Cellular neural networks: theory
Chua, L.O.; Yang, L.
Circuits and Systems, IEEE Transactions on
Volume 35, Issue 10, Oct 1988 Page(s):1257 - 1272
Digital Object Identifier   10.1109/31.7600
Summary:A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear analog circuits that process signals in real time. Like cellular automata, they consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly only through their nearest neighbors. Each cell is made of a linear capacitor, a nonlinear voltage-controlled current source, and a few resistive linear circuit elements. Cellular neural networks share the best features of both worlds: their continuous-time feature allows real-time signal processing, and their local interconnection feature makes them particularly adapted for VLSI implementation. Cellular neural networks are uniquely suited for high-speed parallel signal processing

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