A Markovian dynamics for C. elegans behavior across scales

TL;DR

This paper develops a unified Markovian framework to model C. elegans behavior across multiple scales, linking microstate dynamics to movement patterns and revealing new behavioral insights.

Contribution

It introduces a maximum entropy partition of posture sequences to construct a high-fidelity Markov model for multi-scale behavioral analysis.

Findings

Identifies a Markovian structure connecting posture fluctuations to movement.

Reveals both known and new behavioral states through eigenvector analysis.

Shows a trade-off between local and global search strategies in foraging.

Abstract

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm $C. elegans$, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion, and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.