Structural characteristics of local cortical networks wired by distance dependent connectivity rules

Background The function of the cerebral cortex is shaped by its anatomical connectivity, yet experimental findings on connection probabilities in local cortical networks remain inconsistent. This study explores structural characteristics of local cortical networks based on distance dependent, Gaussian connectivity profiles. Monolayers of 101 x 101 pyramidal neurons were examined. Their connectivity was based on experimental anatomical or electrophysiological data. In the anatomical setting the connection probability between neighboring neurons was 0.8. In the electrophysiological scenario connection probabilities for adjacent neurons ranged between 0.08 and 0.23. All distance dependent networks were compared to the configuration model which generates degree-preserving but otherwise randomly rewired networks. The networks thus constructed were analyzed applying tools of network science, i.e. average degrees, degree distributions, local clustering coefficients and graph distances. Moreover, the numbers, sizes and spatial dimensions of cliques were investigated as well as the cost of connectivity. Results Distance-dependent networks differed fundamentally from configuration-model networks across all structural measures. They showed substantially higher local clustering, formed more numerous and more spatially compact groups of strongly connected neurons, and required lower wiring cost. Importantly, the structure of distance-dependent networks was highly sensitive to near-neighbor connectivity: when neurons had a high probability of connecting locally, the network reliably developed tightly wired, spatially localized assemblies. Conclusions Distance-dependent connectivity gives rise to structural network features that may facilitate the emergence of functional neuronal assemblies. Based on the findings of this study, a general probabilistic rule for local cortical connectivity is proposed that can be used to design artificial neural networks with biologically inspired wiring principles.