Data from: Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire

Animal behavior has been studied for centuries, but few efficient methods
are available to automatically identify and classify it. Quantitative
behavioral studies have been hindered by the subjective and imprecise
nature of human observation, and the slow speed of annotating behavioral
data. Here, we developed an automatic behavior analysis pipeline for the
cnidarian Hydra vulgaris using machine learning. 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 quantified 6 basic behaviors and
found surprisingly similar behavior statistics across animals within the
same species, regardless of experimental conditions. Our analysis
indicates that the fundamental behavioral repertoire of Hydra is stable.
This robustness could reflect a homeostatic neural control of
"housekeeping" behaviors which could have been already present
in the earliest nervous systems.