Penalty function approach to recurrent neural network dynamics

Abstract

The Hopfield dynamics for recurrent neural networks minimizes a certain quadratic form on the unit hypercube. I show here how the dynamical system can be derived from a standard method of optimization theory. I use the method to give a precise meaning to nonsymmetric interactions, and I discuss the possibility of introducing other types of dynamics.