of purkinje cells: a hypothesis. Trends in Neurosciences, 18:291-295, 1995. [ bib ] |
[1657] | L. Schwabe and K. Obermayer. Adaptivity of tuning functions in a generic recurrent network model of a cortical hypercolumn. The Journal of Neuroscience, 25(13):3323-3332, 2005. [ bib ] |
[1658] | E. Schwartz. Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception. Biol. Cybern., 25:181-194, 1977. [ bib ] |
[1659] | E. L. Schwartz. Computational Neuroscience. MIT Press, Cambridge, 1990. [ bib ] |
[1660] | G. Schwartz, R. Harris, D. Shrom, and M. II. Detection and prediction of periodic patterns by the retina. Nature Neuroscience, 10:552-554, 2007. [ bib ] |
[1661] | O. Schwartz, T. J. Sejnowski, and P. Dayan. Soft mixer assignment in a hierarchical generative model of natural scene statistics. Neural Computation, 18(11):2680-2718, 2006. [ bib ] |
[1662] | O. Schwartz and E. P. Simoncelli. Natural signal statistics and sensory gain control. Nature Neuroscience, 4:819-825, 2001. [ bib ] |
[1663] | D. W. Scott. Multivariate Density Estimation. Wiley, New York, 1992. [ bib ] |
[1664] | W. von Seelen. Informationverarbeitung in homogenen netzen von neuronen. Kybernetik, 5:133-148, 1968. [ bib ] |
[1665] | I. Segev. Single neurone models: oversimple, complex and reduced. Trends in Neurosciences, 15(11):414-421, 1992. [ bib ] |
[1666] | I. Segev, J. W. Fleshman, and R. E. Burke. Compartmental model of complex neurons. In C. Koch and I. Segev, editors, Methods in Neuronal modeling, pages 63-96, Cambridge, 1989. MIT Press. [ bib ] |
[1667] | J. P. Segundo, G. P. Moore, L. J. Stensaas, and T. H. Bullock. Sensitivity of neurons in Aplysia to temporal patterns of arriving impulses. J. Exp. Biol., 40:643-667, 1963. [ bib ] |
[1668] | A. R. Seitz, J. E. Nanez, S. R. Holloway, S. Koyama, and T. Watanabe. Seeing what is not there shows the cost of perceptual learning. PNAS, 102:9080-9085, 2005. [ bib ] |
[1669] | T. Sejnowski. Storing covariance with nonlinearly interacting neurons. J. Mathematical Biology, 4:303-321, 1977. [ bib ] |
[1670] | T. Sejnowski. On the stochastic dynamics of neuronal interaction. Biological Cybernetics, 22:203-211, 1976. [ bib ] |
[1671] | T. Sejnowski and O. Paulsen. Network Oscillations: Emerging Computational Principles. Journal of Neuroscience, 26(6):1673-1676, 2006. [ bib ] |
[1672] | T. J. Sejnowski. The book of hebb. Neuron, 24:773-776, 1999. [ bib ] |
[1673] | T. J. Sejnowski. Pattern recognition. time for a new neural code? Nature, 376(6535):21-22, Jul 1995. [ bib | DOI | http ] |
[1674] | T. J. Sejnowski and G. Tesauro. The Hebb rule for synaptic plasticity: algorithms and implementations. In J. H. Byrne and W. O. Berry, editors, Neural Models of Plasticity, chapter 6, pages 94-103. Academic Press, 1989. [ bib ] |
[1675] | W. Senn. Beyond spike timing: the role of non-linear plasticity and unreliable synapses. Biological Cybernetics, 87:344-355, 2002. [ bib ] |
[1676] | W. Senn, M. Schneider, and B. Ruf. Activity-dependent development of axonal and dendritic delays or, why synaptic transmission should be unreliable. Neural Computation, 14:583-619, 2002. [ bib ] |
[1677] | W. Senn, M. Tsodyks, and H. Markram. An algorithm for synaptic modification based on exact timing of pre-and postsynaptic action potentials. In W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, editors, Artificial Neural Networks - ICANN'97, Lecture Notes in Computer Science 1327, pages 121-126. Springer, 1997. [ bib ] |
[1678] | W. Senn, M. Tsodyks, and H. Markram. An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Computation, 13:35-67, 2001. [ bib ] |
[1679] | W. Senn, M. Tsodyks, and H. Markram. An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Computation, 13:35-67, 2001. [ bib ] |
[1680] | W. Senn, K. Wyler, J. Streit, M. Larkum, H.-R. Lüscher, M. H, L. Müller, D. Steinhauser, K. Vogt, and T. Wannier. Dynamics of random neural network with synaptic depression. Neural Networks, 9:575-588, 1996. [ bib ] |
[1681] | P. Seriès, P. Latham, and A. Pouget. Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations. Nature Neuroscience, 7:1129-1135, 2004. [ bib ] |
[1682] | H. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron, 40:1063-1073, 2003. [ bib ] |
[1683] | H. Seung. Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission. Neuron, 40(6):1063-1073, 2003. [ bib ] |
[1684] | H. S. Seung and H. Sompolinski. Simple models for reading neuronal population codes. PNAS, 90:10749-10753, 1993. [ bib ] |
[1685] | B. Seymour, J. O'Doherty, P. Dayan, M. Koltzenburg, A. Jones, R. J. Dolan, K. Friston, and R. Frackowiak. Temporal difference models describe higher order learning in humans. Nature, 429:664-667, 2004. [ bib ] |
[1686] | M. Shadlen and W. T. Newsome. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. of Neuroscience, 18:3870-3896, 1998. [ bib ] |
[1687] | M. N. Shadlen and J. A. Movshon. Synchrony unbound: a critical evaluation of the temporal binding hypothesis. Neuron, 24(1):67-77, 1999. [ bib ] |
[1688] | M. N. Shadlen and W. T. Newsome. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci, 18(10):3870-3896, 1998. [ bib ] |
[1689] | M. N. Shadlen and W. T. Newsome. Noise, neural codes and cortical organization. Current Opininon in Neurobiology, 4:569-579, 1994. [ bib ] |
[1690] | S. A. Shamma. Stereausis: Binaural processing without neuronal delays. J. Acoust. Soc. Am., 86(3):989-1006, 1989. [ bib ] |
[1691] | E. P. Sharp, H. T. Blair, and J. Cho. The anatomical and computational basis of the rat head-direction cell signal. Trends in Neurosciences, 24(5):289-294, 2001. [ bib ] |
[1692] | P. Sharp. Multiple spatial/behavioral correlates for cells in the rat postsubiculum: Multiple regression analysis and comparison to other hippocampal areas. Cerebral Cortex, 6:238-259, 1996. [ bib ] |
[1693] | P. Sharp and C. Green. Spatial correlates of firing patterns of single cells in the subiculum of the freely moving rat. Journal of Neuroscience, 14:2339-2356, 1994. [ bib ] |
[1694] | P. Sharp, J. Kubie, and R. Muller. Firing properties of hippocampal neurons in a visually symmetrical environment: Contributions of multiple sensory cues and mnemonic properties. Journal of Neuroscience, 10:3093-3105, 1990. [ bib ] |
[1695] | P. E. Sharp. Regional distribution and variation in the firing properties of head direction cells. In S. I. Wiener and S. Taube, editors, Head direction cells and the neural mechanisms of spatial orientation, chapter 1, pages 3-15. MIT Press, 2005. [ bib ] |
[1696] | P. E. . Sharp. Computer simulation of hippocampal place cells. Psychobiology, 19(2):103-115, 1991. [ bib ] |
[1697] | J. Shaw. Unifying Perception and Curiosity. PhD thesis, University of Rochester, 2006. [ bib ] |
[1698] | J. Shaw. Predictive coding with temporal invariance. Technical report, University of Rochester, 2003. [ bib | http ] |
[1699] | C. Shawn and D. Bavelier. Action video game modifies visual selective attention. Nature, 423:534-537, 2003. [ bib ] |
[1700] | R. Shema, T. C. Sacktor, and Y. Dudai. Rapid erasure of long-term memory associations in the cortex by an inhibitor of pkm zeta. Science, 317(5840):951-953, Aug 2007. [ bib | DOI | http ] |
[1701] | G. M. Shepherd. The synaptic organization of the brain. Oxford University Press, Oxford, 1990. [ bib ] |
[1702] | G. M. Shepherd. Neurobiology. Oxford University Press, Oxford, 2nd edition, 1988. [ bib ] |
[1703] | J. D. Shepherd and R. L. Huganir. The cell biology of synaptic plasticity: Ampa receptor trafficking. Annu Rev Cell Dev Biol, 23:613-643, 2007. [ bib | DOI | http ] |
[1704] | C. Sherrington. The Integrative Action of the Nervous System. C. Scribner's sons, 1906. [ bib ] |
[1705] | D. Sheynikhovich, R. Chavarriaga, T. Strösslin, and W. Gerstner. Spatial representation and navigation in a bio-inspired robot. In M. E. Stefan Wermter, Günther Palm, editor, Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience, pages 245-264, 2005. [ bib ] |
[1706] | D. Sheynikhovich, R. Chavarriaga, T. Strösslin, and W. Gerstner. Spatial representation and navigation in a bio-inspired robot, 2005. [ bib | http ] |
[1707] | M. Shiino and M. Frankowicz. Synchronization of infinitely many coupled limit cycle oscillators. Physics Letters A, 136:103-108, 1989. [ bib ] |
[1708] | S. Shinomoto, Y. Sakai, and S. Funahashi. The ornstein-uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex. Neural Comput, 11(4):935-951, 1999. [ bib ] |
