MODEL OF GLOBAL SPONTANEOUS ACTIVITY AND LOCAL STRUCTURED ACTIVITY DURING DELAY PERIODS IN THE CEREBRAL-CORTEX

We investigate self-sustaining stable states (attractors) in networks of integrate-and-fire neurons. First, we study the stability of sponta neous activity in an unstructured network. It is shown that the stocha stic background activity, of 1-5 spikes/s, is unstable if all neurons are excitatory. On the other hand, spontaneous activity becomes self-s tabilizing in presence of local inhibition, given reasonable values of the parameters of the network. Second, in a network sustaining physio logical spontaneous rates, we study the effect of learning in a local module, expressed in synaptic modifications in specific populations of synapses. We find that if the average synaptic potentiation (LTP) is too low, no stimulus specific activity manifests itself in the delay p eriod. Instead, following the presentation and removal of any stimulus there is, in the local module, a delay activity in which all neurons selective (responding visually) to any of the stimuli presented for le arning have rates which gradually increase with the amplitude of synap tic potentiation; When the average LTP increases beyond a critical val ue, specific local attractors (stable states) appear abruptly against the background of the global uniform spontaneous attractor. In this ca se the local module has two available types of collective delay activi ty: if the stimulus is unfamiliar, the activity is spontaneous; if it is similar to a learned stimulus, delay activity is selective. These n ew attractors reflect the synaptic structure developed during learning . In each of them a small population of neurons have elevated rates, w hich depend on the strength of LTP. The remaining neurons of the modul e have their activity at spontaneous rates. The predictions made in th is paper could be checked by single unit recordings in delayed reponse experiments.