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Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography

Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual annotation. Scalable and objective tracking methods...

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Autores principales: Ebbesen, Christian L., Froemke, Robert C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8807631/
https://www.ncbi.nlm.nih.gov/pubmed/35105858
http://dx.doi.org/10.1038/s41467-022-28153-7
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author Ebbesen, Christian L.
Froemke, Robert C.
author_facet Ebbesen, Christian L.
Froemke, Robert C.
author_sort Ebbesen, Christian L.
collection PubMed
description Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual annotation. Scalable and objective tracking methods are required to understand the neural circuits underlying social behavior. Here we describe a hardware/software system and analysis pipeline that combines 3D videography, deep learning, physical modeling, and GPU-accelerated robust optimization, with automatic analysis of neuronal receptive fields recorded in interacting mice. Our system (“3DDD Social Mouse Tracker”) is capable of fully automatic multi-animal tracking with minimal errors (including in complete darkness) during complex, spontaneous social encounters, together with simultaneous electrophysiological recordings. We capture posture dynamics of multiple unmarked mice with high spatiotemporal precision (~2 mm, 60 frames/s). A statistical model that relates 3D behavior and neural activity reveals multiplexed ‘social receptive fields’ of neurons in barrel cortex. Our approach could be broadly useful for neurobehavioral studies of multiple animals interacting in complex low-light environments.
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spelling pubmed-88076312022-02-07 Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography Ebbesen, Christian L. Froemke, Robert C. Nat Commun Article Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions and their neural correlates are lacking. Currently, most studies rely on labor-intensive methods such as manual annotation. Scalable and objective tracking methods are required to understand the neural circuits underlying social behavior. Here we describe a hardware/software system and analysis pipeline that combines 3D videography, deep learning, physical modeling, and GPU-accelerated robust optimization, with automatic analysis of neuronal receptive fields recorded in interacting mice. Our system (“3DDD Social Mouse Tracker”) is capable of fully automatic multi-animal tracking with minimal errors (including in complete darkness) during complex, spontaneous social encounters, together with simultaneous electrophysiological recordings. We capture posture dynamics of multiple unmarked mice with high spatiotemporal precision (~2 mm, 60 frames/s). A statistical model that relates 3D behavior and neural activity reveals multiplexed ‘social receptive fields’ of neurons in barrel cortex. Our approach could be broadly useful for neurobehavioral studies of multiple animals interacting in complex low-light environments. Nature Publishing Group UK 2022-02-01 /pmc/articles/PMC8807631/ /pubmed/35105858 http://dx.doi.org/10.1038/s41467-022-28153-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ebbesen, Christian L.
Froemke, Robert C.
Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography
title Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography
title_full Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography
title_fullStr Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography
title_full_unstemmed Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography
title_short Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography
title_sort automatic mapping of multiplexed social receptive fields by deep learning and gpu-accelerated 3d videography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8807631/
https://www.ncbi.nlm.nih.gov/pubmed/35105858
http://dx.doi.org/10.1038/s41467-022-28153-7
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