Dynamics of Recall and Association

Abstract

Introduction The concept of associative memory in neural networks is already discussed elsewhere (see STATISTICAL MECHANICS OF NEURAL NETWORKS). Associative memory networks are usually recurrent, which implies that one cannot simply write down the values of successive neuron states (as with layered networks). The latter must be solved from coupled dynamic equations. Dynamical studies shed light on the pattern recall process and its relation with the choice of the initial state, the properties of the stored patterns, the noise level and the network architecture. In addition, for non-symmetric networks (where the equilibrium statistics are not known) dynamical techniques are in fact the only tools available. Since our interest is usually in large networks and in global recall processes, the common strategy of the theorist is to move away from the microscopic neuronal equations and derive dynamical laws at a macroscopic level of q