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

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.