[1709] | S. Shipp and S. Zeki. The functional organization of area V2, I: specialization across stripes and layers. Visual Neuroscience, 19(2):187-210, Mar. 2002. [ bib ] |
[1710] | Shiu and Pashler. Improvement in line orientation discrimination is retinally local but dependant on cognitive set. Perception and Psychophysics, 52:582-588, 1992. [ bib ] |
[1711] | H. Shouval, M. Bear, and L. Cooper. A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proceedings of the National Academy of Sciences, 99(16):10831, 2002. [ bib ] |
[1712] | H. Z. Shouval, M. F. Bear, and L. N. Cooper. A unified model of nmda receptor dependent bidirectional synaptic plasticity. Proc. Natl. Acad. Sci. USA, 99:10831-10836, 2002. [ bib ] |
[1713] | H. Z. Shouval, G. C. Castellani, B. S. Blais, L. C. Yeung, and L. N. Cooper:. Converging evidence for a simplified biophysical model of synaptic plasticity. Biol. Cybernetics, 87:383-391, 2002. [ bib ] |
[1714] | H. Z. Shouval and M. P. Perrone. Post-Hebbian learning rules. In M. A. Arbib, editor, The handbook of brain theory and neural networks, pages 645-748. MIT-Press, 1995. [ bib ] |
[1715] | O. Shriki, D. Hansel, and H. Sompolinsky. Rate models for conductance-based cortical neuronal networks. Neural Computation, 15:1809-1841, 2003. [ bib ] |
[1716] | Y. Shu, A. Hasenstaub, and D. McCormick. Turning on and off recurrent balanced cortical activity. Nature, 423:288-293, 2003. [ bib ] |
[1717] | W. M. Siebert and P. R. Gray. Random process model for the firing pattern of single auditory nerve fibers. Q. Prog. Rep. Lab. of Elec. MIT, 71:241, 1963. [ bib ] |
[1718] | R. K. Siegel. Hallucinations. Sci. Am., 237(4):132-140, 1977. [ bib ] |
[1719] | R. K. Siegel and L. J. West. Hallucinations: Behavior, Experience, and Theory. Wiley, New York, 1975. [ bib ] |
[1720] | A. Siegert. On the first passage time probability problem. Phys. Rev., 81:617-623, 1951. [ bib ] |
[1721] | M. Sigman and C. D. Gilbert. Learning to find a shape. Nature Neuroscience, 3:264-269, 2000. [ bib ] |
[1722] | G. Silberberg, M. Bethge, H. Markram, K. Pawelzik, and M. Tsodyks. Dynamics of population rate codes in ensembles of neocortical neurons. J. Neurophysiology, 91:704-709, 2004. [ bib ] |
[1723] | A. M. Sillito, H. E. Jones, G. L. Gerstein, and D. C. West. Feature-linked synchronization of thalamic relay cell firing induced by feedback from the visual cortex. Nature, 369:479-482, 1994. [ bib ] |
[1724] | A. M. Sillito, J. A. Kern, J. A. Milson, and N. Berardi. A re-evaluation of the mechanisms underlying simple cell orientation selectivity. Brain Res., 194:517-520, 1980. [ bib ] |
[1725] | E. Simoncelli, L. Paninski, J. Pillow, and O. Schwarz. Characterization of neural responses with stochastic stimuli. In M. Gazzaninga, editor, The new cognitive neuroscience. MIT Press, 3rd edition, 2004. [ bib ] |
[1726] | E. Simoncelli and O. Schwartz. Modeling surround suppresion in v1 neurons with a statistically derived normalization model. Advances in Neural Information Processing, 11:153-159, 1999. [ bib ] |
[1727] | L. Sincich and J. Horton. The circuitry of V1 and V2: integration of color, form, and motion. Annual Reviews of Neuroscience, 28:303-326, 2005. [ bib ] |
[1728] | W. Singer. The formation of cooperative cell assemblies in the visual cortex. In J. Krüger, editor, Neural cooperativity, pages 165-183, Berlin Heidelberg New York, 1991. Springer. [ bib ] |
[1729] | W. Singer. The role of synchrony in neocortical processing and synaptic plasticity, pages 141-173. Springer, Berlin Heidelberg New York, 1994. [ bib ] |
[1730] | W. Singer. A new job for the thalamus. Nature, 369:444-445, 1994. [ bib ] |
[1731] | W. Singer. Gehirn und Bewusstsein - mit einer Einfuehrung von Wolf Singer. Spektrum-Verlag, Heidelberg, 1994. [ bib ] |
[1732] | W. Singer. Synchronization of cortical activity and its putative role in information processing and learning. Annu. Rev. Physiol., 55:349-374, 1993. [ bib ] |
[1733] | R. Sireteanu and R. Rettenbach. Perceptual learning in visual search: fast, enduring, but non-specific. Vision Research, 35:2037-2043, 1995. [ bib ] |
[1734] | J. Sirosh. A self-organizing neural network model of the primary visual cortex. Ph.D. thesis AI95-237, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1995. [ bib ] |
[1735] | P. Sjöström and M. Häusser. A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons. Neuron, 51(2):227-238, 2006. [ bib ] |
[1736] | P. Sjöström and S. Nelson. Spike timing, calcium signals and synaptic plasticity. Current Opinion in Neurobiology, 12:305-314, 2002. [ bib ] |
[1737] | P. Sjöström, G. Turrigiano, and S. Nelson. Endocannabinoid-dependent neocortical layer-5 ltd in the absence of postsynaptic spiking. J. Neurophysiol., 92:3338-3343, 2004. [ bib ] |
[1738] | P. Sjöström, G. Turrigiano, and S. Nelson. Neocortical ltd via coincident activation of presynaptic nmda and cannabinoid receptors. Neuron, 39:641-654, 2003. [ bib ] |
[1739] | P. Sjöström, G. Turrigiano, and S. Nelson. Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron, 32:1149-1164, 2001. [ bib ] |
[1740] | P. J. Sjöström and S. B. Nelson. Spike timing, calcium signals and synaptic plasticity. Current Opinion in Neurobiology, 12:305-314, 2003. [ bib ] |
[1741] | P. J. Sjöström, G. G. Turrigiano, and S. B. Nelson. Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron, 32(6):1149-1164, Dec 2001. [ bib ] |
[1742] | W. E. Skaggs, J. J. Knierim, H. S. Kudrimoti, and B. L. McNaughton. A model of the neural basis of the rat's sense of direction. Advances in neural information processing systems, 7:173-80, 1995. [ bib ] |
[1743] | W. E. Skaggs and B. L. McNaughton. Spatial firing properties of hippocampal CA1 populations in an environment containing two visually identical regions. Journal of Neuroscience, 18(20):8455-8466, 1998. [ bib ] |
[1744] | N. Slonim. Information Bottlneck theory and applications. PhD thesis, Hebrew University of Jerusalem, 2003. [ bib ] |
[1745] | N. Slonim, N. Friedman, and N. Tishby. Multivariate information bottleneck. Neural Computation, 18:1739-1789, 2006. [ bib ] |
[1746] | N. Slonim and N. Tishby. Document clustering using word clusters via the information bottleneck method. In N. Belkin, P. Ingwersen, and M.-K. Leong, editors, Proc. Research and Development in Information Retrieval (SIGIR-00), pages 208-215. ACM press, New York, 2000. [ bib ] |
[1747] | N. Slonim and Y. Weiss. Maximum likelihood and the information bottleneck. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, 2002. [ bib ] |
[1748] | A. Smith, K. Singh, A. Williams, and M. Greenlee. Estimating Receptive Field Size from fMRI Data in Human Striate and Extrastriate Visual Cortex. Cerebral Cortex, 11(12):1182-1190, 2001. [ bib | DOI ] |
[1749] | G. D. Smith, C. Cox, S. Sherman, and J. Rinzel. Fourier analysis of sinusoidally driven thalamocortical relay neurons and a minimal integrate-and-fire or burst model. J. Neurophusiology, 83:588-610, 2001. [ bib ] |
[1750] | A. Smithy and J.P.Bolam. The neural network of basal ganglia as revealed by the study of synaptic connections of identified neurons. Trends in Neurosciences, 13:259-265, 1990. [ bib ] |
[1751] | D. Smyth, B. Willmore, G. E. Baker, I. D. Thompson, and D. J. Tolhurst. The receptive-field organization of simple cells in primary visual cortex of ferrets under natural scene stimulation. Journal of Neuroscience, 23(11):4746-4759, June 2003. [ bib ] |
[1752] | H. P. Snippe. Parameter extraction from population codes: a critical assessment. Neural Computation, 8(3):511-529, 1996. [ bib ] |
[1753] | J. E. S. Socolar, G. Grinstein, and C. Jayaprakash. On self-organized criticality in nonconserving systems. Phys. Rev. E., 47:2366-2376, 1993. [ bib ] |
[1754] | W. Softky. Sub-millisecond coincidence detection in active dendritic trees. Neuroscience, 58(1):13-41, 1994. [ bib ] |
[1755] | W. Softky and C. Koch. The highly irregular firing pattern of cortical cells is inconsistent with temporal integration of random epsps. J . Neurosci., 13:334-350, 1993. [ bib ] |
[1756] | W. R. Softky. Simple codes versus efficient codes. Current Opinion in Neurobiology, 5:239-247, 1995. [ bib ] |
[1757] | T. Solstad, E. I. Moser, and G. T. Einevoll. From grid cells to place cells: A mathematical model. Hippocampus, 2006. [ bib ] |
[1758] | D. Somers and N. Kopell. Rapid synchronization through fast threshold modulation. Biol. Cybern., 68:393-407, 1993. [ bib ] |
