Statistical Coding and Short-Term Synaptic Plasticity: A Scheme for Knowledge Representation in the Brain

It is suggested that the scheme of a synaptic pattern may be more adapted than the classical cell-assembly notion for explaining cognitive abilities such as generalization and categorization, which pertain to the notion of invariance.

Abstract

This work is a theoretical investigation of some consequences of the hypothesis that transmission efficacies of synapses in the Central Nervous System (CNS) undergo modification on a short time-scale. Short-term synaptic plasticity appears to be an almost necessary condition for the existence of activity states in the CNS which are stable for about 1 sec., the time-scale of psychological processes. It gives rise to joint "activity-and-connectivity" dynamics. This dynamics selects and stabilizes particular high-order statistical relationships in the timing of neuronal firing; at the same time, it selects and stabilizes particular connectivity patterns. In analogy to statistical mechanics, these stable states, the attractors of the dynamics, can be viewed as the minima of a hamiltonian, or cost function. It is found that these low-cost states, termed synaptic patterns, are topologically organized. Two important properties of synaptic patterns are demonstrated: (i) synaptic patterns can be "memorized" and later "retrieved", and (ii) synaptic patterns have a tendency to assemble into compound patterns according to simple topological rules. A model of position-invariant and size-invariant pattern recognition based on these two properties is briefly described. It is suggested that the scheme of a synaptic pattern may be more adapted than the classical cell-assembly notion for explaining cognitive abilities such as generalization and categorization, which pertain to the notion of invariance.