Static and dynamic attractors of autoassociative neural networks

Abstract

In this paper we study the problem of the occurrence of cycles in autoassociative neural networks. We call these cycles dynamic attractors, show when and why they occur and how they can be identified. Of particular interest is the pseudo-inverse network with reduced self-connection. We prove that it has dynamic attractors, which occur with a probability proportional to the number of prototypes and the degree of weight reduction. We show how to predict and avoid them.