  Self-organization through adaptive rewiring of random neural networks
generates brain-like topologies comprising modular small-world structures with
rich club effects, merely as the product of optimizing the network topology. In
the nervous system, spatial organization is optimized no less by rewiring,
through minimizing wiring distance and maximizing spatially aligned wiring
layouts. We show that such spatial organization principles interact
constructively with adaptive rewiring, contributing to establish the networks'
connectedness and modular structures. We use an evolving neural network model
with weighted and directed connections, in which neural traffic flow is based
on consensus and advection dynamics, to show that wiring cost minimization
supports adaptive rewiring in creating convergent-divergent unit structures.
Convergent-divergent units consist of a convergent input-hub, connected to a
divergent output-hub via subnetworks of intermediate nodes, which may function
as the computational core of the unit. The prominence of minimizing wiring
distance in the dynamic evolution of the network determines the extent to which
the core is encapsulated from the rest of the network, i.e., the
context-sensitivity of its computations. This corresponds to the central role
convergent-divergent units play in establishing context-sensitivity in neuronal
information processing.
