Retrospective and prospective persistent activity induced by Hebbian learning in a recurrent cortical network

Recordings from cells in the associative cortex of monkeys performing visual working memory tasks link persistent neuronal activity, long‐term memory and associative memory. In particular, delayed pair‐associate tasks have revealed neuronal correlates of long‐term memory of associations between stimuli. Here, a recurrent cortical network model with Hebbian plastic synapses is subjected to the pair‐associate protocol. In a first stage, learning leads to the appearance of delay activity, representing individual images ('retrospective' activity). As learning proceeds, the same learning mechanism uses retrospective delay activity together with choice stimulus activity to potentiate synapses connecting neural populations representing associated images. As a result, the neural population corresponding to the pair‐associate of the image presented is activated prior to its visual stimulation ('prospective' activity). The probability of appearance of prospective activity is governed by the strength of the inter‐population connections, which in turn depends on the frequency of pairings during training. The time course of the transitions from retrospective to prospective activity during the delay period is found to depend on the fraction of slow, N‐methyl‐d‐aspartate‐like receptors at excitatory synapses. For fast recurrent excitation, transitions are abrupt; slow recurrent excitation renders transitions gradual. Both scenarios lead to a gradual rise of delay activity when averaged over many trials, because of the stochastic nature of the transitions. The model reproduces most of the neuro‐physiological data obtained during such tasks, makes experimentally testable predictions and demonstrates how persistent activity (working memory) brings about the learning of long‐term associations.

[1]  G. E. Alexander,et al.  Neuron Activity Related to Short-Term Memory , 1971, Science.

[2]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[3]  A. Kendon,et al.  Organization of behavior in face-to-face interaction , 1975 .

[4]  Masataka Watanabe,et al.  Prefrontal and cingulate unit activity during timing behavior in the monkey , 1979, Brain Research.

[5]  J. Fuster,et al.  Inferotemporal neurons distinguish and retain behaviorally relevant features of visual stimuli. , 1981, Science.

[6]  J. Fuster,et al.  Cellular discharge in the dorsolateral prefrontal cortex of the monkey in cognitive tasks , 1982, Experimental Neurology.

[7]  D. McCormick,et al.  Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. , 1985, Journal of neurophysiology.

[8]  Joachim M. Buhmann,et al.  Noise-driven temporal association in neural networks , 1987 .

[9]  Y. Miyashita,et al.  Neuronal correlate of pictorial short-term memory in the primate temporal cortexYasushi Miyashita , 1988, Nature.

[10]  D J Amit,et al.  Neural networks counting chimes. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Y. Miyashita Neuronal correlate of visual associative long-term memory in the primate temporal cortex , 1988, Nature.

[12]  P. Goldman-Rakic,et al.  Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. , 1989, Journal of neurophysiology.

[13]  T. Sawaguchi,et al.  Depth distribution of neuronal activity related to a visual reaction time task in the monkey prefrontal cortex. , 1989, Journal of Neurophysiology.

[14]  Y. Miyashita,et al.  Neural organization for the long-term memory of paired associates , 1991, Nature.

[15]  K. Stratford,et al.  Synaptic transmission between individual pyramidal neurons of the rat visual cortex in vitro , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[16]  Bard Ermentrout,et al.  Complex dynamics in winner-take-all neural nets with slow inhibition , 1992, Neural Networks.

[17]  Daniel J. Amit,et al.  Conversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors , 1999, Neural Computation.

[18]  C. Gross,et al.  Neural ensemble coding in inferior temporal cortex. , 1994, Journal of neurophysiology.

[19]  J. Fuster Memory in the cerebral cortex , 1994 .

[20]  N Brunel,et al.  Correlations of cortical Hebbian reverberations: theory versus experiment , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[21]  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.

[22]  P. Goldman-Rakic,et al.  Modulation of memory fields by dopamine Dl receptors in prefrontal cortex , 1995, Nature.

[23]  Walter J. Freeman,et al.  The Hebbian paradigm reintegrated: Local reverberations as internal representations , 1995, Behavioral and Brain Sciences.

[24]  P. Goldman-Rakic Cellular basis of working memory , 1995, Neuron.

[25]  K. Nakamura,et al.  Mnemonic firing of neurons in the monkey temporal pole during a visual recognition memory task. , 1995, Journal of neurophysiology.

[26]  Y. Miyashita,et al.  Activity of primate inferotemporal neurons related to a sought target in pair-association task. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[27]  S. Jones,et al.  Principles of protein-protein interactions. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Nicolas Brunel,et al.  Hebbian Learning of Context in Recurrent Neural Networks , 1996, Neural Computation.

