New rigorous results for the Hopfield's neural network model

Abstract

New asymptotic bounds for the learning capacity of Hopfield's model with random memorized patterns are given. The usual independence assumption is replaced by a different hypothesis on the joint distribution of spins. The proof of the main result uses classical tools of probabilistic analysis of algorithms.