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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Article and author information
Author details
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
- Ronald L Calabrese, Emory University, United States
Version history
- Received: October 9, 2017
- Accepted: March 23, 2018
- Accepted Manuscript published: March 28, 2018 (version 1)
- 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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Comprehensive machine learning analysis of Hydra behavior reveals a stable behavioral repertoire
eLife 7:e32605.
https://doi.org/10.7554/eLife.32605
Further reading
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Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.
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Subpopulations of neurons in the subthalamic nucleus have distinct activity patterns that relate to the three hypotheses of the Drift Diffusion Model.
