New properties of 2D Cellular Automata found through Polynomial Cellular Neural Networks
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Abstract
In this paper we show how polynomial cellular neural networks can be used to find new properties of two-dimensional binary cellular automata (CA). In particular, we define formally a complexity index for totalistic and semi-totalistic CA, and we discuss on the intrinsic complexity of universal CA finding a surprising result: universal rules are slightly more complex than linearly separable ones.
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Citaciones: 5