The data repository for Sehara et al., 2021 eNeuro, real-time DeepLabCut (Pose-Trigger) project.

Keisuke Sehara 62f28e59f5 update metadata		3 years ago
DLCProjects @ e532b022be	8b7836b9c0 add DLCProjects	3 years ago
PerformanceProfiling @ 245f553fb9	49b0523a6d add PerformanceProfiling	3 years ago
PostHocEstimations @ b6c0886ebc	851ecefc63 add PostHocEstimations	3 years ago
RawVideos @ 9a1122550a	98b1b8241f add RawVideos repository	3 years ago
Spike2Recordings @ d46e794ba7	3e58d2c435 add Spike2Recordings	3 years ago
.gitmodules	49b0523a6d add PerformanceProfiling	3 years ago
LICENSE	4c54e69817 Initial commit	3 years ago
README.md	62f28e59f5 update metadata	3 years ago
datacite.yml	62f28e59f5 update metadata	3 years ago

Data for Sehara et al., the real-time DeepLabCut project

The data repository for Sehara et al., the real-time DeepLabCut (Pose-Trigger) project.

Datasets

Raw videos: the raw videos acquired using Pose-Trigger. The files are converted to HDF5 files (instead of the original NumPy files).
Spike2 recordings: recordings of the frame and trigger signals during Pose-Trigger acquisition, using Spike2. The files are converted to HDF5 files (instead of the original .smrx files).
DeepLabCut projects: the DeepLabCut (v2.1) projects used in the study.
Post hoc pose estimation: the post-hoc pose-estimation data to be compared with the real-time data. The files are in the HDF5 format (in the structure different from the "original" PyTables format that DeepLabCut generates).
Performance profiling: the data and analytical procedures (and some figures) used to profile the speed and accuracy of Pose-Trigger.

License

datacite.yml
Title	Data for Sehara et al., 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. [] (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

README.md

Data for Sehara et al., the real-time DeepLabCut project

Datasets

License