Modeling active memory: Experiment, theory and simulation

Neuro-physiological experiments on cognitively performing primates are described to argue that strong evidence exists for localized, non-ergodic (stimulus specific) attractor dynamics in the cortex. The specific phenomena are delay activity distributions-enhanced spike-rate distributions resulting from training, which we associate with working memory. The anatomy of the relevant cortex region and the physiological characteristics of the participating elements (neural cells) are reviewed to provide a substrate for modeling the observed phenomena. Modeling is based on the properties of the integrate-and-fire neural element in presence of an input current of Gaussian distribution. Theory of stochastic processes provides an expression for the spike emission rate as a function of the mean and the variance of the current distribution. Mean-field theory is then based on the assumption that spike emission processes in different neurons in the network are independent, and hence the input current to a neuron is Gau...

[1]  Daniel J. Amit,et al.  Paradigmatic Working Memory (Attractor) Cell in IT Cortex , 1997, Neural Computation.

[2]  Daniel J. Amit,et al.  Quantitative Study of Attractor Neural Network Retrieving at Low Spike Rates: I , 1991 .

[3]  I. I. Gikhman Theory of stochastic processes , 1974 .

[4]  Henry C. Tuckwell,et al.  Introduction to theoretical neurobiology , 1988 .

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

[6]  Haim Sompolinsky,et al.  Chaotic Balanced State in a Model of Cortical Circuits , 1998, Neural Computation.

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

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

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

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

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

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

[13]  R. Desimone,et al.  Neural Mechanisms of Visual Working Memory in Prefrontal Cortex of the Macaque , 1996, The Journal of Neuroscience.