Adaptive rewiring of random neural networks generates convergent–divergent​ units

Abstract

Brain networks are continually rewired adaptively, 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 advection and consensus 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.