[29]  Y. Miyashita,et al.  Formation of mnemonic neuronal responses to visual paired associates in inferotemporal cortex is impaired by perirhinal and entorhinal lesions. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[30]  H S Seung,et al.  How the brain keeps the eyes still. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Bard Ermentrout,et al.  Type I Membranes, Phase Resetting Curves, and Synchrony , 1996, Neural Computation.

[32]  Nicolas Brunel,et al.  Dynamics of a recurrent network of spiking neurons before and following learning , 1997 .

[33]  H. Markram,et al.  Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. , 1997, The Journal of physiology.

[34]  D. Amit,et al.  Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. , 1997, Cerebral cortex.

[35]  R. Yuste,et al.  Input Summation by Cultured Pyramidal Neurons Is Linear and Position-Independent , 1998, The Journal of Neuroscience.

[36]  P. Goldman-Rakic,et al.  Matching patterns of activity in primate prefrontal area 8a and parietal area 7ip neurons during a spatial working memory task. , 1998, Journal of neurophysiology.

[37]  Daniel J. Amit,et al.  Simulation in neurobiology: theory or experiment? , 1998, Trends in Neurosciences.

[38]  E. Miller,et al.  Neural Activity in the Primate Prefrontal Cortex during Associative Learning , 1998, Neuron.

[39]  B. Ermentrout Neural networks as spatio-temporal pattern-forming systems , 1998 .

[40]  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.

[41]  J. Fuster,et al.  From perception to action: temporal integrative functions of prefrontal and parietal neurons. , 1999, Cerebral cortex.

[42]  R. Desimone,et al.  Responses of Macaque Perirhinal Neurons during and after Visual Stimulus Association Learning , 1999, The Journal of Neuroscience.

[43]  Nicolas Brunel,et al.  Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates , 1999, Neural Computation.

[44]  E. Miller,et al.  Prospective Coding for Objects in Primate Prefrontal Cortex , 1999, The Journal of Neuroscience.

[45]  T. Joh,et al.  Neuroprotection and Neuronal Differentiation Studies Using Substantia Nigra Dopaminergic Cells Derived from Transgenic Mouse Embryos , 1999, The Journal of Neuroscience.

[46]  Davide Badoni,et al.  Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation , 2000, Neural Computation.

[47]  P. Goldman-Rakic,et al.  Destruction and Creation of Spatial Tuning by Disinhibition: GABAA Blockade of Prefrontal Cortical Neurons Engaged by Working Memory , 2000, The Journal of Neuroscience.

[48]  James K. Kroger,et al.  Cross-modal and cross-temporal association in neurons of frontal cortex , 2000, Nature.

[49]  P. Goldman-Rakic,et al.  Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. , 2000, Cerebral cortex.

[50]  T. Sejnowski,et al.  Neurocomputational models of working memory , 2000, Nature Neuroscience.

[51]  N. Brunel Persistent activity and the single-cell frequency–current curve in a cortical network model , 2000, Network.

[52]  Volodya Yakovlev,et al.  A model of expectation effects in inferior temporal cortex , 2001, Neurocomputing.

[53]  Xiao-Jing Wang Synaptic reverberation underlying mnemonic persistent activity , 2001, Trends in Neurosciences.

[54]  Y. Miyashita,et al.  Backward spreading of memory-retrieval signal in the primate temporal cortex. , 2001, Science.

[55]  P. J. Sjöström,et al.  Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity , 2001, Neuron.

[56]  Carson C. Chow,et al.  Stationary Bumps in Networks of Spiking Neurons , 2001, Neural Computation.

[57]  A. Koulakov,et al.  Properties of synaptic transmission and the global stability of delayed activity states , 2001, Network.

[58]  L. Squire,et al.  Neuronal representations of stimulus associations develop in the temporal lobe during learning , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[59]  J. Fuster The Prefrontal Cortex—An Update Time Is of the Essence , 2001, Neuron.

[60]  D. Hansel,et al.  How Noise Contributes to Contrast Invariance of Orientation Tuning in Cat Visual Cortex , 2002, The Journal of Neuroscience.

[61]  Yasushi Miyashita,et al.  Forward Processing of Long-Term Associative Memory in Monkey Inferotemporal Cortex , 2003, The Journal of Neuroscience.

[62]  Daniel J. Amit,et al.  Spike-Driven Synaptic Dynamics Generating Working Memory States , 2003, Neural Computation.

[63]  D J Amit,et al.  Multiple-object working memory--a model for behavioral performance. , 2003, Cerebral cortex.

[64]  Nicolas Brunel,et al.  Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons , 2000, Journal of Computational Neuroscience.

[65]  Xiao-Jing Wang,et al.  A Model of Visuospatial Working Memory in Prefrontal Cortex: Recurrent Network and Cellular Bistability , 1998, Journal of Computational Neuroscience.