[1759] | D. Somers, E. Todorov, A. G. Siapas, L. Toth, D. Kim, and M.Sur. A local ciruit approach to understanding integration of long-range inputs in primary visual cortex. Cerebral Cortex, 8:204-217, 1998. [ bib ] |
[1760] | D. C. Somers, S. B. Nelson, and M. Sur. An emergent model of orientation selectivity in cat visual cortical simple cells. J. of Neuroscience, 15:5448-5465, 1995. [ bib ] |
[1761] | C. Sommer. Dynamische Modellierung der synaptischen Plastizität. Diplomarbeit, Technische Universität München, 1994. [ bib ] |
[1762] | H. Sompolinksy, A. Crisanti, and H. Sommers. Chaos in random neural networks. Physical Review Letters, 61:259-262, 1988. [ bib ] |
[1763] | H. Sompolinsky, D. Golomb, and D. Kleinfel. Cooperative dynamics in visual processing. Phys. Rev. A, 43:6990-7011, 1991. [ bib ] |
[1764] | H. Sompolinsky, D. Golomb, and D. Kleinfeld. Global processing of visual stimuli in a neural network of coupled oscillators. Proc. Natl. Acad. Sci. USA, 87:7200-7204, 1990. [ bib ] |
[1765] | H. Sompolinsky and I. Kanter. Temporal association in asymmetric neural networks. Phys. Rev. Lett., 57:2861-2864, 1986. [ bib ] |
[1766] | E. Y. Song, Y. B. Kim, Y. H. Kim, and M. W. Jung. Role of active movement in place-specific firing of hippocampal neurons. Hippocampus, 15:8-17, 2005. [ bib ] |
[1767] | S. Song and L. Abbott. Column and map development and cortical re-mapping through spike-timing dependent plasticity. Neuron, 32(339-350), 2001. [ bib ] |
[1768] | S. Song and L. F. Abbott. Cortical mapping and development through spike timing-dependent plasticity. Neuron, 32:339-350, 2001. [ bib ] |
[1769] | S. Song, K. Miller, and L. Abbott. Competitive Hebbian learning through spike-time-dependent synaptic plasticity. Nature Neuroscience, 3:919-926, 2000. [ bib ] |
[1770] | W. S. Sossin. Defining memories by their distinct molecular traces. Trends Neurosci, 31(4):170-175, Apr 2008. [ bib | DOI | http ] |
[1771] | G. Sperling. The information available in brief visual presentations. Psychol. Monogr., 74(11 Whole No. 498):1-29, 1960. [ bib ] |
[1772] | M. Spiridon, C. Chow, and W. Gerstner. Frequency spectrum of coupled stochastic neurons with refractoriness. In L. Niklasson, M. Bodén, and T. Ziemke, editors, ICANN'98, pages 337-342. Springer, 1998. [ bib ] |
[1773] | M. Spiridon, C. Chow, and W. Gerstner. Effect of correlations on signal transmission in a population of spiking neurons. Neurocomputing, 32-33:529-535, 2000. [ bib ] |
[1774] | M. Spiridon and W. Gerstner. Effect of lateral connections on the accuracy of the population code for a network of spiking neurons. Network: Computation in Neural Systems, 12:409-421257-272, 2001. [ bib ] |
[1775] | M. Spiridon and W. Gerstner. Noise spectrum and signal transmission trough a population of spiking neurons. Network: Computation in Neural Systems, 10:257-272, 1999. [ bib ] |
[1776] | O. Sporns, J. A. Gally, G. N. Reeke, and G. M. Edelman. Reentrant signaling among simulated neuronal groups leads to coherency in their oscillatory activity. Proc. Natl. Acad. Sci. USA, 86:7265-7269, 1989. [ bib ] |
[1777] | O. Sporns, G. Tononi, and G. Edelman. Reentry and dynamical interactions of cortical networks. In E. Domany, J. van Hemmen, and K. Schulten, editors, Models of neural networks 2, page Chap. 9?, New York, 1994. Springer. [ bib ] |
[1778] | O. Sporns, G. Tononi, and G. M. Edelman. Modeling perceptual grouping and figure-ground segregation by means of active reentrent connections. Proc. Natl. Acad. Sci. USA, 88:129-133, 1991. [ bib ] |
[1779] | H. Sprekeler, C. Michaelis, and L. Wiskott. Slowness: An objective for spike-timing-plasticity? PLoS Computational Biology, 3(6):e112, 2007. [ bib ] |
[1780] | H. Sprekeler and L. Wiskott. Analytical derivation of complex cell properties from the slowness principle. In Proceedings CNS 2006, 2006. [ bib ] |
[1781] | J. C. Sprott. Chaos and Time-Series Analysis. 2003. [ bib ] |
[1782] | M. V. Srinivasan, S. B. Laughlin, and A. Dubs. Predictive coding: a fresh view of inhibition in the retina. Proceedings of the Royal Society of London. Series B, Biological Sciences, 216(1205):427-459, Nov 1982. [ bib ] |
[1783] | R. W. Stackman, E. J. Golob, J. P. Bassett, and J. S. Taube. Passive transport disrupts directional path integration by rat head direction cells. Journal of Neurophysiology, 90:2862-2874, 2003. [ bib ] |
[1784] | R. W. Stackman and J. S. Taube. Firing properties of rat lateral mammillary single units: head direction, head pitch and angular head velocity. Journal of Neuroscience, 18(21):9020-9037, 1998. [ bib ] |
[1785] | R. W. Stackman and M. B. Zugaro. Self-motion cues and resolving intermodality conflicts: Head direction cells, place cells, and behavior. In S. I. Wiener and S. Taube, editors, Head direction cells and the neural mechanisms of spatial orientation, chapter 7, pages 137-162. MIT Press, 2005. [ bib ] |
[1786] | P. K. Stanton and T. J. Sejnowski. Associative long-term depression in the hippocampus induced by hebbian covariance. Nature, 339:215-218, 1989. [ bib ] |
[1787] | H.-A. Steffenach, M. Witter, M.-B. Moser, and E. Moser. Spatial memory in the rat requires the dorsolateral band of the entorhinal cortex. Neuron, 45(2):301-313, 2005. [ bib ] |
[1788] | R. B. Stein. The information capacity of nerve cells using a frequency code. Biophys. J., 7:797-826, 1967. [ bib ] |
[1789] | R. B. Stein. Some models of neuronal variability. Biophys. J., 7:37-68, 1967. [ bib ] |
[1790] | R. B. Stein. A theoretical analysis of neuronal variability. Biophys. J., 5:173-194, 1965. [ bib ] |
[1791] | P. N. . Steinmetz, A. Roy, P. J. Fitzgerald, S. S. Hsiao, K. Johnson, and E. Niebur. Attention modultaes synchronized neuronal firing in primate somatosensory cortex. Nature, 404:187-190, 2000. [ bib ] |
[1792] | P. N. Steinmetz, A. Manwani, C. Koch, M. London, and I. Segev. Subthreshold voltage noise due to channel fluctuations in active neuronal membranes. J. Computational Neuroscience, 9:133-148, 2000. [ bib ] |
[1793] | M. Stemmler. A single spike suffices: the simplest form of stochastic resonance in model neurons. Network, 7:687-716, 1996. [ bib ] |
[1794] | M. Stemmler and C. Koch. How voltage-dependent conductances can adapt to maximize the information encoded by neurons. Nature Neuroscience, 2:521-527, 1999. [ bib ] |
[1795] | M. Stemmler, M. Usher, and E. Niebur. Lateral interactions in primary visual cortex: a model bridging physiology and psychophysics. Science, 269:1877-1880, 1998. [ bib ] |
[1796] | M. Steriade. Thalamic Oscillations and Signalling. John Wiley, New York, 1990. [ bib ] |
[1797] | M. Steriade, I. Timoveev, and F. Grenier. Natural waking and sleep states: a view from inside neocortical neurons. J. Neurophysiol., 85:1969-1985, 2001. [ bib ] |
[1798] | C. F. Stevens and Y. Wang. Changes in reliability of synaptic function as a mechanism for plasticity. Nature, 371:704-707, 1994. [ bib ] |
[1799] | C. F. Stevens and A. M. Zador. Noel integrate-and-fire like model of repetitive firing in cortical neurons. In Proc. of the 5th Joint Symposium on Neural Computation, page Report. can be downloaded from http://www.sloan.salk.edu/ zador/rep_fire_inc/rep_fire_inc.html, 1998. [ bib ] |
[1800] | C. F. Stevens and A. M. Zador. Input synchrony and the irregular firing of cortical neurons. Nature Neuroscience, 1:210-217, 1998. [ bib ] |
[1801] | A. Stocker and E. Simoncelli. Constraining a bayesian model of human visual speed perception. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1361-1368. MIT Press, Cambridge, MA, 2005. [ bib ] |
[1802] | J. V. Stone. Blind source separation using temporal predictability. Neural Computation, 13(7):1559-1574, 2001. [ bib ] |
[1803] | J. V. Stone and A. Bray. A learning rule for extracting spatio-temporal invariances. Network: Computation in Neural Systems, 6:429-436, 1995. [ bib ] |
[1804] | P. Stopka and D. Macdonald. Way-marking behaviour: an aid to spatial navigation in the wood mouse (apodemus sylvaticus). BMC Ecology, 3:3, 2003. [ bib ] |
[1805] | T. Stroesslin, D. Sheynikhovich, R. Chavarriaga, and W. Gerstner. Robust self-localisation and navigation based on hippocampal place cells. Neural Networks, 18:1125-1140, 2005. [ bib ] |
[1806] | S. H. Strogatz. Nonlinear dynamical systems and chaos. Addison Weslsy, Reading MA, 1994. [ bib ] |
[1807] | S. H. Strogatz and R. E. Mirollo. Stability of incoherence in a population of coupled oscillators. J. Stat. Phys., 63:613-635, 1991. [ bib ] |
