Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion

View ORCID ProfileKevin Luxem, View ORCID ProfilePetra Mocellin, Falko Fuhrmann, Johannes Kürsch, Stefan Remy, View ORCID ProfilePavol Bauer
doi: https://doi.org/10.1101/2020.05.14.095430
Kevin Luxem
1Leibniz Institute for Neurobiology, Department of Cellular Neuroscience, Magdeburg, Germany
2German Center for Neurodegenerative Diseases, Magdeburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kevin Luxem
Petra Mocellin
1Leibniz Institute for Neurobiology, Department of Cellular Neuroscience, Magdeburg, Germany
2German Center for Neurodegenerative Diseases, Magdeburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Petra Mocellin
Falko Fuhrmann
1Leibniz Institute for Neurobiology, Department of Cellular Neuroscience, Magdeburg, Germany
2German Center for Neurodegenerative Diseases, Magdeburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Johannes Kürsch
1Leibniz Institute for Neurobiology, Department of Cellular Neuroscience, Magdeburg, Germany
2German Center for Neurodegenerative Diseases, Magdeburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stefan Remy
1Leibniz Institute for Neurobiology, Department of Cellular Neuroscience, Magdeburg, Germany
2German Center for Neurodegenerative Diseases, Magdeburg, Germany
3Center for Behavioral Brain Sciences, Magdeburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: stefan.remy@lin-magdeburg.de
Pavol Bauer
1Leibniz Institute for Neurobiology, Department of Cellular Neuroscience, Magdeburg, Germany
2German Center for Neurodegenerative Diseases, Magdeburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pavol Bauer
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Quantification and detection of the hierarchical organization of behavior is a major challenge in neuroscience. Recent advances in markerless pose estimation enable the visualization of highdimensional spatiotemporal behavioral dynamics of animal motion. However, robust and reliable technical approaches are needed to uncover underlying structure in these data and to segment behavior into discrete hierarchically organized motifs. Here, we present an unsupervised probabilistic deep learning framework that identifies behavioral structure from deep variational embeddings of animal motion (VAME). By using a mouse model of beta amyloidosis as a use case, we show that VAME not only identifies discrete behavioral motifs, but also captures a hierarchical representation of the motif’s usage. The approach allows for the grouping of motifs into communities and the detection of differences in community-specific motif usage of individual mouse cohorts that were undetectable by human visual observation. Thus, we present a novel and robust approach for quantification of animal motion that is applicable to a wide range of experimental setups, models and conditions without requiring supervised or a-priori human interference.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵4 These authors jointly supervised this work

  • Restructuring, all figures and tables updated, new supplementary figures

  • https://github.com/LINCellularNeuroscience/VAME/

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.
Back to top
PreviousNext
Posted January 14, 2022.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion
Kevin Luxem, Petra Mocellin, Falko Fuhrmann, Johannes Kürsch, Stefan Remy, Pavol Bauer
bioRxiv 2020.05.14.095430; doi: https://doi.org/10.1101/2020.05.14.095430
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion
Kevin Luxem, Petra Mocellin, Falko Fuhrmann, Johannes Kürsch, Stefan Remy, Pavol Bauer
bioRxiv 2020.05.14.095430; doi: https://doi.org/10.1101/2020.05.14.095430

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4663)
  • Biochemistry (10320)
  • Bioengineering (7647)
  • Bioinformatics (26266)
  • Biophysics (13486)
  • Cancer Biology (10655)
  • Cell Biology (15372)
  • Clinical Trials (138)
  • Developmental Biology (8473)
  • Ecology (12787)
  • Epidemiology (2067)
  • Evolutionary Biology (16806)
  • Genetics (11374)
  • Genomics (15438)
  • Immunology (10586)
  • Microbiology (25099)
  • Molecular Biology (10176)
  • Neuroscience (54271)
  • Paleontology (399)
  • Pathology (1663)
  • Pharmacology and Toxicology (2884)
  • Physiology (4329)
  • Plant Biology (9216)
  • Scientific Communication and Education (1583)
  • Synthetic Biology (2547)
  • Systems Biology (6765)
  • Zoology (1459)