A null model of the mouse whole-neocortex micro-connectome

Michael W. Reimann (), Michael Gevaert, Ying Shi, Huanxiang Lu, Henry Markram and Eilif Muller
Additional contact information
Michael W. Reimann: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Michael Gevaert: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Ying Shi: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Huanxiang Lu: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Henry Markram: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Eilif Muller: Ecole Polytechnique Fédérale de Lausanne (EPFL)

Nature Communications, 2019, vol. 10, issue 1, 1-16

Abstract: Abstract In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-11630-x Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11630-x

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-11630-x

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().