Selfee, self-supervised features extraction of animal behaviors
- Tsinghua University, China
- Chinese Academy of Sciences, China
Abstract
Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in laboratories for behavior analysis. However, it has not been achieved to use an end-to-end unsupervised neural network to extract comprehensive and discriminative features directly from social behavior video frames for annotation and analysis purposes. Here, we report a self-supervised feature extraction (Selfee) convolutional neural network with multiple downstream applications to process video frames of animal behavior in an end-to-end way. Visualization and classification of the extracted features (Meta-representations) validate that Selfee processes animal behaviors in a way similar to human perception. We demonstrate that Meta-representations can be efficiently used to detect anomalous behaviors that are indiscernible to human observation and hint in-depth analysis. Furthermore, time-series analyses of Meta-representations reveal the temporal dynamics of animal behaviors. In conclusion, we present a self-supervised learning approach to extract comprehensive and discriminative features directly from raw video recordings of animal behaviors and demonstrate its potential usage for various downstream applications.
Data availability
Major data used in this study were uploaded to Dryad, including pretrained weights. Data could be accessed via:https://doi.org/10.5061/dryad.brv15dvb8.With the uploaded dataset and pretrained weights, our experiments could be replicated. However, due to its huge size and the limited internet service resources, we are currently not able to share our full training dataset. The full dataset is as large as 400GB, which is hard to upload to a public server and will be difficult for others users to download.For training dataset, it would be available from the corresponding author upon reasonable request (wei_zhang@mail.tsinghua.edu.cn), and then we can discuss how to transfer the dataset. No project proposal is needed as long as the dataset is not used for any commercial purpose.Our Python scripts could be accessed on GitHub: https://github.com/EBGU/SelfeeOther software used in our project include ImageJ(https://imagej.net/software/fiji/) and GraphPad Prism(https://www.graphpad.com/).All data used to plot graphs and charts in the manuscript can be fully accessed on Dryad (DOI 10.5061/dryad.brv15dvb8).
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Data from: Selfee: Self-supervised features extraction of animal behaviorsDryad Digital Repository, doi:10.5061/dryad.brv15dvb8.
Article and author information
Author details
Funding
National Natural Science Foundation of China (32022029)
- Wei Zhang
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Marta Zlatic, MRC Laboratory of Molecular Biology, United Kingdom
Ethics
Animal experimentation: All mating experiments were approved by the Animal Care and Use Committee of the Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China (IACUC No. NA-016-2016)All studies and experimental protocols of CIS and OFT were approved by Institutional Animal Care and Use Committee (IACUC) at Tsinghua University (No. 19-ZY1). Experiments were performed using the principles outlined in the Guide for the Care and Use of Laboratory Animals of Tsinghua University.
Version history
- Received: December 8, 2021
- Preprint posted: December 24, 2021 (view preprint)
- Accepted: June 15, 2022
- Accepted Manuscript published: June 16, 2022 (version 1)
- Version of Record published: July 19, 2022 (version 2)
Copyright
© 2022, Jia et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Selfee, self-supervised features extraction of animal behaviors
eLife 11:e76218.
https://doi.org/10.7554/eLife.76218
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