Dynamics and plasticity of stimulus-selective persistent activity in cortical network models

Abstract

Persistent neuronal activity is widespread in many areas of the cerebral cortex of monkeys performing cognitive tasks with a working memory component. Modeling studies have helped understanding of the conditions under which persistent activity can be sustained in cortical circuits. Here, we first review several basic models of persistent activity, including bistable models with excitation only and multistable models for working memory of a discrete set of pictures or objects with structured excitation and global inhibition. In many experiments, persistent activity has been shown to be subject to changes due to associative learning. In cortical network models, Hebbian learning shapes the synaptic structure and, in turn, the properties of persistent activity when pictures are associated together in the course of a task. It is shown how the theoretical models can reproduce basic experimental findings of neurophysiological recordings from inferior temporal and perirhinal cortices obtained using the following experimental protocols: (i) the pair-associate task; (ii) the pair-associate task with color switch; and (iii) the delay match to sample task with a fixed sequence of samples.