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Klinger, E. ; Motta, A.* ; Marr, C. ; Theis, F.J. ; Helmstaedter, M.*

Cellular connectomes as arbiters of local circuit models in the cerebral cortex.

Nat. Commun. 12:2785 (2021)
Publ. Version/Full Text DOI PMC
Open Access Gold
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With the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex. Here, we investigated whether cellular-resolution connectomic data can in principle allow model discrimination for local circuit modules in layer 4 of mouse primary somatosensory cortex. We used approximate Bayesian model selection based on a set of simple connectome statistics to compute the posterior probability over proposed models given a to-be-measured connectome. We find that the distinction of the investigated local cortical models is faithfully possible based on purely structural connectomic data with an accuracy of more than 90%, and that such distinction is stable against substantial errors in the connectome measurement. Furthermore, mapping a fraction of only 10% of the local connectome is sufficient for connectome-based model distinction under realistic experimental constraints. Together, these results show for a concrete local circuit example that connectomic data allows model selection in the cerebral cortex and define the experimental strategy for obtaining such connectomic data.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Approximate Bayesian Computation; Layer 4; Inhibitory Interneurons; Reconstruction; Network; Neurons; Barrel; Selection; Column; Connectivity
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 12, Issue: 1, Pages: , Article Number: 2785 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)