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AlphaTracker: A Multi-Animal Tracking and Behavioral Analysis Tool

Zexin Chen, View ORCID ProfileRuihan Zhang, View ORCID ProfileYu Eva Zhang, View ORCID ProfileHaowen Zhou, Hao-Shu Fang, View ORCID ProfileRachel R. Rock, View ORCID ProfileAneesh Bal, Nancy Padilla-Coreano, Laurel Keyes, View ORCID ProfileKay M. Tye, Cewu Lu
doi: https://doi.org/10.1101/2020.12.04.405159
Zexin Chen
1Shanghai Jiao Tong University Department of Computer Science, Shanghai, China
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Ruihan Zhang
2Zhiyuan College, Shanghai Jiao Tong University, Minhang, Shanghai, China
3Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
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Yu Eva Zhang
4Neurobiology Section, Center for Neural Circuits and Behavior, and Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
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Haowen Zhou
5Zhiyuan College, Shanghai Jiao Tong University, Minhang, Shanghai, China
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Hao-Shu Fang
1Shanghai Jiao Tong University Department of Computer Science, Shanghai, China
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Rachel R. Rock
6Salk Institute for Biological Studies, La Jolla, California
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Aneesh Bal
6Salk Institute for Biological Studies, La Jolla, California
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Nancy Padilla-Coreano
6Salk Institute for Biological Studies, La Jolla, California
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Laurel Keyes
6Salk Institute for Biological Studies, La Jolla, California
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Kay M. Tye
6Salk Institute for Biological Studies, La Jolla, California
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  • For correspondence: lucewu@sjtu.edu.cn tye@salk.edu
Cewu Lu
1Shanghai Jiao Tong University Department of Computer Science, Shanghai, China
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  • For correspondence: lucewu@sjtu.edu.cn tye@salk.edu
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Abstract

The advancement of behavioral analysis in neuroscience has been aided by the development of computational tools1,2. Specifically, computer vision algorithms have emerged as a powerful tool to elevate behavioral research3,4. Yet fully automatic analysis of social behavior remains challenging in two ways. First, existing tools to track and analyze behavior often focus on single animals, not multiple, interacting animals. Second, many available tools are not developed for novice users and require programming experience to run. Here, we unveil a computer vision pipeline called AlphaTracker, which requires minimal hardware requirements and produces reliable tracking of multiple unmarked animals. An easy-to-use user interface further enables manual inspection and curation of results. We demonstrate the practical, real-time advantages of AlphaTracker through the study of multiple, socially-interacting mice.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The AlphaTracker code is available here: https://github.com/ZexinChen/AlphaTracker

  • https://github.com/ZexinChen/AlphaTracker

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 December 06, 2020.
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AlphaTracker: A Multi-Animal Tracking and Behavioral Analysis Tool
Zexin Chen, Ruihan Zhang, Yu Eva Zhang, Haowen Zhou, Hao-Shu Fang, Rachel R. Rock, Aneesh Bal, Nancy Padilla-Coreano, Laurel Keyes, Kay M. Tye, Cewu Lu
bioRxiv 2020.12.04.405159; doi: https://doi.org/10.1101/2020.12.04.405159
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AlphaTracker: A Multi-Animal Tracking and Behavioral Analysis Tool
Zexin Chen, Ruihan Zhang, Yu Eva Zhang, Haowen Zhou, Hao-Shu Fang, Rachel R. Rock, Aneesh Bal, Nancy Padilla-Coreano, Laurel Keyes, Kay M. Tye, Cewu Lu
bioRxiv 2020.12.04.405159; doi: https://doi.org/10.1101/2020.12.04.405159

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