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 semitotalistic 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.
['Giovanni Egidio Pazienza', 'Eduardo Gómez-Ramírez']
New properties of 2D Cellular Automata found through Polynomial Cellular Neural Networks