Abstract

If hardware implementations of neural networks are to be successful new models are required that are simpler to implement. As a first step in this direction a new associative memory model is proposed that is specifically designed for optical implementations. This memory is a modification of Kanerva’s sparse distributed memory,¹ which eliminates the negative connection elements by employing sparsely coded representations on all layers. The advantages of sparse coding include improved storage capacity, small weight range, and a simplified learning rule. The implementation of the memory is illustrated by suggesting several optical architectures. The advantages and disadvantages of the model in each case are discussed.

© 1992 Optical Society of America