SELECTIVE STABILIZATION OF NEURAL REPRESENTATIONS BY RESONANCE BETWEEN SPONTANEOUS PREREPRESENTATIONS AND PERCEPTS EVOKED BY INTERACTION OF SENSORY ORGANS WITH THE OUTSIDE WORLD

Abstract

Changeux et al. have recently discussed a model of learning by selection in which the storage of patterns of activity, prerepresenataions, within a network of neurons, results from the coincidence or resonance between a spontaneous activity of the neurons and external signals applied to the network, for instance sensory stimuli. A mathematical formulation of the model is presented, based on that proposed by Little and Shaw for the statistical analysis of neuronal activity within a network and on a rule for modulation of synaptic efficacies derived from that proposed by Hebb. The effect of an external signal .rho. on the probability P(.beta.) of occurrence of a given prerepresentation .beta. under stationary conditions were analytically derived. Taking into account that the system spontaneously fluctuates between various prerepresentations, it is shown that P(.beta.) is increased by the external signal .rho. when (1) .beta. is close to .rho., namely the external signal significantly modifies the probabilities of those prerepresentations that resemble .rho.; when the external signal .rho. sets the neurons precisely in the state that they would have more probably reached at the moment when the external signal was applied. There should be a resonance between .rho. and the prerepresentation of the network when .rho. is applied.