Computing with Probabilistic Cellular Automata

Authors:
Martin Schüle;Thomas Ott;Ruedi Stoop
Affiliations:
Institute of Neuroinformatics, ETH and University of Zurich, Zurich, Switzerland 8057;Institute of Applied Simulation, ZHAW Zurich, Wädenswil, Switzerland 8820;Institute of Neuroinformatics, ETH and University of Zurich, Zurich, Switzerland 8057
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

We investigate the computational capabilities of probabilistic cellular automata by means of the density classification problem. We find that a specific probabilistic cellular automata rule is able to solve the density classification problem, i.e. classifies binary input strings according to the number of 1's and 0's in the string, and show that its computational abilities are related to critical behaviour at a phase transition.