A distributed optical associative memory model

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.