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.
- Rafael Yuste
- Shuting Han
- Christophe Dupre
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Ronald L Calabrese, Emory University, United States
© 2018, Han et al.
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