Cellular neural networks

Abstract

A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear circuits which process signals in real time. Like cellular automata, they are made of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly only through their nearest neighbors. Some applications in such areas as image processing are demonstrated, albeit with only a crude circuit. In particular, examples of cellular neural networks which can be designed to recognize the key features of Chinese characters are presented. Some theorems concerning the dynamic range and the steady states of cellular neural networks are proved. In view of the near-neighbor interactive property of cellular neural networks, they are much more amenable to VLSI implementation than general neural networks. >