[1808] | S. H. Strogatz, R. E. Mirollo, and P. C. Matthews. Coupled nonlinear oscillators below the synchronization threshold: Relaxation be generalized landau damping. Phys. Rev. Lett., 68:2730-2733, 1992. [ bib ] |
[1809] | G. Stuart and M. Häusser. Dendritic coincidence detection of epsps and action potentials. Nature Neuroscience, 4:63-71, 2001. [ bib ] |
[1810] | G. Stuart and M. Häusser. Dendritic coincidence detection of EPSPs and action potentials. Nature Neuroscience, 4(1):63-71, 2001. [ bib ] |
[1811] | G. Stuart, J. Schiller, and B. Sakmann. Action potential initiation and propagation in rat neocortical pyramidal neurons. J Physiol, 505(3):617-632, 1997. [ bib ] |
[1812] | G. Stuart, N. Spruston, and M. Häusser. Dendrites. Oxford University Press, Oxford, 2nd ed edition, 2007. [ bib | .html ] |
[1813] | G. Stuart, N. Spruston, and M. Häusser. Action potential initiation and backpropagation in the mammalian CNS. Trends in Neuroscience, 20:125-131, 1997. [ bib ] |
[1814] | G. J. Stuart and B. Sakmann. Active propagation of somatic action potentials into neocortical pyramidal cell dendrites. Nature, 367:69-72, 1994. [ bib ] |
[1815] | T. J. Sullivan and V. R. de Sa. Homeostatic synaptic scaling in self-organizing maps. Neural Networks, 19:734-743, 2006. [ bib ] |
[1816] | W. E. Sullivan and M. Konishi. Neural map of interaural phase difference in the owl's brainstem. Proc. Natl. Acad. Sci USA, 83:8400-8404, 1986. [ bib ] |
[1817] | W. E. Sullivan and M. Konishi. Segregation of stimulus phase and intensity coding in the Cochlear Nucleus of the barn owl. J. Neurosci., 4(7):1787-1799, 1984. [ bib ] |
[1818] | M. Sur and C. Leamey. Development and plasticity of cortical areas and networks. Nature Reviews Neuroscience, 2:251-262, 2001. [ bib ] |
[1819] | R. Suri and W. Schultz. Temporal difference model reproduces anticipatory neural activity. Neural Computation, 13:841-862, 2001. [ bib ] |
[1820] | R. E. Suri and W. Schultz. A neural network with dopamine-like reinforcement signal that learns a spatial delayed response task. Neuroscience, 91, 1999. [ bib ] |
[1821] | R. E. Suri and W. Schultz. Learning of sequential movements with dopamine-like reinforcement signal in neural network model. Exp. Brain Res., 121:350-354, 1998. [ bib ] |
[1822] | R. E. Suri and T. Sejnowski. Spike propagation synchronized by temporally asymmetric hebbian learning. Biol. Cybern., 87:440-445, 2002. [ bib ] |
[1823] | S. Sutherland. Only four possible solutions. Nature, 353:389-390, 1991. [ bib ] |
[1824] | J. P. Sutton, J. S. Beis, and L. E. H. Trainor. Hierarchical model of memory and memory loss. J. Phys. A, 21:4443-4454, 1988. [ bib ] |
[1825] | R. Sutton. Learning to predict by the method of temporal differences. Machine learning, 3:9-44, 1998. [ bib ] |
[1826] | R. Sutton and A. Barto. Time-derivative models of pavlovian reinforcement. In M. Gabriel and J. Moore, editors, Learning and Computational Neuroscience: Foundations of Adaptive Networks, pages 497-537. MIT-Press, Cambridge, 1990. [ bib ] |
[1827] | R. Sutton and A. Barto. Reinforcement learning. MIT Press, Cambridge, 1998. [ bib ] |
[1828] | R. Sutton and A. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998. [ bib ] |
[1829] | R. S. Sutton. Learning to predict by the methods of temporal differences. Machine Learning, 3(1):9-44, Aug. 1988. [ bib | http ] |
[1830] | R. S. Sutton and A. G. Barto. Towards a modern theory of adaptive networks: expectation and prediction. Psychol. Review, 88:135-171, 1981. [ bib ] |
[1831] | R. S. Sutton and S. D. Whitehead. Online learning with random representations. In Proc. 1995 conference on machine learning, pages 314-321. Morgan Kaufman, 1993. [ bib ] |
[1832] | W. Suzuki and D. Amaral. Functional neuroanatomy of the medial temporal lobe memory system. Cortex, 40(1):220-222, 2004. [ bib ] |
[1833] | G. Svirskis and J. Rinzel. Influence of subthrshold nonlinearities on signal-to-noise ration and timing precision for small signals in neurons: minimal model analysis. Network: Comput. Neural Syst., 14:137-150, 2003. [ bib ] |
[1834] | K. Svoboda, W. Denk, D. Kleinfeld, and D. Tank. In vivo dendritic calcium dynamics in neocortical pyramidal neurons. Nature, 185:161-165, 1997. [ bib ] |
[1835] | N. V. Swindale. A model for the formation of orientation columns. Proc. Royal Soc. London B, 215:211-230, 1982. [ bib ] |
[1836] | B. Takacs and A. Lorincz. Independent component analysis forms place cells in realistic robot simulations. Neurocomputing, 69(10-12):1249-1252, 2006. [ bib ] |
[1837] | S. Tanabe, K. Pakdaman, T. Nomura, and S. Sato. Dynamics of an ensemble of leaky integrate-and-fire neuron models and its response to a pulse input. Technical Report of IEICE, pages NLP98-14, 1998. [ bib ] |
[1838] | M. Tanaka, H. Weber, and O. D. Creutzfeldt. Visual properties and spatial distribution of neurones in the visual association area on the prelunate gyrus of the awake monkey. Experimental Brain Research, 65(1):11-37, 1986. [ bib ] |
[1839] | S. Tanaka, K. Doya, G. Okada, K. Ueda, Y. Okamoto, and S. Yamawaki. Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Nature Neuroscience, 7(8):887-893, 2004. [ bib ] |
[1840] | H. Tanila, M. Shapiro, and H. Eichenbaum. Discordance of spatial representations in ensembles of hippocampal place cells. Hippocampus, 7:613-623, 1997. [ bib ] |
[1841] | H. Tanila, P. Sipila, M. Shapiro, and H. Eichenbaum. Brain aging: Impaired coding of novel environmental cues. Journal of Neuroscience, 17:5167-5174, 1997. [ bib ] |
[1842] | D. W. Tank, A. Gelperin, and D. Kleinfeld. Odors, oscillations, and waves: Does it all compute? Science, 265:1819-1820, 1994. [ bib ] |
[1843] | T. Tateno, A. Harsch, and H. Robinson. Threshold firing frequency - current relationships of neurons in rat somatosensory cortex: Type 1 and type 2 dynamics. J. Neurophysiology, 92:2283-2294, 2004. [ bib ] |
[1844] | J. Taube. Place cells recorded in the parasubiculum of freely moving rats. Hippocampus, 6(5):569-583, 1995. [ bib ] |
[1845] | J. S. Taube and J. P. Bassett. Persistent neural activity in head direction cells. Cerebral Cortex, 13:1162-1172, 2003. [ bib ] |
[1846] | J. S. Taube and R. U. Muller. Comparisons of head direction cell activity in the postsubiculum and anterior thalamus of freely moving rats. Hippocampus, 8:87-108, 1998. [ bib ] |
[1847] | J. S. Taube, R. U. Muller, and J. B. J. Ranck. Head direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations. Journal of Neuroscience, 2(10):436-447, 1990. [ bib ] |
[1848] | J. Tegner and A. Kepecs. An adaptive spike-timing-dependent plasticity rule. Neurocomputing, 44(46):189-194, 2002. [ bib ] |
[1849] | A. F. Teich and N. Qian. Learning and adaptation in a recurrent model of v1 orientation selectivity. The Journal of Neurophysiology, 89:2086-2100, 2003. [ bib ] |
[1850] | J. Tenenbaum, V. Silva, and J. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290(5500):2319-2323, 2000. [ bib ] |
[1851] | A. Terashima, K. A. Pelkey, J.-C. Rah, Y. H. Suh, K. W. Roche, G. L. Collingridge, C. J. McBain, and J. T. R. Isaac. An essential role for pick1 in nmda receptor-dependent bidirectional synaptic plasticity. Neuron, 57(6):872-882, Mar 2008. [ bib | DOI | http ] |
[1852] | D. Terman, N. Kopell, and A. Bose. Dynamics of mutually coupled slow inhibitory neurons. Physica D, 117:241-275, 1998. [ bib ] |
[1853] | D. Terman and D. Wang. Global competition and local cooperation in a network of neural oscillators. Physica D, 81:148-176, 1995. [ bib ] |
[1854] | G. Tesauro. Td-gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation, 6:215-219, 1994. [ bib ] |
[1855] | F. Theunissen, S. David, N. Sing, A. Hsu, W. Vinje, and J. Gallant. Estimating spatio-temporal receptive fiels of auditory and visual neurons form their responses to natural stimuli. Network, 12:289-316, 2001. [ bib ] |
[1856] | F. Theunissen and J. Miller. Temporal encoding in nervous systems: a rigorous definition. J. Comput. Neurosci,, 2:149-162, 1995. [ bib ] |
[1857] | R. F. Thompson. The brain. W. H. Freeman and Company, New York, 2nd edition, 1993. [ bib ] |
[1858] | R. F. Thompson. Das Gehirn. Spektrum der Wissenschaft, Heidelberg, 1990. [ bib ] |
[1859] | A. Thomson and C. Lamy. Functional maps of neocortical local circuitry. Frontiers in Neuroscience, 1:19-42, 2007. [ bib ] |
[1860] | A. M. Thomson and D. West. Presynaptic frequency filtering in the gamma frequency band; dual intracellular recordings in slices of adult rat and cat neocortex. Cerebral Cortex, 13:136-143, 2003. [ bib ] |
