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

View ORCID ProfileMatthew R Whiteway, View ORCID ProfileDan Biderman, Yoni Friedman, View ORCID ProfileMario Dipoppa, View ORCID ProfileE. Kelly Buchanan, Anqi Wu, John Zhou, View ORCID ProfileJean-Paul Noel, The International Brain Laboratory, John Cunningham, Liam Paninski
doi: https://doi.org/10.1101/2021.02.22.432309
Matthew R Whiteway
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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  • For correspondence: m.whiteway@columbia.edu
Dan Biderman
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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Yoni Friedman
1Center for Theoretical Neuroscience, Columbia University, New York, USA
7Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, USA
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Mario Dipoppa
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
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E. Kelly Buchanan
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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  • ORCID record for E. Kelly Buchanan
Anqi Wu
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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John Zhou
6Department of Computer Science, Columbia University, New York, USA
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Jean-Paul Noel
8Center for Neural Science, New York University, New York, USA
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  • ORCID record for Jean-Paul Noel
John Cunningham
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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Liam Paninski
1Center for Theoretical Neuroscience, Columbia University, New York, USA
2Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
3Grossman Center for the Statistics of Mind, Columbia University, New York, USA
4Department of Statistics, Columbia University, New York, USA
5Department of Neuroscience, Columbia University, New York, USA
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Abstract

Recent neuroscience studies in awake and behaving animals 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 this video data. In this work we introduce a new semi-supervised framework 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 method, the Partitioned Subspace Variational Autoencoder (PS-VAE), on head-fixed mouse behavioral videos. In a close up video of a mouse face, where we track pupil location and size, our method extracts unsupervised outputs that correspond to the eyelid and whisker pad positions, with no additional user annotations required. We use this resulting interpretable behavioral representation to construct saccade and whisking detectors, and quantify the accuracy with which these signals can be decoded from neural activity in visual cortex. In a two-camera mouse video we show how our method separates movements of experimental equipment from animal behavior, and extracts unsupervised features like chest position, again with no additional user annotation needed. This allows us to construct paw and body movement detectors, and decode individual features of behavior from widefield calcium imaging data. Our results demonstrate how the interpretable partitioning of behavioral videos provided by the PS-VAE can facilitate downstream behavioral and neural analyses.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 23, 2021.
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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders
Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E. Kelly Buchanan, Anqi Wu, John Zhou, Jean-Paul Noel, The International Brain Laboratory, John Cunningham, Liam Paninski
bioRxiv 2021.02.22.432309; doi: https://doi.org/10.1101/2021.02.22.432309
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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders
Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E. Kelly Buchanan, Anqi Wu, John Zhou, Jean-Paul Noel, The International Brain Laboratory, John Cunningham, Liam Paninski
bioRxiv 2021.02.22.432309; doi: https://doi.org/10.1101/2021.02.22.432309

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