Auto-associative memory produced by disinhibition in a sparsely connected network.

Abstract

Algorithms for auto-associative memory in sparsely connected networks are commonly brittle with respect to relationships between such parameters as training set size, connectivity, synaptic strength, threshold, or inhibitory feedback. This paper describes an algorithm for auto-associative memory based on depotentiation of inhibitory synapses (disinhibition) rather than potentiation of excitatory synapses. All parameter values are robust, largely independent of one another, and independent of network architecture over a large range of random and structured architectures. The algorithm does not require thresholds which depend on the number of active neurons or the number of synapses per neuron. Architectures to which the algorithm is applicable include randomized variants of projective networks in which all links between neurons in the same layer are of path length two (i.e., through some other layer). The resulting fanout produces prodigiously large networks with correspondingly large information storage capacities relative to the number of synapses per neuron.