[1861] | E. Thorndike. Animal Intelligence. Hafner, Darien, CT, 1911. [ bib ] |
[1862] | S. Thorpe, A. Delorme, and R. Van Rullen. Spike-based strategies for rapid processing. Neural Networks, 14:715-725, 2001. [ bib ] |
[1863] | S. Thorpe, D. Fize, and C. Marlot. Speed of processing in the human visual system. Nature, 381:520-522, 1996. [ bib ] |
[1864] | S. Thrun. Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99:21-71, 1998. [ bib ] |
[1865] | P. Tiesinga, J.-M. Fellous, and T. J. Sejnowski. Regulation of spike timing in visual cortical circuits. Nature Reviews Neuroscience, 9:97-107, 2008. [ bib | .pdf ] |
[1866] | N. Tishby, F. Pereira, and W. Bialek. The information bottleneck method. In Proceedings of the 37-th Annual Allerton Conference on Communication, Control and Computing, pages 368-377, 1999. [ bib | .html ] |
[1867] | M. Toledo-Rodriguez, B. Blumenfeld, C. Wu, J. Luo, B. Attali, P. Goodman, and H. Markram. Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cereb Cortex, 14(12):1310-1327, 2004. [ bib | DOI ] |
[1868] | M. Toledo-Rodriguez, B. Blumenfeld, C. Wu, J. Luo, B. Attali, P. Goodman, and H. Markram. Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cerebral Cortex, 14:1310-1327, 2004. [ bib ] |
[1869] | D. Tolhurst, J. Movshon, and A. Dean. The statistical reliability of signals in single neurons in cat and monkey visual-cortex. Vision Research, 23:775-785, 1983. [ bib ] |
[1870] | E. C. Tolman. Cognitive maps in rats and man. Psychological Review, 55:189-208, 1948. Definition of the cognitive map. [ bib ] |
[1871] | A. Tonnelier. The piecewise linear fitzhugh-nagumo model. I the space clamped system. Technical Report, University of Grenoble, xx:xx, 2002. [ bib ] |
[1872] | A. Tonnelier and W. Gerstner. Piecewise linear differential equations and integrate-and-fire neurons : insights from two-dimensional membrane models. Phys. Rev. E, 67:21908, 2003. [ bib ] |
[1873] | C. Torborg and M. Feller. Spontaneous patterned retinal activity and the refinement of retinal projections. Progress in Neurobiology, 76(4):312-235, 2005. [ bib ] |
[1874] | J. Touboul. Bifurcation analysis of a general class of nonlinear integrate-and-fire neurons. SIAM Journal on Applied Mathematics, 68(4):1045-1079, 2008. [ bib ] |
[1875] | D. S. Touretzky. Advances in Neural Information Processing Systems, volume 2. Morgan Kaufmann Publishers, San Mateo, 1990. [ bib ] |
[1876] | J. Touryan, G. Felsen, and Y. Dan. Spatial structure of complex cell receptive fields measured with natural images. Neuron, 45(5):781-791, Mar 2005. [ bib ] |
[1877] | M. J. Tovee and E. T. Rolls. Information encoding in short firing rate epochs by single neurons in the primate temporal visual cortex. Visual Cognition, 2(1):35-58, 1995. [ bib ] |
[1878] | M. J. Tovee, E. T. Rolls, A. Treves, and R. P. Belles. Information encoding and the responses of single neurons in the primate visual cortex. J. Neurophysiol., 70:640-654, 1993. [ bib ] |
[1879] | T. Toyoizumi, J.-P. Pfister, K. Aihara, and W. Gerstner. Optimality model of unsupervised spike-timing dependent plasticity: Synaptic memory and weight distribution. Neural comutation, 19:639-671, 2007. [ bib ] |
[1880] | T. Toyoizumi, J. Pfister, K. Aihara, and W. Gerstner. Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution. Neural Computation, 19(3):639, 2007. [ bib ] |
[1881] | T. Toyoizumi, J.-P. Pfister, K. Aihara, and W. Gerstner. Generalized bienenstock-cooper-munro rule for spiking neurons that maximizes information transmission. Proc. National Academy Sciences (USA), 102:5239-5244, 2005. [ bib ] |
[1882] | T. Toyoizumi, J. Pfister, K. Aihara, and W. Gerstner. Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. Proceedings of the National Academy of Sciences, 102(14):5239-5244, 2005. [ bib ] |
[1883] | T. Toyoizumi, J.-P. Pfister, K. Aihara, and W. Gerstner. Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1409-1416. MIT Press, Cambridge, MA, 2005. [ bib ] |
[1884] | R. Traub, A. Bibbig, F. LeBeau, E. Buhl, and M. Whittington. Cellular mechanisms of neuronal population oscillations in the hippocampus in vitro. Annu. Rev. Neurosci., 27:247-278, 2004. [ bib ] |
[1885] | R. D. Traub and R. Miles. Neural Networks of the Hippocampus. Cambridge University Press, Cambridge, 1991. [ bib ] |
[1886] | R. D. Traub, R. K. S. Wong, R. Miles, and H. Michelson. A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. J. Neurophysiol., 66:635-650, 1991. [ bib ] |
[1887] | T. Trefz. Oszillationen im cortex. Diplomarbeit, Technische Universität München, 1991. [ bib ] |
[1888] | A. Treves. Mean-field analysis of neuronal spike dynamics. Network, 4:259-284, 1993. [ bib ] |
[1889] | A. Treves. Local neocortical processing: a time for recognition. Int. J. of Neural Systems, 3 (Supp):115-119, 1992. [ bib ] |
[1890] | A. Treves. Graded-response neurons and information encoding in autoassociative memory. Phys. Rev. A, 42:2418-2430, 1990. [ bib ] |
[1891] | A. Treves, O. Miglino, and D. Parisi. Rats, nets, maps and the emergence of place cells. Psychobiology, 20(1):1-8, 1992. [ bib ] |
[1892] | A. Treves and E. T. Rolls. A computational analysis of the role of the hippocampus in learning and memory. Hippocampus, 4(3):373-391, 1994. [ bib ] |
[1893] | A. Treves, E. T. Rolls, and M. Simmen. Time for retrieval in recurrent associative memories. Physica D, 107:392-400, 1997. [ bib ] |
[1894] | J. Triesch. A gradient rule for the plasticity of a neuron's intrinsic excitability. In W. Duch and al., editors, ICANN 2005, volume 3696 of LNCS, pages 65-70. Springer-Verlag, Berlin Heidelberg, 2005. [ bib ] |
[1895] | J. Triesch. Synergies between intrinsic and synaptic plasticity mechanisms. Neural computation, 19:885 -909, 2007. [ bib ] |
[1896] | T. W. Troyer, A. Krukowski, N. Priebe, and K. Miller. Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity. J. Neuroscience, 18:5908-5927, 1998. [ bib ] |
[1897] | T. W. Troyer and K. Miller. Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell. Neural Computation, 9:971-983, 1997. [ bib ] |
[1898] | O. Trullier, S. Wiener, A. Berthoz, and J.-A. Meyer. Biologically based artificial navigation systems: review and prospects. Progress in Neurobiology, 51:483-544, 1997. [ bib ] |
[1899] | R. Tsien, D. Lipscombe, D. V. Madison, K. Bley, and A. P. Fox. Multiple types of calcium channels and their selective modulations. Trends in Neuroscience, 11:431-438, 1988. [ bib ] |
[1900] | M. Tsodyks and C. Gilbert. Neural networks and perceptual learning. Nature, 431:775-781, 2004. [ bib ] |
[1901] | M. Tsodyks, T. Kenet, A. Grinvald, and A. Arieli. Linking Spontaneous Activity of Single Cortical Neurons and the Underlying Functional Architecture. Science, 286(5446):1943, 1999. [ bib ] |
[1902] | M. Tsodyks and H. Markram. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Academy of Sci., USA, 94:719-723, 1997. [ bib ] |
[1903] | M. Tsodyks, I. Mitkov, and H. Sompolinsky. Patterns of synchrony in inhomogeneous networks of oscillators with pulse interaction. Phys. Rev. Lett., 71:1281-1283, 1993. [ bib ] |
[1904] | M. V. Tsodyks and T. Sejnowski. Rapid state switching in balanced cortical networks. Network, 6:111-124, 1995. [ bib ] |
[1905] | H. C. Tuckwell. Stochastic Processes in the Neurosciences. SIAM, Philadelphia, 1989. [ bib ] |
[1906] | H. C. Tuckwell. Introduction to theoretic neurobiology. Cambridge Univ. Press, Cambridge, 1988. [ bib ] |
[1907] | H. C. Tuckwell. Introduction to theoretic neurobiology, volume 1. Cambridge Univ. Press, Cambridge, 1988. [ bib ] |
[1908] | H. C. Tuckwell. Introduction to theoretic neurobiology, volume 2. Cambridge Univ. Press, Cambridge, 1988. [ bib ] |
[1909] | H. C. Tuckwell. Synaptic transmission in a model for stochastic neural activity. J. Theor. Biology, 71:167-183, 1979. [ bib ] |
[1910] | H. C. Tuckwell. The response of a spatially distributed neuron to white noise current injection. Biol. Cybern., 33:39-55, 1979. [ bib ] |
[1911] | R. Turner and M. Sahani. A Maximum-Likelihood Interpretation for Slow Feature Analysis. Neural Computation, 19(4):1022, 2007. [ bib ] |
[1912] | G. Turrigiano and S. Nelson. Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience, 5:97-107, 2004. [ bib ] |
[1913] | G. G. Turrigiano. Homeostatic signaling: the positive side of negative feedback. Current Opinion in Neurobiology, 17:318-324, 2007. [ bib ] |
[1914] | G. G. Turrigiano, K. R. Leslie, N. S. Desai, L. C. Rutherford, and S. B. Nelson. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature, 391:892-895, 1998. [ bib ] |
