Extension of cellular automata to neural computation: theory and applications
François Blayo, Marchal · 1989
A relationship between cellular automata and neuro-mimetic theory is presented. A systolic approach is proposed for evaluating a first-neighborhood cellular automaton. This method has been extended for a full-neighborhood automaton and applied to Hopfield-like neural networks. Three possible resulting solutions are discussed, and a new approach which allows for full extensibility is shown. Appropriate external storage of the parameters provides a flexible recognition system able to dynamically modify the synaptic weights. Such a function will be used later for the implementation of an autoadaptive system.>