Stochastic cellular automaton

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Stochastic cellular automata or 'probabilistic cellular automata' (PCA) or 'random cellular automata' or locally interacting Markov chains[1][2] are an important extension of cellular automaton. Cellular automata are a discrete-time dynamical system of interacting entities, whose state is discrete.

The state of the collection of entities is updated at each discrete time according to some simple homogeneous rule. All entities' states are updated in parallel or synchronously. Stochastic Cellular Automata are CA whose updating rule is a stochastic one, which means the new entities' states are chosen according to some probability distributions. It is a discrete-time random dynamical system. From the spatial interaction between the entities, despite the simplicity of the updating rules, complex behaviour may emerge like self-organization. As mathematical object, it may be considered in the framework of stochastic processes as an interacting particle system in discrete-time. See [3] for a more detailed introduction.

PCA as Markov stochastic processes[edit]

As discrete-time Markov process, PCA are defined on a product space (cartesian product) where is a finite or infinite graph, like and where is a finite space, like for instance or . The transition probability has a product form where and is a probability distribution on . In general some locality is required where with a finite neighbourhood of k. See [4] for a more detailed introduction following the probability theory's point of view.

Examples of stochastic cellular automaton[edit]

Majority cellular automaton[edit]

There is a version of the majority cellular automaton with probabilistic updating rules. See the Toom's rule.

Relation to lattice random fields[edit]

PCA may be used to simulate the Ising model of ferromagnetism in statistical mechanics.[5] Some categories of models were studied from a statistical mechanics point of view.

Cellular Potts model[edit]

There is a strong connection[6] between probabilistic cellular automata and the cellular Potts model in particular when it is implemented in parallel.

Non Markovian generalization[edit]

The Galves-Locherbach model is an example of a generalized PCA with a non Markovian aspect.


  1. ^ Toom, A. L. (1978), Locally Interacting Systems and their Application in Biology: Proceedings of the School-Seminar on Markov Interaction Processes in Biology, held in Pushchino, March 1976, Lecture Notes in Mathematics, 653, Springer-Verlag, Berlin-New York, ISBN 3-540-08450-9, MR 0479791
  2. ^ R. L. Dobrushin; V. I. Kri︠u︡kov; A. L. Toom (1978). Stochastic Cellular Systems: Ergodicity, Memory, Morphogenesis. ISBN 9780719022067.
  3. ^ Fernandez, R.; Louis, P.-Y.; Nardi, F. R. (2018). "Chapter 1: Overview: PCA Models and Issues". In Louis, P.-Y.; Nardi, F. R. Probabilistic Cellular Automata. Springer. doi:10.1007/978-3-319-65558-1_1. ISBN 9783319655581.
  4. ^ P.-Y. Louis PhD
  5. ^ Vichniac, G. (1984), "Simulating physics with cellular automata", Physica D, 10: 96–115, Bibcode:1984PhyD...10...96V, doi:10.1016/0167-2789(84)90253-7.
  6. ^ Boas, Sonja E. M.; Jiang, Yi; Merks, Roeland M. H.; Prokopiou, Sotiris A.; Rens, Elisabeth G. (2018). "Chapter 18: Cellular Potts Model: Applications to Vasculogenesis and Angiogenesis". In Louis, P.-Y.; Nardi, F. R. Probabilistic Cellular Automata. Springer. doi:10.1007/978-3-319-65558-1_18. ISBN 9783319655581.

Further reading[edit]