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The impact of neuron morphology on cortical network architecture

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Udvary,  Daniel
Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Max Planck Society;

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Macke,  Jakob H
External Organizations;
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;

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Sakmann,  Bert
Emeritus Group: Cortical Column in silico / Sakmann, MPI of Neurobiology, Max Planck Society;

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Oberlaender,  Marcel
Max Planck Research Group In Silico Brain Sciences, Center of Advanced European Studies and Research (caesar), Max Planck Society;

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Citation

Udvary, D., Harth, P., Macke, J. H., Hege, H.-C., de Kock, C. P., Sakmann, B., et al. (2021). The impact of neuron morphology on cortical network architecture. bioRxiv: the preprint server for biology, 2020.11.13.381087v3. doi:10.1101/2020.11.13.381087.


Cite as: https://hdl.handle.net/21.11116/0000-0008-C6C1-E
Abstract
It has become increasingly clear that the neurons in the cerebral cortex are not randomly interconnected. This wiring specificity can result from synapse formation mechanisms that interconnect neurons depending on their activity or genetically defined identity. Here we report that in addition to these synapse formation mechanisms, the structural composition of the neuropil provides a third prominent source by which wiring specificity can emerge in cortical networks. This structurally determined wiring specificity reflects the packing density, morphological diversity and similarity of the neurons’ dendritic and axonal processes. The higher these three factors, the more recurrent the networks’ topology. Conversely, lower density, diversity and similarity yield feedforward networks. These principles predict connectivity patterns from subcellular to network scales that are remarkably consistent with empirical observations from a rich body of literature. Thus, cortical network architectures reflect the specific morphological properties of their constituents to a much larger degree than previously thought.