  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.