[1915] | G. G. Turrigiano and S. B. Nelson. Hebb and homeostasis in neuronal plasticity. Current Opinion in Neurobiology, 10:358-364, 2000. [ bib ] |
[1916] | J. Tyson and J. P. Keener. Singular perturbation theory of travelling waves in excitable media (a review). Physica D, 32:327-361, 1988. [ bib ] |
[1917] | T. Tzounopoulos, Y. Kim, D. Oertel, and L. O. Trussell. Cell-specific, spike timing−dependent plasticities in the dorsal cochlear nucleus. Nature Neuroscience, 7:719-125, 2004. [ bib ] |
[1918] | T. Tzounopoulos, Y. Kim, D. Oertel, and L. O. Trussell. Cell-specific, spike timing-dependent plasticity in the dorsal cochlear nucleus. Nature Neuroscience, 7(7):719-725, 2004. [ bib ] |
[1919] | K. Türker and R. Powers. Black box revisited: a technique for estimating postsynaptic potentials in neurons. Trends in Neurosciences, 28:379-386, 2005. [ bib ] |
[1920] | G. E. Uhlenbeck and L. S. Ornstein. On the theory of the Brownian motion. Phys. Rev, 36:823-841, 1930. [ bib ] |
[1921] | M. Usher, H. G. Schuster, and E. Niebur. Dynamics of populations of integrate-and-fire neurons, partial synchronization and memory. Neural Comp., 5:570-586, 1993. [ bib ] |
[1922] | M. Usher, M. Stemmler, and Z. Olami. Dynamic pattern formation leads to 1/f noise in neural populations. Phys. Rev. Lett., 74:326-329, 1995. [ bib ] |
[1923] | V.Afraimovich, N.Veritchev, and M.Rabinovich. Stochastically synchronized oscillators in dissipative systems. Radiophysics and Quantum Electronics, 29:795, 1986. in Russian. [ bib ] |
[1924] | M. C. Vanier and J. M. Bower. A comparative survey of automated parameter-search methods for compartmental neural models. J Comput Neurosci, 7(2):149-171, 1999. [ bib ] |
[1925] | V. Vapnik. The Nature of Statistical Learning Theory. Springer, 2000. [ bib ] |
[1926] | V. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995. [ bib ] |
[1927] | V. D. Veksler, W. D. Gray, and S. M. J. Categorization and reinforcement learning: State identification in reinforcement learning and network reinforcement learning. In Twenty-Ninth Annual Meeting of the Cognitive Science Society. [ bib ] |
[1928] | F. Verhulst. Nonlinear differential equations and dynamical systems. Springer, Berlin, 1996. [ bib ] |
[1929] | L. Viana and A. Bray. Phase-diagrams for dilute spin-glasses. J. Phys. C, 18:3037-3051, 1985. [ bib ] |
[1930] | R. Vogels and G. A. Orban. The effect of practice on the oblique effect in line orientation judgments. Vision Research, 25(11):1679-1687, 1985. [ bib ] |
[1931] | V. Volterra. Theory of functionals and of integral and integro-differential equations. Dover Publications Inc., 1959. [ bib ] |
[1932] | C. van Vreeswijk. Stability of the asynchronous state in networks of non-linear oscillators. Physical Review Letters, 84:5110-5113, 2000. [ bib ] |
[1933] | C. van Vreeswijk. Partially synchronized states in networks of pulse-coupled neurons. Physical Review E, 54:5522-5537, 1996. [ bib ] |
[1934] | C. van Vreeswijk and L. F. Abbott. Self-sustained firing in populations of integrate-and-fire neurons. SIAM J. Appl. Math., 53(1):253-264, 1993. [ bib ] |
[1935] | C. van Vreeswijk, L. F. Abbott, and G. B. Ermentrout. Inhibition not excitation synchronizes neural firing. Journal of Computational Neuroscience, 1:303-313, 1994. [ bib ] |
[1936] | C. van Vreeswijk and D. Hansel. Patterns of synchrony in neural networks with spike adaptation. Neural Computation, 13:959-992, 2001. [ bib ] |
[1937] | C. van Vreeswijk and H. Sompolinsky. Irregular firing in cortical circuits with inhibition/excitation balance. In J. Bower, editor, Computational Neuroscience: Trends in Reserach, 1997, pages 209-213. Plenum Press, New York, 1997. [ bib ] |
[1938] | C. van Vreeswijk and H. Sompolinsky. Chaotic balanced state in a model of cortical circuits. Neural Computation, 10:1321-1371, 1998. [ bib ] |
[1939] | C. van Vreeswijk and H. Sompolinsky. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science, 274:1724-1726, 1996. [ bib ] |
[1940] | Y. W.Freeman. Model of biological pattern recognition with spatially chaotic dynamics. Neural Networks, 3:153-170, 1990. [ bib ] |
[1941] | H. Wagner and B. Frost. Disparity-sensitive cells in the owl have a characteristic disparity. Nature, 364(6440):796-798, 1993. [ bib ] |
[1942] | G. Wallis and R. Baddeley. Optimal, unsupervised learning in invariant object recognition. Neural Computation, 9:883-894, 1997. [ bib ] |
[1943] | G. Wallis and H. Bülthoff. Effects of temporal association on recognition memory. Proceedings of the National Academy of Sciences, page 71028598, 2001. [ bib ] |
[1944] | G. Wallis and E. T. Rolls. Invariant face and object recognition in the visual system. Progress in Neurobiology, 51(2):167-194, Feb. 1997. [ bib ] |
[1945] | H. Wan, D. S. Touretzky, and A. Redish. Towards a computational theory of rat navigation. In Proceedings of the 1993 Connectionist Models Summer School, pages 11-19. Lawrence Earlbaum Associates, 1994. [ bib ] |
[1946] | D. Wang. Emergent synchrony in locally coupled neural oscillators. IEEE Transactions on Neural Networks, 6:941-948, 1995. [ bib ] |
[1947] | D. Wang, J. Buhmann, and C. von der Malsburg. Pattern segmentation in associative memory. Neural Computation, 2:94-106, 1990. [ bib ] |
[1948] | H.-X. Wang, R. Gerkin, D. Nauen, and G.-Q. Wang. Coactivation and timing-dependent integration of synaptic potentiation and depression. Nature Neuroscience, 8:187-193, 2005. [ bib ] |
[1949] | X.-J. Wang. Probabilistic decision making by slow reverrberation in cortical circuits. Neuron, 36:955-968, 2002. [ bib ] |
[1950] | Y. Wang, A. Gupta, M. Toledo-Rodriguez, C. Wu, and H. Markram. Anatomical, physiological, molecular and circuit properties of nest basket cells in the developing somatosensory cortex. Cerebral Cortex, 12:395-410, 2002. [ bib ] |
[1951] | D. Warland, A. Huberman, and L. Chalupa. Dynamics of Spontaneous Activity in the Fetal Macaque Retina during Development of Retinogeniculate Pathways. Journal of Neuroscience, 26(19):5190, 2006. [ bib ] |
[1952] | C. Watkings. Learning from delayed rewards. PhD-thesis, Cambridge University, Cambridge, 1989. [ bib ] |
[1953] | C. Watkins and P. Dayan. Q-learning. Machine Learning, 8:279-292, 1992. [ bib ] |
[1954] | H. Wechsler. Neural networks for perception, vol. 1 + 2. Academic Press, Boston, 199x. [ bib ] |
[1955] | U. Wehmeier, D. Dong, C. Koch, and D. van Essen. Modeling the mammalian visual system. In Methods in Neuronal Modeling, pages 335-359. MIT Press, Cambridge, 1989. [ bib ] |
[1956] | R. Wehner, B. Michel, and P. Antonsen. Visual navigation in insects: Coupling of egocentric and geocentric information. Journal of Experimental Biology, 199:129-140, 1996. [ bib ] |
[1957] | M. Wehr and A. Zador. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature, 426:442-446, 2003. [ bib ] |
[1958] | T. Weiss. A model of the peripheral auditory system. Kybernetik, 3:153-175, 1966. [ bib ] |
[1959] | G. Westheimer. Illusory figures and real neurons. Nature, 371:745-746, 1994. [ bib ] |
[1960] | J. A. White, J. T. Rubinstein, and A. R. Kay. Channle noise in neurons. Trends in Neurosciences, 23:131-137, 2000. [ bib ] |
[1961] | O. L. White, D. D. Lee, and H. Sompolinsky. Short-term memory in orthogonal neural networks. Phys. Rev. Lett, 92(148102), 2004. [ bib ] |
[1962] | J. Wickens. Basal ganglia: structure and computations. Network: Computation in Neural Systems, 8:R77-R109, 1997. [ bib ] |
[1963] | J. Wickens. Stiatal dopamine in motor activiation and reward-mediated learning: steps towards a unifying model. J. Neural Transmism., 80:9-31, 1990. [ bib ] |
[1964] | J. Wickens and R. Kotter. Cellular models of reinforcement. In J. Houk, J. Davis, and D. G. Beiser, editors, Models of information processing in basal ganglia, pages 187-214. MIT-Press, Cambridge, 1995. [ bib ] |
[1965] | B. Widrow, N. Gupta, and S. Maitra. Punish/reward: learning with a critic in adaptive threshold systems. IEEE transactions on systems, man, cybernetics, 3:455-465, 1973. [ bib ] |
[1966] | B. Widrow and M. E. Hoff. Adaptive switching circuits. In 1960 IRE WESCON Convention Record, pages 96-104, New York: IRE, 1960. [ bib ] |
[1967] | M. Wiener and B. Richmond. Decoding spike trains instant by instant using order statistics and the mixture-of-poissons model. Neuroscience, 23:2394-2406, 2003. [ bib ] |
[1968] | S. I. Wiener, C. A. Paul, and H. Eichenbaum. Spatial and behavioral correlates of hippocampal neuronal activity. Journal of Neuroscience, 9(8):2736-2763, 1989. [ bib ] |
