Abstract
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. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Pazienza, G. E., Gomez-Ramirez, E., & Vilasís-Cardona, X. (2007). Polynomial cellular neural networks for implementing the game of life. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4668 LNCS, pp. 914–923). Springer Verlag. https://doi.org/10.1007/978-3-540-74690-4_93
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