Keisuke Sehara
7e3ce5c906
add spike2 recordings again
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3 years ago | |
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| SNA-079258 | 3 years ago | |
| SNA-079259 | 3 years ago | |
| SNA-079260 | 3 years ago | |
| LICENSE | 3 years ago | |
| README.md | 3 years ago | |
README.md
Spike2 recording data for the real-time DeepLabCut project
Spike2 recordings for the real-time DeepLabCut project. Recording was performed using CED Power1401.
File organization
- The name the top directories represent the name of the animals used in the study.
- Each of the top directories has subdirectories corresponding to one behavioral session. The label "train" indicates that the session was used to train the DeepLabCut model, and trigger output is not available. The label "test" indicates that real-time trigger generation was used based on the corresponding DeepLabCut model.
- Some sessions separate Spike2 recordings with video recordings, but the others have one Spike2 recording for multiple video recordings.
For the ease of reuse, we converted the original .smrx data into the HDF5 format. Each dataset entry corresponds to an analog channel. More information can be found in the attribute fields of the root entry and the channel entries.
| 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 |