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Cellular neural networks: theory

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2 Author(s)
Chua, L.O. ; Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA ; Yang, L.

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

Published in:

Circuits and Systems, IEEE Transactions on  (Volume:35 ,  Issue: 10 )

Date of Publication:

Oct 1988

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