ArticleNature communications2021
Cellular connectomes as arbiters of local circuit models in the cerebral cortex.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Simulation-based inference for efficient identification of generative models in computational connectomics.PLoS computational biology · 2023Article
- Connecting Connectomes to Physiology.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Review
- mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.bioRxiv : the preprint server for biology · 2023Article
- mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.Frontiers in neural circuits · 2023Article
- Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy.Nature communications · 2022Article
- Sample Preparation and Warping Accuracy for Correlative Multimodal Imaging in the Mouse Olfactory Bulb Using 2-Photon, Synchrotron X-Ray and Volume Electron Microscopy.Frontiers in cell and developmental biology · 2022Article
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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