Virtual template expansion in cellular neural networks using backpropagation through time

A design method is proposed which allows the implementation of a discrete-time cellular neural network (CNN) with a virtual neighbourhood radius r>1, using recursively r nearest-neighbour physical templates. The proposed approach, based on the idea of the unfolding of time and the backpropagation through the time learning algorithm, presents some advantages over existing methods which are illustrated by an example concerning a feature detector