Keisuke Sehara
3174e51bf2
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| frame-capture | 3 years ago | |
| position-estimation | 3 years ago | |
| realtime-accuracy | 3 years ago | |
| spike2-profiling | 3 years ago | |
| trigger-output | 3 years ago | |
| .gitattributes | 3 years ago | |
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| LICENSE | 3 years ago | |
| README.md | 3 years ago | |
README.md
Performance-profiling data for the real-time DeepLabCut project
Latency and accuracy profiling for the real-time DeepLabCut project.
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.
Subdirectories
Generated data, along with the code, can be found on each directory.
- frame-capture contains the code and data used to profile the time spent for capturing a video frame.
- position-estimation contains the code and data used to profile the time spent for the body-part estimation step.
- trigger-output contains the code and data used to profile the time spent for trigger output generation.
- spike2-profiling contains the code and the summary data of the hardware-recorded frame rate and trigger latency.
- realtime-accuracy contains the code and the summary data of computing the real-time accuracy of trigger output generation.
| datacite.yml | |
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| 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)
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| 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 |