Robust transformations of firing patterns for neural networks

Abstract

As a promising computational paradigm, occurrence of critical states in artificial and biological neural networks has attracted wide-spread attention. An often-made explicit or implicit assumption is that one single critical state is responsible for two separate notions of criticality (avalanche criticality and dynamical edge of chaos criticality). Previously, we provided an isolated counter-example for co-occurrence. Here, we reveal a persistent paradigm of structural transitions that such networks undergo, as the overall connectivity strength is varied over its biologically meaningful range. Among these transitions, only one avalanche critical point emerges, with edge of chaos failing to co-occur. Our observations are based on ensembles of networks obtained from variations of network configuration and their neurons. This suggests that not only non-coincidence of criticality, but also the persistent paradigm of network structural changes in function of the overall connectivity strength, could be generic features of a large class of biological neural networks.

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