On the behavior of some associative neural networks.
Since Hopfield published his work on an associative memory model, a large number of works have studied the model from several angles and showed in particular its weaknesses, and presented ways to overcome them. Most of the proposed solutions seem to us however not biologically plausible. In this paper we present a simple statistical analysis of two networks similar to the Hopfield net, and show that the usage of positive feedback enhances the net recognizing capability without jeopardizing the stability. We also describe a layered parallel network composed of modules, each module being a modified Hopfield net. We finally present computer simulation results to support our analytical findings. The most important principles of this network are supported by data from the world of neurobiology.
Braham
R
R
Computer Architecture Laboratory, Georgia Tech-CRB 396, Atlanta 30332-0540.
Hamblen
J O
JO
D001185:Artificial Intelligence; D001244:Association / Q000502:physiology*; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D008959:Models, Neurological