Functional and spatial rewiring principles jointly regulate context-sensitive computation

Adaptive rewiring provides a basic principle of self-organizing connectivity in evolving neural network topology. By selectively adding connections to regions with intense signal flow and deleting underutilized connections, adaptive rewiring generates optimized brain-like, i.e. modular, small-world, and rich club connectivity structures. Besides topology, neural self-organization also follows spatial optimization principles, such as minimizing the neural wiring distance and topographic alignment of neural pathways. We simulated the interplay of these spatial principles and adaptive rewiring in evolving neural networks with weighted and directed connections. The neural traffic flow within the network is represented by the equivalent of diffusion dynamics for directed edges: consensus and advection. We observe a constructive synergy between adaptive and spatial rewiring, which contributes to network connectedness. In particular, wiring distance minimization facilitates adaptive rewiring in creating convergent-divergent units. These units support the flow of neural information and enable context-sensitive information processing in the sensory cortex and elsewhere. Convergent-divergent units consist of convergent hub nodes, which collect inputs from pools of nodes and project these signals via a densely interconnected set of intermediate nodes onto divergent hub nodes, which broadcast their output back to the network. Convergent-divergent units vary in the degree to which their intermediate nodes are isolated from the rest of the network. This degree, and hence the context-sensitivity of the network’s processing style, is parametrically determined in the evolving network model by the relative prominence of spatial versus adaptive rewiring.Author summary: Context-sensitivity plays an important role in neural signal processing. A type of network structure supporting context-sensitive processing is known as a convergent-divergent unit. Convergent-divergent units can give rise to sensory neurons that respond to local features but are also modulated, in their activities, by long-range contextual information. These units can arise through self-organization in neural networks according to adaptive rewiring, a principle used to optimize network structure for information processing. Two spatial rewiring principles, distance minimization and topographic alignment, are also considered important in shaping network connectivity structure, but their role in the formation of context sensitivity is unknown. We asked whether these spatial rewiring principles can facilitate adaptive rewiring in the formation of convergent-divergent units. Using weighted digraphs, we found that the proportion of rewiring based on distance minimization improves context-sensitivity, as shown by the increased robustness of the convergent-divergent units, while the subtler effects of the alignment principle depend on the shape of the topographic map that network connections align with.