Glider compositionality and information capacity in an E/I-balanced neuronal automaton

Abstract

In cellular automata, localized activity patterns called gliders propagate and interact to carry and transform information. Whether neuronal networks can harness this computational power remains unclear. Here, we demonstrate that a cortical network model with separate excitatory and inhibitory populations, graded cellular activity and uniform local connectivity spontaneously generates diverse, interacting gliders. An analytical reduction identifies the cellular conditions sustaining propagation of the dominant G-glider. Distinct gliders can assemble into growing structures that emit other glider types. Near excitation/inhibition balance, sparse glider dynamics coexist with high computational capacity, measured by input differentiation and generalization, with local capacity varying across space and time. Strong mutual inhibition between interneurons instead permits metastatic proliferation with low computational capacity. These findings connect cellular automata to neuronal information processing and identify interacting gliders as a candidate mechanism for dynamic plasticity: the reorganization of computation within an unchanged synaptic architecture.