Learning of correlated patterns in spin-glass networks by local learning rules

Abstract

Two simple storing prescriptions are presented for neural network models of N two-state neurons. These rules are local and allow the embedding of correlated patterns without errors in a network of spin-glass type. Starting from an arbitrary configuration of synaptic bonds, up to N patterns can be stored by successive modification of the synaptic efficacies. Proofs for the convergence are given. Extensions of these rules are possible.