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

Klinger, E., Motta, A., Marr, C., Theis, F. J., & Helmstaedter, M. (2021). Cellular connectomes as arbiters of local circuit models in the cerebral cortex. Nature Communications, 12: 2785. doi:10.1038/s41467-021-22856-z.

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Klinger, Emmanuel1, 2, 3, Author
Motta, Alessandro1, Author
Marr, Carsten2, Author
Theis , Fabian J.2, 3, Author
Helmstaedter, Moritz1, Author           
Affiliations:
1Connectomics Department, Max Planck Institute for Brain Research, Max Planck Society, ou_2461695              
2Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany, ou_persistent22              
3Technische Universität München, Center for Mathematics, Chair of Mathematical Modelling of Biological Systems, Garching, Germany, ou_persistent22              

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 Abstract: 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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Language(s): eng - English
 Dates: 2017-12-012021-03-282021-05-13
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41467-021-22856-z
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Title: Nature Communications
  Abbreviation : Nat Commun
Source Genre: Journal
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Publ. Info: London : Nature Research
Pages: - Volume / Issue: 12 Sequence Number: 2785 Start / End Page: - Identifier: ISSN: 2041-1723