[1969] | C. J. Wierenga, K. Ibata, and G. G. Turrigiano. Postsynaptic expression of homeostatic plasticity at neocortical synapses. J. Neurosci., 25(11):2895-2905, Mar. 2005. [ bib | http ] |
[1970] | M. Wiering and J. Schmidhuber. HQ-learning. Adaptive Behavior, 6(2):219-246, 1998. [ bib ] |
[1971] | T. N. Wiesel. Postnatal development of the visual cortex and the influence of environment. Nature, 299(5884):583-591, Oct 1982. [ bib ] |
[1972] | K. Wiesenfeld and F. Jaramillo. Minireview of stochastic resonance. Chaos, 8:539-548, 1998. [ bib ] |
[1973] | K. Wiesenfeld and F. Moss. Stochastic resonance and the benefits of noise: from ice ages to crayfish and squids. Nature, 373:33-36, 1995. [ bib ] |
[1974] | K. Wiesenfeld, D. Pierson, E. Pantazelou, and F. Moss. Stochastic resonance on a circle. Phys. Rev. Lett., 72:2125-2129, 1994. [ bib ] |
[1975] | R. Williams. Simple statistical gradient-following methods for connectionist reinforcement learning. Machine Learning, 8:229-256, 1992. [ bib ] |
[1976] | R. Williams. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning. Reinforcement Learning, 8:229-256, 1992. [ bib ] |
[1977] | T. J. Wills, C. Lever, F. Cacucci, N. Burgess, and J. O'Keefe. Attractor dynamics in the hippocampal representation of the local environment. Science, 308:873-876, 2005. [ bib ] |
[1978] | D. J. Willshaw, O. P. Bunemann, and H. C. Longuet-Higgins. Non-holographic associative memory. Nature, 222:960-962, 1969. [ bib ] |
[1979] | D. J. Willshaw and C. von der Malsburg. How patterned neuronal connections can be set up by self-organization. Proc. R. Soc. (London) Ser. B, 194:431-445, 1976. [ bib ] |
[1980] | C. J. Wilson. The basal ganglia. In G. Shepherd, editor, The synaptic organization of the brain, pages 279-316. Oxford University Press, Oxford, 1990. [ bib ] |
[1981] | H. R. Wilson and J. D. Cowan. A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik, 13:55-80, 1973. [ bib ] |
[1982] | H. R. Wilson and J. D. Cowan. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J., 12:1-24, 1972. [ bib ] |
[1983] | M. A. Wilson, U. S. Bhalla, J. D. Uhley, and J. M. Bower. Genesis: A system for simulating neural networks. In D. Touretzky, editor, Advances in Neural Information Processing Systems, pages 485-492, San Mateo CA, 1989. Morgan Kaufmann Publishers. [ bib ] |
[1984] | M. A. Wilson and J. M. Bower. A computer simulation of oscillatory behavior in primary visual cortex. Neural Computation, 3:498-509, 1991. [ bib ] |
[1985] | M. A. Wilson and B. L. McNaughton. Dynamics of the hippocampal ensemble code for space. Science, 261:1055-1058, 1993. [ bib ] |
[1986] | S. Wimbauer, W. Gerstner, and J. van Hemmen. A developmental model of spatio-temporal receptive field properties for simple cells in the visual cortex. In xx, editor, ICANN'95, volume xx, page (preprint). Springer, 1995. [ bib ] |
[1987] | S. Wimbauer, W. Gerstner, and J. van Hemmen. Analysis of a correlation-based model for the development of orientation-selective receptive fields in the visual cortex. Network, 9:449-466, 1998. [ bib ] |
[1988] | S. Wimbauer, W. Gerstner, and J. L. van Hemmen. Motion detection in a linsker network. In M. Marinaro and P. G. Morasso, editors, ICANN'94, Proceedings of the International Conference on Artificial Neural Networks, Sorrento, Italy, 26-19 May 1994, pages 1001-1004. Springer-Verlag, London, 1994. [ bib ] |
[1989] | S. Wimbauer, W. Gerstner, and J. L. van Hemmen. Emergence of spatio-temporal receptive fields and its application to motion detection. Biol. Cybern., 72:81-92, 1994. [ bib ] |
[1990] | S. Wimbauer and J. L. van Hemmen. Hebbian unlearning. In S. I. Andersson, editor, Analysis of Dynamical and Cognitive Systems, volume 888 of Lecture Notes in Computer Science, pages 121-136. Springer, Berlin Heidelberg New York, 1995. [ bib ] |
[1991] | S. Wimbauer, N. Klemmer, and J. L. van Hemmen. Universality of unlearning. Neural Networks, 7, 1994. [ bib ] |
[1992] | S. Wimbauer, O. Wenisch, and J. van Hemmen. A linear hebbian model for the development of spatiotemporal receptive fields of simple cells. In W. G. et al., editor, Artificial Neural Networks, ICANN'97, Heidelberg, 1997. Springer. [ bib ] |
[1993] | S. Wimbauer, O. G. Wenisch, J. L. van Hemmen, and K. D. Miller. Development of spatiotemporal receptive fields of simple cells: Ii. simulation and analysis. Biol. Cybern., 77:463-477, 1997. [ bib ] |
[1994] | S. Wimbauer, O. G. Wenisch, K. D. Miller, and J. L. van Hemmen. Development of spatiotemporal receptive fields of simple cells: I. model formulation. Biol. Cybern., 77:453-461, 1997. [ bib ] |
[1995] | A. T. Winfree. The geometry of biological time. Springer-Verlag, Berlin Heidelberg New York, 1980. [ bib ] |
[1996] | L. Wiskott. How does our visual system achieve shift and size invariance? In J. L. van Hemmen and T. J. Sejnowski, editors, 23 Problems in Systems Neuroscience. Oxford University Press, 2005. [ bib ] |
[1997] | L. Wiskott. Learning invariance manifolds. In L. Niklasson, M. Bodén, and T. Ziemke, editors, Proceedings of the 8th International Conference on Artificial Neural Networks, ICANN'98, Skövde, Perspectives in Neural Computing, pages 555-560, London, Sept. 1998. Springer. [ bib ] |
[1998] | L. Wiskott. Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis. arXiv.org e-Print archive, http://arxiv.org/abs/cond-mat/0312317/, Dec. 2003. [ bib ] |
[1999] | L. Wiskott. Slow feature analysis: A theoretical analysis of optimal free responses. Neural Computation, 15(9):2147-2177, Sept. 2003. [ bib ] |
[2000] | L. Wiskott and T. Sejnowski. Slow feature analysis: unsupervised learning of invariances. Neural Computation, 14:715-770, 2002. [ bib ] |
[2001] | L. Wiskott and T. Sejnowski. Slow feature analysis: Unsupervised learning of invariances. Neural Computation, 14(4):715-770, 2002. [ bib ] |
[2002] | L. Wiskott and T. Sejnowski. Constraint optimization for neural map formation: a unifying framework for weight growth and normalization. Neural Computation, 10:671-716, 1998. [ bib ] |
[2003] | F. Wolf, H.-U. Bauer, and T. Geisel. Formation of field discontinuities and islands in visual cortical maps. Biol. Cybern., 70:525-531, 1994. [ bib ] |
[2004] | L. Wolf, S. Bileschi, and E. Meyers. Perception strategies in hierarchichal visual systems. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2006. [ bib ] |
[2005] | S. Wolfram. Mathematica. xx, xx. [ bib ] |
[2006] | R. Wong. Retinal waves and visual system development. Annual Review of Neuroscience, 22(1):29-47, 1999. [ bib ] |
[2007] | R. K. S. Wong, D. A. Prince, and A. F. Basbaum. Intradendritic recordings from hippocampal neurons. Proc. Natl. Acad. Sci. USA, 76:986-990, 1979. [ bib ] |
[2008] | E. Wood, P. Dudchenko, and H. Eichenbaum. The global record of memory in hippocampal neuronal activity. Nature, 397(6720):613-6, 1999. [ bib ] |
[2009] | S. H. Wu and J. B. Kelly. Physiological properties of neurons in the mouse superior olive: Membrane characteristics and postsynaptic responses studied in vitro. J. Neurophysiol., 65(2):230-246, 1991. [ bib ] |
[2010] | S. H. Wu and D. Oertel. Intracellular injection with horseradish peroxidase of physiologically characterized stellate and bushy cells in slices of mouse Anteroventral Cochlear Nucleus. J. Neurosci., 4(6):1577-1588, 1984. [ bib ] |
[2011] | X. Wu, R. A. Baxter, and W. B. Levy. Context codes and the effect of noisy learning on a simplified hippocampal ca3 model. Biol. Cybern., 74:159-165, 1996. [ bib ] |
[2012] | R. Wyss, P. König, and P. Verschure. A model of the ventral visual system based on temporal stability and local memory. PLoS Biology, 4(5):e120, 2006. [ bib ] |
[2013] | F. Wörgötter and C. Koch. A detailed model of the primary visual pathway in the cat: Comparison of afferent excitatory and intracortica l inhibitory connection schemes for orientation sele ctivity. Journal of Neuroscience, 11:1959-1979, 1991. [ bib ] |
[2014] | X. Xie and H. Seung. Learning in neural networks by reinforcement of irregular spiking. Physical Review E, 69(4):41909, 2004. [ bib ] |
[2015] | X. Xie and S. Seung. Spike-based learning rules and stabilization of persistent neural activity. In S. A. Solla, T. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 199-205. MIT-Press, Cambridge, 2000. [ bib ] |
[2016] | X. Xie and S. Seung. Learning in neural networks by reinforcement of irregular spiking. Phys. Rev. E, 69:41909, 2004. [ bib ] |
