Comprehensive machine learning analysis of Hydra behavior reveals a stable behavioral repertoire
- 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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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.
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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Comprehensive machine learning analysis of Hydra behavior reveals a stable behavioral repertoire
eLife 7:e32605.
https://doi.org/10.7554/eLife.32605
