ASSOCIATIVE MEMORY WITH NONMONOTONE DYNAMICS

The dynamics of autocorrelation associative memory is examined, and a novel neural dynamics which greatly enhances the ability of associative neural networks is presented. This dynamics is such that the output of some particular neurons is reversed (for a discrete model) or the output function is not sigmoid but nonmonotonic (for an analog model). It is also shown by numerical experiments that most of the problems of the conventional model are overcome by the improved dynamics. These results are important not only for practical purposes but also for understanding dynamical properties of associative neural networks.