[2017] | N. Xu, C. Ye, M. Poo, and X. Zhang. Coincidence Detection of Synaptic Inputs Is Facilitated at the Distal Dendrites after Long-Term Potentiation Induction. Journal of Neuroscience, 26(11):3002-3009, 2006. [ bib ] |
[2018] | W. M. Yamada, C. Koch, and P. R. Adams. Multiple channels and calcium dynamics. In C. Koch and I. Segev, editors, Methods in neuronal modelin, Cambridge, 1989. MIT Press. [ bib ] |
[2019] | Y. Yamaguchi and H. Shimizu. Theory of selfsynchronization in the presence of native frequency distribution and external noises. Physica D, 11:212-226, 1984. [ bib ] |
[2020] | T. Yang and J. H. R. Maunsell. The effect of perceptual learning on neuronal responses in monkey visual area v4. The Journal of Neuroscience, 24(7):1617-1626, 2004. [ bib ] |
[2021] | Y. Yang, X. bin Wang, M. Frerking, and Q. Zhou. Spine expansion and stabilization associated with long-term potentiation. J Neurosci, 28(22):5740-5751, May 2008. [ bib | DOI | http ] |
[2022] | H. Yao and Y. Dan. Stimulus timing-dependent plasticity in cortical processing of orientation. Neuron, 32(2):315-323, Oct 2001. [ bib ] |
[2023] | H. Yao, Y. Shen, and Y. Dan. Intracortical mechanism of stimulus-timing-dependent plasticity in visual cortical orientation tuning. PNAS, 101(14):5081-5086, 2004. [ bib ] |
[2024] | H. Yao, L. Shi, F. Han, H. Gao, and Y. Dan. Rapid learning in cortical coding of visual scenes. Nature Neuroscience, 10:772-778, 2007. [ bib ] |
[2025] | L. C. Yeung, H. Shouval, B. Blais, and L. Cooper. Synaptic homeostasis and input selectivity follow from a model of calcium dependent plasticity. Proc. Nat. Acad. Sci. USA,, 101:14943-14948, 2004. [ bib ] |
[2026] | T. C. T. Yin and J. C. K. Chan. Interaural time sensitivity in medial superior olive of cat. Journal of Neurophysiology, 64(2):465-488, 1990. [ bib ] |
[2027] | D. Yoganarasimha and J. J. Knierim. Coupling between place cells and head direction cells during relative translations and rotations of distal land marks. Experimental Brain Research, 160:344-359, 2005. [ bib ] |
[2028] | D. Yogaranasimha, X. Yu, and J. J. Knierim. Head direction cell representations maintain internal coherence during conflicting proximal and distal cue rotations: Comparison with hippocampal place cells. Journal of Neuroscience, 26(2):622-631, 2006. [ bib ] |
[2029] | B. Young, G. Fox, and H. Eichenbaum. Correlates of hippocampal complex-spike cell activity in rats performing a nonspatial radial maze task. Journal of Neuroscience, 14:6553-6563, 1994. [ bib ] |
[2030] | J. Young, W. Waleszczyk, C. Wang, M. Calford, B. Dreher, and K. Obermayer. Cortical reorganization consistent with spike timing-but not correlation-dependent plasticity. Nature Neuroscience, 10:887-895, 2007. [ bib ] |
[2031] | A. L. Yuille, D. M. Kammen, and D. S. Cohen. Quadrature and the development of orientation selective cortical cells by Hebb rules. Biol. Cybern., 61:183-194, 1989. [ bib ] |
[2032] | R. Yuste and T. Bonhoeffer. Genesis of dendritic spines: insights from ultrastructural and imaging studies. Nat Rev Neurosci, 5(1):24-34, 2004. [ bib ] |
[2033] | A. Zador, C. Koch, and T. H. Brown. Biophysical model of a hebbian synapse. Proc. Natl. Acad. Sci., 87:6718-6722, 1990. [ bib ] |
[2034] | A. M. Zador. Impact of synaptic unreliability on the information transmitted by spiking neuron. J. Neurophysiology, 79:1219-1229, 1998. [ bib ] |
[2035] | S. Zeki. A vision of the brain. Blackwell Scientific Publications, Oxford, 1993. [ bib ] |
[2036] | S. Zeki and S. Shipp. The functional logic of cortical connections. Nature, 335:310-317, 1988. [ bib ] |
[2037] | A. Zell. Simulation Neuronaler Netze. Addison-Wesley, 1994. [ bib ] |
[2038] | Zhang and Sejnowski. Neuronal tuning: to sharpen or broaden? Neural Computation, 11:75-84, 1999. [ bib ] |
[2039] | L. Zhang, H. Tao, C. Holt, W.A.Harris, and M.-M. Poo. A critical window for cooperation and competition among developing retinotectal synapses. Nature, 395:37-44, 1998. [ bib ] |
[2040] | L. I. Zhang, H. W. Tao, C. E. Holt, W. A. Harris, and M. Poo. A critical window for cooperation and competition among developing retinotectal synapses. Nature, 395(6697):37-44, Sept. 1998. [ bib ] |
[2041] | X. L. Zhang, Z. Zhou, J. Winterer, W. Müller, and P. K. Stanton. NMDA-dependent, but not group-I metabotropic glutamate receptor-dependent, long-term depression at Schaffer collateral-CA1 synapses is associated with long-term reduction of release from the rapidly recycling presynaptic vesicle pool. Journal of Neuroscience, 26(40):10270-10280, 2006. [ bib ] |
[2042] | V. Zhigulin, M. Rabinovich, R. Huerta, and H. Abarbanel. Robustness and enhancement of neural synchronization by activity-dependent coupling. Physical Review E, 67:21901, 2003. [ bib ] |
[2043] | Q. Zhou, H. Tao, and M. Poo. Reversal and stabilization of synaptic modifications in the developping visual system. Science, 300:1953-1957, 2003. [ bib ] |
[2044] | A. Ziehe and K. Müller. TDSEP-an efficient algorithm for blind separation using time structure. Proc. Int. Conf. on Artificial Neural Networks (ICANN '98), pages 675-680, 1998. [ bib ] |
[2045] | L. Zinyuk, S. Kubik, Y. Kaminsky, A. A. Fenton, and J. Bures. Understanding hippocampal activity by using purposeful behavior: Place navigation induces place cell discharge in both task-relevant and task-irrelevant spatial reference frames. Proceedings of the National Academy of Sciences, 7(97):3771-3776, 2000. [ bib ] |
[2046] | D. Zipser. A computational model of hippocampal place fields. Behavioral Neuroscience, 99(5):1006-1018, 1985. [ bib ] |
[2047] | D. Zoccolan, M. Kouh, T. Poggio, and J. DiCarlo. Trade-off between object selectivity and tolerance in monkey inferotemporal cortex. Journal of Neuroscience, 27(45):12292, 2007. [ bib ] |
[2048] | E. Zohary, S. Celebrini, K. H. Britten, and W. T. Newsome. Neuronal plasticity that underlies improvement in perceptual performance. Science, 263:1289-1292, 1994. [ bib ] |
[2049] | E. Zohary, M. N. Shadlen, and W. T. Newsome. Correlated neuronal discharge rate and its implications for psychophysical performance. Nature, 370:140-143, 1994. [ bib ] |
[2050] | M. Zugaro, A. Arleo, A. Berthoz, and S. I. Wiener. Rapid spatial reorientation and head direction cells. Journal of Neuroscience, 23(8):3478-3482, 2003. [ bib ] |
[2051] | P. Andersen, R. Morris, D. Amaral, T. Bliss, and J. O'Keefe, editors. The hippocampus book. Oxford university press, 2007. [ bib ] |
[2052] | J. A. Anderson and E. Rosenfeld, editors. Neurocomputing: Directions of research. MIT-Press, Cambridge Mass., 1990. [ bib ] |
[2053] | J. A. Anderson and E. Rosenfeld, editors. Neurocomputing: Foundations of research. MIT-Press, Cambridge Mass., 1988. [ bib ] |
[2054] | G. Chen, editor. Controlling Chaos and Bifurcations in Engineering Systems. CRC Press, Boca Raton, Fl. USA, 1999. [ bib ] |
[2055] | E. Domany, J. L. van Hemmen, and K. Schulten, editors. Models of neural networks II. Springer-Verlag, New York, 1995. [ bib ] |
[2056] | E. Domany, J. L. van Hemmen, and K. Schulten, editors. Models of neural networks III. Springer-Verlag, New York, 1995. [ bib ] |
[2057] | E. Domany, J. L. van Hemmen, and K. Schulten, editors. Models of neural networks. Springer-Verlag, Berlin Heidelberg New York, 1991. [ bib ] |
[2058] | M. Fahle and T. Poggio, editors. Perceptual learning. The MIT Press, 2002. [ bib ] |
[2059] | N. A. Macmillan and C. D. Creelman, editors. Detection theory: a user's guide. Routledge, 2004. [ bib ] |
[2060] | T. McKenna, J. Davis, and S. F. Zornetzer, editors. Single neuron computation, Neural nets. Foundations to applications, London, 1992. Academic Press. [ bib ] |
[2061] | G. L. Shaw and G. Palm, editors. Brain theory, volume 2 of Advances in Neuroscience. World Scientific, Singapore, New Jersey, Hong Kong, 1990. [ bib ] |
[2062] | G. L. Shaw and G. Palm, editors. Brain theory, volume 1 of Advances in Neuroscience. World Scientific, Singapore, New Jersey, Hong Kong, 1988. [ bib ] |
[2063] | D. S. Touretzky, editor. Advances in Neural Information Processing Systems, volume 1. Morgan Kaufmann Publishers, San Mateo, 1989. [ bib ] |
[2064] | S. I. Wiener and S. Taube, editors. Head direction cells and the neural mechanisms of spatial orientation. MIT Press, 2005. [ bib ] |
[2065] | H. D. Landahl. Theory of the distribution of response times in nerve fibers. Bulletin of Mathematical Biology, 1941. [ bib ] |
This file was generated by bibtex2html 1.91.