Occurrence:

The Probabilistic Cellular Automaton is a modification of the classic cellular automaton, where random or probabilistic elements are introduced. The concept of using probabilities in cellular automata has been explored for many years. One of the earliest studies in this area is the work of John Hopfield and Nancy H. Himmel in 1986, where they proposed a model of a probabilistic cellular automaton to model neural networks.

Principle of operation:

The number of neighbors of the grain (output cell) and, accordingly, the principle of neighborhood is the same as in the cell Moore automaton. But the difference is that each neighbor is given a probability with which it will be filled.