Spontaneous Emergence of Computation in Network Cascades

TL;DR

This paper demonstrates that complex Boolean functions can spontaneously emerge in neuronal networks through cascades, with the emergence influenced by network connectivity and inhibition, revealing insights into neural computation and information processing.

Contribution

It reveals how computation of Boolean functions arises spontaneously in threshold networks via cascades, linking network parameters to emergent computational complexity.

Findings

Boolean functions emerge spontaneously in threshold networks

Optimal inhibition supports efficient information processing

Inverse relationship between motif complexity and function probability

Abstract

Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.