Abstract

To increase the storage capacity of the Hopfield-type neural network, the spurious states need to be reduced or eliminated. Recently, a new type of attractor called a terminal attractor, which represents singular solutions of a neural dynamic system, has been introduced¹ for the elimination of spurious states in associative memory. These terminal attractors are characterized by having finite relaxation times, no spurious states, and infinite stability. They provide a means of real time high density associative memory applications and potential solutions of learning and global optimization problems. The original terminal attractor model assumed continuous variable representation of neural states in the neural dynamic equations. Also, sigmoidal thresholding functions are assumed. These assumptions present difficulties for optical implementations. To implement the terminal attractor model, we have made modifications to incorporate this model to the associative memory system.

© 1989 Optical Society of America