An algorithm to model the non-random connectome of cortical circuitry

Neuronal connectivity has been characterized at various scales and with respect to various structural aspects. In models of connectivity, it has so far remained difficult to match all of them at once, in particular the higher-order structure appears to be elusive. Here we introduce a new type of graph model that matches non-random structure characterized and described as relevant in biological neuronal networks. The structure emerges because the algorithm considers the need for axons to physically bridge the gap from soma to dendrites. If it targets one neuron, probabilities that it also targets other nearby neurons increase. We demonstrate that the algorithm can be successfully fit to complex, biologically relevant reference connectomes. Furthermore, we outline an intuitive expansion of the model from merely local to a combination of local and long-range connectivity. We provide a performant implementation that can be used to instantiate point neuron or morphologically-detailed network models at whole-cortex scale.