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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Polynomial Cellular Neural Networks for Implementing the Game of Life
verfasst von : Giovanni Egidio Pazienza, Eduardo Gomez-Ramirez, Xavier Vilasís-Cardona
Erschienen in: Artificial Neural Networks – ICANN 2007
Verlag: Springer Berlin Heidelberg
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One-layer space-invariant Cellular Neural Networks (CNNs) are widely appreciated for their simplicity and versatility; however, such structures are not able to solve non-linearly separable problems. In this paper we show that a polynomial CNN - that has with a direct VLSI implementation - is capable of dealing with the ‘Game of Life’, a Cellular Automaton with the same computational complexity as a Turing machine. Furthermore, we describe a simple design algorithm that allows to convert the rules of a Cellular Automaton into the weights of a polynomial CNN.