DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection

DeepLabStream, developed by Schweihoff and colleagues, is a deep-learning based toolkit to conduct closed-loop behavioral experiments triggered by postural expressions. The capabilities of this new toolkit are shown in an optogenetic stimulation experiment capturing activity dependent neuronal ensem...

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Autores principales:	Jens F. Schweihoff, Matvey Loshakov, Irina Pavlova, Laura Kück, Laura A. Ewell, Martin K. Schwarz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/0b703c96fcda4d4aacde89d962e46ac8
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spelling	oai:doaj.org-article:0b703c96fcda4d4aacde89d962e46ac82021-12-02T14:16:33ZDeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection10.1038/s42003-021-01654-92399-3642https://doaj.org/article/0b703c96fcda4d4aacde89d962e46ac82021-01-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-01654-9https://doaj.org/toc/2399-3642DeepLabStream, developed by Schweihoff and colleagues, is a deep-learning based toolkit to conduct closed-loop behavioral experiments triggered by postural expressions. The capabilities of this new toolkit are shown in an optogenetic stimulation experiment capturing activity dependent neuronal ensembles, as well as in an autonomously conducted conditioning task.Jens F. SchweihoffMatvey LoshakovIrina PavlovaLaura KückLaura A. EwellMartin K. SchwarzNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jens F. Schweihoff Matvey Loshakov Irina Pavlova Laura Kück Laura A. Ewell Martin K. Schwarz DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection
description	DeepLabStream, developed by Schweihoff and colleagues, is a deep-learning based toolkit to conduct closed-loop behavioral experiments triggered by postural expressions. The capabilities of this new toolkit are shown in an optogenetic stimulation experiment capturing activity dependent neuronal ensembles, as well as in an autonomously conducted conditioning task.
format	article
author	Jens F. Schweihoff Matvey Loshakov Irina Pavlova Laura Kück Laura A. Ewell Martin K. Schwarz
author_facet	Jens F. Schweihoff Matvey Loshakov Irina Pavlova Laura Kück Laura A. Ewell Martin K. Schwarz
author_sort	Jens F. Schweihoff
title	DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection
title_short	DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection
title_full	DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection
title_fullStr	DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection
title_full_unstemmed	DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection
title_sort	deeplabstream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0b703c96fcda4d4aacde89d962e46ac8
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DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection

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