Whiteway, MR;
Biderman, D;
Friedman, Y;
Dipoppa, M;
Buchanan, EK;
Wu, A;
Zhou, J;
... Paninski, L; + view all
(2021)
Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.
PLoS Computational Biology
, 17
(9)
, Article e1009439. 10.1371/journal.pcbi.1009439.
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Abstract
Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone. Copyright:
| Type: | Article |
|---|---|
| Title: | Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1009439 |
| Publisher version: | https://doi.org/10.1371/journal.pcbi.1009439 |
| Language: | English |
| Additional information: | Copyright: © 2021 Whiteway et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| Keywords: | Algorithms, Animals, Artificial Intelligence, Behavior, Animal, Computational Biology, Computer Simulation, Markov Chains, Mice, Models, Statistical, Neural Networks, Computer, Supervised Machine Learning, Unsupervised Machine Learning, Video Recording |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10147388 |
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