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Sehara_et_al_2021_RealtimeDLC_DataRepository
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10.12751/g-node.lgu2m0
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Keisuke Sehara a00abd6701 add DLC projects back 3 years ago
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02_training_logs a00abd6701 add DLC projects back 3 years ago
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README.md a00abd6701 add DLC projects back 3 years ago

README.md

DeepLabCut projects for the real-time DeepLabCut project

"Projects", in the sense they are used in DeepLabCut. The names of videos are compatible with (but, probably, not exactly the same as) what you can find in the RawVideos repository.

IMPORTANT NOTE

The code reflects the organization of files when the dataset was generated, and it is very likely that it does not work as-is. Please change the output path etc. for the code to work properly.

File organization

  1. 01_projects contains the DLC projects themselves.
  2. 02_training_logs contains the Jupyter notebooks when we trained the projects.
datacite.yml
Title Data for Sehara et al., 2021 eNeuro (the real-time DeepLabCut project)
Authors Sehara,Keisuke;Institut für Biologie, Humboldt Universität zu Berlin, Berlin, 10117 Germany.;ORCID:0000-0003-4368-8143
Zimmer-Harwood,Paul;Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, United Kingdom.
Colomb,Julien;Institut für Biologie, Humboldt Universität zu Berlin, Berlin, 10117 Germany.;ORCID:0000-0002-3127-5520
Larkum,Matthew E.;Institut für Biologie, Humboldt Universität zu Berlin, Berlin, 10117 Germany.;ORCID:0000-0002-6627-0199
Sachdev,Robert N.S.;Institut für Biologie, Humboldt Universität zu Berlin, Berlin, 10117 Germany.;ORCID:0000-0002-3127-5520
Description Computer vision approaches have made significant inroads into offline tracking of behavior and estimating animal poses. In particular, because of their versatility, deep-learning approaches have been gaining attention in behavioral tracking without any markers. Here we developed an approach using DeepLabCut for real-time estimation of movement. We trained a deep neural network offline with high-speed video data of a mouse whisking, then transferred the trained network to work with the same mouse, whisking in real-time. With this approach, we tracked the tips of three whiskers in an arc and converted positions into a TTL output within behavioral time scales, i.e 10.5 millisecond. With this approach it is possible to trigger output based on movement of individual whiskers, or on the distance between adjacent whiskers. Flexible closed-loop systems like the one we have deployed here can complement optogenetic approaches and can be used to directly manipulate the relationship between movement and neural activity.
License Creative Commons 4.0 Attribution (https://creativecommons.org/licenses/by/4.0/)
References Sehara K, Zimmer-Harwood P, Larkum ME, Sachdev RNS (2021) Real-time closed-loop feedback in behavioral time scales using DeepLabCut. [doi:10.1523/eneuro.0415-20.2021] (IsSupplementTo)
Funding EU, EU.670118
EU, EU.327654276
EU, EU.720270
EU, EU.785907
EU, EU.945539
DFG, DFG.250048060
DFG, DFG.246731133
DFG, DFG.267823436
Keywords Neuroscience
Behavioral tracking
Closed-loop experiment system
Resource Type Dataset