Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire.

Shuting Han,Christophe Dupre,Rafael Yuste,Ekaterina Taralova

doi:10.7554/elife.32605

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.

Highlights

Animal behavior is generally characterized by an enormous variability in posture and the motion of different body parts, even if many complex behaviors can be reduced to sequences of simple stereotypical movements (Berman et al, 2014; Branson et al, 2009; Gallagher et al, 2013; Srivastava et al, 2009; Wiltschko et al, 2015; Yamamoto and Koganezawa, 2013)
Much remains unknown about how the specific spatiotemporal pattern of activity of the nervous systems integrate external sensory inputs and internal neural network states in order to selectively generate different behavior
Advances in calcium imaging techniques have enabled the recording of the activity of large neural populations (Chen et al, 2013; Jin et al, 2012; Kralj et al, 2011; St-Pierre et al, 2014; Tian et al, 2009; Yuste and Katz, 1991), including whole brain activity from small organisms such as C. elegans and larval zebrafish (Ahrens et al, 2013; Nguyen et al, 2016; Prevedel et al, 2014)

Summary

Introduction

Animal behavior is generally characterized by an enormous variability in posture and the motion of different body parts, even if many complex behaviors can be reduced to sequences of simple stereotypical movements (Berman et al, 2014; Branson et al, 2009; Gallagher et al, 2013; Srivastava et al, 2009; Wiltschko et al, 2015; Yamamoto and Koganezawa, 2013). As a way to systematic capture this variability and compositionality, quantitative behavior recognition and measurement methods could provide an important tool for investigating behavioral differences under various conditions using large datasets, allowing for the discovery of behavior features that are beyond the capability of human inspection, and defining a uniform standard for describing behaviors across conditions (Egnor and Branson, 2016). Automatic methods to measure and classify behavior quantitatively could allow researchers to indetify potential neural mechanisms by providing a standard measurement of the behavioral output of the nervous system. A recent study has demonstrated the cnidarian Hydra can be used as an alternative model to image the complete neural activity during behavior (Dupre and Yuste, 2017).

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Conclusion