Capturing the continuous complexity of behaviour inCaenorhabditis elegans

Animal behaviour is characterized by repeated movements which can be difficult to analyse quantitatively. Here, the authors apply a data-driven framework based on theory of dynamical systems to characterize nematode behaviour and explain its complexity through deterministic chaotic dynamics. 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 roundwormCaenorhabditis 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 andnpr-1mutants. 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.

Automated summary

The authors use a data-driven framework based on the theory of dynamical systems to characterize nematode behavior and explain its complexity through deterministic chaotic dynamics. They find both basic stereotyped movements and locally unstable dynamics with signatures of deterministic chaos in the behavior of the roundworm. The presence of positive Lyapunov exponents and a symmetric Lyapunov spectrum suggests that the worm's movement control is driven by damped-driven Hamiltonian dynamics.