Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks

Cortical microcircuits in a variety of brain regions express similar, highly nonrandom, synaptically-connected cell triplets. The origin of these universal network building blocks is unclear and was hypothesized to result from plasticity and learning processes. Here we combined in-silico modeling of dense cortical microcircuits with electrophysiological/anatomical studies to demonstrate that this recurring connectivity emerges primarily from the anisotropic morphology of cortical neurons and their embedding in the cortical volume. Using graph-theoretical and machine-learning tools, we developed a series of progressively more complex generative models for circuit connectivity that considers the geometry of cortical neurons. This framework provided predictions for the spatial alignment of cells composing particular cell-triplets which were directly validated via in-vitro whole-cell 12-patches recordings (7,309 triplets) in the rat somatosensory cortex. We concluded that the local geometry of cortical neurons imposes an innate, highly structured, global network structure, a skeleton upon which fine-grained structural and functional plasticity processes take place.

Authors
Eyal Gal, Rodrigo Perin, Henry Markram, Michael London, Idan Segev
Year of publication
2019
Journal
bioRxiv preprint first posted online May. 31, 2019