Introduction to Cellular Neural Networks 

A cellular neural network (CNN) is an artificial neural network which features a multi-dimensional array of neurons and local interconnections among the cells.
The original CNN paradigm was first proposed by Chua and Yang in 1988. The two most fundamental ingredients of the CNN paradigm are: the use of analog processing cells with continuous signal values, and local interaction within a finite radius. A CNN is a nonlinear analog circuit which processes signals in real time. It is made of a massive aggregate of regularly spaced cloned circuit, called cells, which communicate with each other directly only through their nearest neighbors.

Architecture of Cellular Neural Networks
The basic circuit unit of a CNN is called a cell. It contains linear and nonlinear circuit elements, which typically are linear capacitors, linear resistors, linear and nonlinear controlled sources, and independent sources.

Global behavior of Cellular Neural Networks
Assuming that the interconnections of a single cell do not depend on its position, the global behavior of a CNN is described by its Template set containing the A-Template, the B-Template, the Bias I, and the initial state.

Application Potential
A CNN is very well suited for high speed parallel signal processing. Its local interconnection feature makes it tailor-made for VLSI implementation.
Potential applications for Cellular Neural Networks are signal processing, pattern recognition and image processing.

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