New properties of 2D Cellular Automata found through Polynomial Cellular Neural Networks
Giovanni Egidio Pazienza, Eduardo Gomez-Ramirez · 2009
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.