Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 28, 2018
- Source ID
- 10.7554/elife.32605
Entities
People
- Christophe Dupre
- Ekaterina Taralova
- Rafael Yuste
- Shuting Han
Organizations
- Columbia University
- Defense Advanced Research Projects Agency
- Grass Foundation
- Howard Hughes Medical Institute