Comprehensive machine learning analysis of Hydra behavior reveals a stable behavioral repertoire

  1. Shuting Han  Is a corresponding author
  2. Ekaterina Taralova
  3. Christophe Dupre
  4. Rafael Yuste  Is a corresponding author
  1. Columbia University, United States

Abstract

Animal behavior has been studied for centuries, but few efficient methods are available to automatically identify and classify behavior. Quantitative behavioral studies have been hindered by the subjective and imprecise nature of human observation, the limitation of human vision and the slow speed of annotating behavioral data. Here we developed an automatic behavior analysis pipeline for the cnidarian Hydra vulgaris using machine learning approaches. We imaged freely behaving Hydra, extracted motion and shape features from the videos, and constructed a dictionary of visual features to classify pre-defined behaviors. We also identified unannotated behaviors with unsupervised methods. Using this analysis pipeline, we found surprisingly similar behavior statistics across animals within the same species, regardless of experimental conditions. Our analysis indicates that the behavioral repertoire of Hydra is stable. This robustness could reflect a homeostatic neural control which could have been already present in the earliest nervous systems.

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The following data sets were generated

Article and author information

Author details

  1. Shuting Han

    Department of Biological Sciences, Columbia University, New York, United States
    For correspondence
    sh3276@columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. Ekaterina Taralova

    Department of Biological Sciences, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Christophe Dupre

    Department of Biological Sciences, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5929-8492
  4. Rafael Yuste

    Department of Biological Sciences, Columbia University, New York, United States
    For correspondence
    rmy5@columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4206-497X

Funding

Defense Advanced Research Projects Agency (HR0011-17-C-0026)

  • Rafael Yuste

Howard Hughes Medical Institute (Howard Hughes Medical Institute International Student Research Fellowship)

  • Shuting Han

Grass Foundation (Grass Fellowship)

  • Christophe Dupre

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Ronald L Calabrese, Emory University, United States

Version history

  1. Received: October 9, 2017
  2. Accepted: March 23, 2018
  3. Accepted Manuscript published: March 28, 2018 (version 1)
  4. Version of Record published: April 27, 2018 (version 2)

Copyright

© 2018, Han 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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  1. Shuting Han
  2. Ekaterina Taralova
  3. Christophe Dupre
  4. Rafael Yuste
(2018)
Comprehensive machine learning analysis of Hydra behavior reveals a stable behavioral repertoire
eLife 7:e32605.
https://doi.org/10.7554/eLife.32605

Share this article

https://doi.org/10.7554/eLife.32605

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