On Memory and Structural Dynamism in Excitable Cellular Automata with Defensive Inhibition

TL;DR

This paper explores how incorporating short-term memory and dynamic topology into excitable cellular automata can lead to novel complex spatio-temporal behaviors, resembling neural network dynamics with inhibitory mechanisms.

Contribution

It introduces a model of excitable cellular automata with memory and dynamic links, extending traditional automata to better simulate neural-like inhibitory processes.

Findings

Memory and topology dynamics produce complex patterns.

Inhibition effects mimic neural network behavior.

Automata exhibit novel spatio-temporal regimes.

Abstract

Commonly studied cellular automata are memoryless and have fixed topology of connections between cells. However by allowing updates of links and short-term memory in cells we may potentially discover novel complex regimes of spatio-temporal dynamics. Moreover by adding memory and dynamical topology to state update rules we somehow forge elementary but non-traditional models of neurons networks (aka neuron layers in frontal parts). In present paper we demonstrate how this can be done on a self-inhibitory excitable cellular automata. These automata imitate a phenomenon of inhibition caused by high-strength stimulus: a resting cell excites if there are one or two excited neighbors, the cell remains resting otherwise. We modify the automaton by allowing cells to have few-steps memories, and make links between neighboring cells removed or generated depending on states of the cells.