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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- Neuroscience
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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- Neuroscience
Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.
