Capturing the continuous complexity of behaviour in Caenorhabditis elegans

Tosif Ahamed, Antonio C. Costa, Greg J. Stephens*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

Animal behaviour is often quantified through subjective, incomplete variables that mask essential dynamics. Here, we develop a maximally predictive behavioural-state space from multivariate measurements, in which the full instantaneous state is smoothly unfolded as a combination of short-time posture sequences. In the off-food behaviour of the roundworm Caenorhabditis elegans, we discover a low-dimensional state space dominated by three sets of cyclic trajectories corresponding to the worm’s basic stereotyped motifs: forward, backward and turning locomotion. We find similar results in the on-food behaviour of foraging worms and npr-1 mutants. In contrast to this broad stereotypy, we find variability in the presence of locally unstable dynamics with signatures of deterministic chaos: a collection of unstable periodic orbits together with a positive maximal Lyapunov exponent. The full Lyapunov spectrum is symmetric with positive, chaotic exponents driving variability balanced by negative, dissipative exponents driving stereotypy. The symmetry is indicative of damped–driven Hamiltonian dynamics underlying the worm’s movement control.

Original languageEnglish
Pages (from-to)275-283
Number of pages9
JournalNature Physics
Volume17
Issue number2
Early online date5 Oct 2020
DOIs
Publication statusPublished - Feb 2021

Bibliographical note

Funding Information:
We thank D. Jordan, I. Etheredge and A. Celani for comments. L. Hebert (OIST Graduate University) developed the custom machine-learning solution for pose estimation of worms in on-food conditions. We would also like to express our gratitude to I. Maruyama for his support during the project. This work was supported by OIST Graduate University (T.A., G.J.S.), a programme grant from the Netherlands Organization for Scientific Research (A.C.C., G.J.S.), Vrije Universiteit Amsterdam (G.J.S.) and the Japan Society for the Promotion of Science (T.A.).

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

We thank D. Jordan, I. Etheredge and A. Celani for comments. L. Hebert (OIST Graduate University) developed the custom machine-learning solution for pose estimation of worms in on-food conditions. We would also like to express our gratitude to I. Maruyama for his support during the project. This work was supported by OIST Graduate University (T.A., G.J.S.), a programme grant from the Netherlands Organization for Scientific Research (A.C.C., G.J.S.), Vrije Universiteit Amsterdam (G.J.S.) and the Japan Society for the Promotion of Science (T.A.).

FundersFunder number
OIST Graduate University
Japan Society for the Promotion of Science
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

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