Adaptive rewiring of random neural networks generates convergent-divergent units

TL;DR

This paper demonstrates that adaptive rewiring in directed neural network models, based on advection and consensus algorithms, leads to the spontaneous emergence of convergent-divergent units that resemble brain connectivity structures.

Contribution

It introduces a biologically plausible adaptive rewiring model combining advection and consensus algorithms to explain the emergence of complex directed connectivity structures.

Findings

Adaptive rewiring shortens path length and improves connectivity.

Convergent-divergent units emerge with balanced advection and consensus.

These units resemble core brain connectivity patterns.

Abstract

Brain networks are adaptively rewired continually, adjusting their topology to bring about functionality and efficiency in sensory, motor and cognitive tasks. In model neural network architectures, adaptive rewiring generates complex, brain-like topologies. Present models, however, cannot account for the emergence of complex directed connectivity structures. We tested a biologically plausible model of adaptive rewiring in directed networks, based on two algorithms widely used in distributed computing: advection and consensus. When both are used in combination as rewiring criteria, adaptive rewiring shortens path length and enhances connectivity. When keeping a balance between advection and consensus, adaptive rewiring produces convergent-divergent units consisting of convergent hub nodes, which collect inputs from pools of sparsely connected, or local, nodes and project them via densely interconnected processing nodes onto divergent hubs that broadcast output back to the local pools. Convergent-divergent units operate within and between sensory, motor, and cognitive brain regions as their connective core, mediating context-sensitivity to local network units. By showing how these structures emerge spontaneously in directed networks models, adaptive rewiring offers self-organization as a principle for efficient information propagation and integration in the brain.