Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

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 informati...

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Vydané v:	PLoS computational biology Ročník 17; číslo 9; s. e1009439
Hlavní autori:	Whiteway, Matthew R., Biderman, Dan, Friedman, Yoni, Dipoppa, Mario, Buchanan, E. Kelly, Wu, Anqi, Zhou, John, Bonacchi, Niccolò, Miska, Nathaniel J., Noel, Jean-Paul, Rodriguez, Erica, Schartner, Michael, Socha, Karolina, Urai, Anne E., Salzman, C. Daniel, Cunningham, John P., Paninski, Liam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 22.09.2021 Public Library of Science (PLoS)
Predmet:	Algorithms Animal behavior Animals Artificial Intelligence - statistics & numerical data Behavior, Animal Biology and Life Sciences Brain research Cameras Computational Biology Computer and Information Sciences Computer Simulation Computer vision Datasets Encoders Engineering and Technology Feature extraction Information processing Machine learning Machine vision Markov Chains Medicine and Health Sciences Mice Models, Statistical Nervous system Neural networks Neural Networks, Computer Principal components analysis Production methods Social Sciences Supervised Machine Learning - statistics & numerical data Unsupervised Machine Learning - statistics & numerical data Video compression Video data Video Recording - statistics & numerical data United States
ISSN:	1553-7358, 1553-734X, 1553-7358
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Popis
Shrnutí:	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.
Bibliografia:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1009439