Stochastic cellular automaton

Stochastic cellular automata or 'probabilistic cellular automata' (PCA) or 'random cellular automata' or locally interacting Markov chains[1] 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 homogenous 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.

PCA as Markov stochastic processes

As discrete-time Markov process, PCA are defined on a product space E=\prod_{k \in G} S_k (cartesian product) where G is a finite or infinite graph, like \mathbb Z and where S_k is a finite space, like for instance S_k=\{-1,+1\} or S_k=\{0,1\} . The transition probability has a product form P(d\sigma | \eta) = \otimes_{k \in G} p_k(d\sigma_k | \eta) where \eta \in E and p_k(d\sigma_k | \eta) is a probability distribution on S_k . In general some locality is required p_k(d\sigma_k | \eta)=p_k(d\sigma_k | \eta_{V_k}) where \eta_{V_k}=(\eta_j)_{j\in V_k} with {V_k} a finite neighbourhood of k.

Examples of stochastic cellular automaton

Majority cellular automaton

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

Relation to random fields

PCA may be used to simulate the Ising model of ferromagnetism in statistical mechanics.[2] Some categories of models were studied from a statistical mechanics point of view.

Cellular Potts model

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

References

  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. ↑ 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.

Additional reading