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Computing with Probabilistic Cellular Automata

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Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5769))

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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.

This work was supported by ETH Research Grant TH-04 07-2.

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Author information

Authors and Affiliations

  1. Institute of Neuroinformatics, ETH and University of Zurich, 8057, Zurich, Switzerland

    Martin Schüle & Ruedi Stoop

  2. Institute of Applied Simulation, ZHAW Zurich, 8820, Wädenswil, Switzerland

    Thomas Ott

Authors
  1. Martin Schüle
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  2. Thomas Ott
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  3. Ruedi Stoop
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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Schüle, M., Ott, T., Stoop, R. (2009). Computing with Probabilistic Cellular Automata. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_53

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  • DOI: https://doi.org/10.1007/978-3-642-04277-5_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04276-8

  • Online ISBN: 978-3-642-04277-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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