Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning
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[1] T. Sejnowski,et al. Storing covariance with nonlinearly interacting neurons , 1977, Journal of mathematical biology.
[2] W. Levy,et al. Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus , 1983, Neuroscience.
[3] B. Kosco. Differential Hebbian learning , 1987 .
[4] Idan Segev,et al. Methods in Neuronal Modeling , 1988 .
[5] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[6] Michael I. Jordan. Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .
[7] D. Whitteridge,et al. An intracellular analysis of the visual responses of neurones in cat visual cortex. , 1991, The Journal of physiology.
[8] T. Bliss,et al. A synaptic model of memory: long-term potentiation in the hippocampus , 1993, Nature.
[9] T. Sejnowski,et al. The predictive brain: temporal coincidence and temporal order in synaptic learning mechanisms. , 1994, Learning & memory.
[10] B. Sakmann,et al. Active propagation of somatic action potentials into neocortical pyramidal cell dendrites , 1994, Nature.
[11] H. Sompolinsky,et al. Theory of orientation tuning in visual cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[12] C. Koch,et al. Modeling direction selectivity of simple cells in striate visual cortex within the framework of the canonical microcircuit , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[13] J G Daugman,et al. Demodulation, predictive coding, and spatial vision. , 1995, Journal of the Optical Society of America. A, Optics, image science, and vision.
[14] S. Nelson,et al. An emergent model of orientation selectivity in cat visual cortical simple cells , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[15] C. Koch,et al. Recurrent excitation in neocortical circuits , 1995, Science.
[16] Wulfram Gerstner,et al. A neuronal learning rule for sub-millisecond temporal coding , 1996, Nature.
[17] Geoffrey E. Hinton,et al. Varieties of Helmholtz Machine , 1996, Neural Networks.
[18] T. Sejnowski,et al. The Monetary Transmission Mechanism in the United Kingdom: Pass-Through and Policy Rules. manuscript , 1996 .
[19] K. I. Blum,et al. Functional significance of long-term potentiation for sequence learning and prediction. , 1996, Cerebral cortex.
[20] H. Markram,et al. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.
[21] D. Johnston,et al. A Synaptically Controlled, Associative Signal for Hebbian Plasticity in Hippocampal Neurons , 1997, Science.
[22] A. Destexhe. Kinetic Models of Synaptic Transmission , 1997 .
[23] Rajesh P. N. Rao,et al. Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex , 1997, Neural Computation.
[24] B. McNaughton,et al. Experience-dependent, asymmetric expansion of hippocampal place fields. , 1997, Proceedings of the National Academy of Sciences of the United States of America.
[25] D. Clapham,et al. NMDA receptors amplify calcium influx into dendritic spines during associative pre- and postsynaptic activation , 1998, Nature Neuroscience.
[26] Christof Koch,et al. Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series) , 1998 .
[27] H. Barlow. Cerebral predictions. , 1998, Perception.
[28] B. Sakmann,et al. Calcium dynamics in single spines during coincident pre- and postsynaptic activity depend on relative timing of back-propagating action potentials and subthreshold excitatory postsynaptic potentials. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[29] Sen Song,et al. Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response Variability , 1998, NIPS.
[30] Li I. Zhang,et al. A critical window for cooperation and competition among developing retinotectal synapses , 1998, Nature.
[31] G. Bi,et al. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.
[32] D. Linden. The Return of the Spike Postsynaptic Action Potentials and the Induction of LTP and LTD , 1999, Neuron.
[33] N. Emptage. Calcium on the Up Supralinear Calcium Signaling in Central Neurons , 1999, Neuron.
[34] Rajesh P. N. Rao,et al. An optimal estimation approach to visual perception and learning , 1999, Vision Research.
[35] T. Sejnowski,et al. The Book of Hebb , 1999, Neuron.
[36] A. Borst. Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.
[37] Vivien A. Casagrande,et al. Biophysics of Computation: Information Processing in Single Neurons , 1999 .
[38] Frances S. Chance,et al. Complex cells as cortically amplified simple cells , 1999, Nature Neuroscience.
[39] Wayne Carl Westerman,et al. Antidromic Spikes Drive Hebbian Learning in an Artificial Dendritic Tree , 1999 .
[40] Rajesh P. N. Rao,et al. Predictive Sequence Learning in Recurrent Neocortical Circuits , 1999, NIPS.
[41] Kenji Doya,et al. Reinforcement Learning in Continuous Time and Space , 2000, Neural Computation.
[42] L. Abbott,et al. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.
[43] T. Sejnowski,et al. Natural patterns of activity and long-term synaptic plasticity , 2000, Current Opinion in Neurobiology.
[44] M. Wilson,et al. From hippocampus to V 1 : E ! ect of LTP on spatio-temporal dynamics of receptive " elds , 2000 .
[45] D. Feldman,et al. Timing-Based LTP and LTD at Vertical Inputs to Layer II/III Pyramidal Cells in Rat Barrel Cortex , 2000, Neuron.
[46] Guo-Qiang Bi,et al. Synaptic modification in neural circuits: a timely action. , 2002, BioEssays : news and reviews in molecular, cellular and developmental biology.
[47] Y. Dan,et al. Spike-timing-dependent synaptic modification induced by natural spike trains , 2002, Nature.
[48] Silvia Scarpetta,et al. Hebbian Imprinting and Retrieval in Oscillatory Neural Networks , 2001, Neural Computation.