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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Veröffentlicht in:	PLoS computational biology Jg. 17; H. 9; S. e1009439
Hauptverfasser:	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
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 22.09.2021 Public Library of Science (PLoS)
Schlagworte:	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
Online-Zugang:	Volltext
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