Designing discrete-time cellular neural networks for the evaluation of local Boolean functions

General methods of designing a discrete-time cellular neural network implementing an arbitrary Boolean function defined on the r-neighborhood are described. This is achieved by operating the network with time-invariant templates as a cellular automaton that processes only binary inputs. These methods are suitable for solving local tasks. As an example, testing minimal distances is discussed.<
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