Cellular neural networks as a model of associative memories

Abstract

Concerns the design of cellular neural networks intended to function as associative memories. The authors consider a discrete-time version of cellular neural nets featuring simple linear thresholding neurons and the synchronous state-updating rule. The Hebbian rule is adopted as the memory design rule. Important issues, such as the memory capacity and the size of the attracting basin, are discussed. The validity of the method is illustrated by a simple example.<>