Probing the attractors in neural networks

Abstract

I propose tools to probe the nature of the retrieval attractors in neural networks. These include the activity distribution, the evolutions of the state damage, activity damage and temporal correlation damage. They enable us to demonstrate that the retrieval attractors in dillute asymmetric neutral networks are not clouds of attractors, but consists of a single chaotic attractor for each stored pattern. Furthermore, they facilitate the devise of effective freezing procedures, which significantly improve the quality of retrieval in dilute asymmetric neural networks.