Learning Task-Agnostic Motifs to Capture the Continuous Nature of Animal Behavior

TL;DR

This paper introduces a novel framework called motif-based continuous dynamics (MCD) that models animal behavior as a continuous mixture of interpretable motor motifs, capturing the fluid nature of behavior better than traditional discrete segmentation methods.

Contribution

The paper presents MCD, a new approach that uncovers latent motif sets and models behavior as evolving mixtures, improving the understanding of continuous animal behavior dynamics.

Findings

MCD identifies reusable motif components across tasks.

It captures continuous compositional dynamics of behavior.

Generates realistic trajectories surpassing traditional models.

Abstract

Animals flexibly recombine a finite set of core motor motifs to meet diverse task demands, but existing behavior segmentation methods oversimplify this process by imposing discrete syllables under restrictive generative assumptions. To better capture the continuous structure of behavior generation, we introduce motif-based continuous dynamics (MCD) discovery, a framework that (1) uncovers interpretable motif sets as latent basis functions of behavior by leveraging representations of behavioral transition structure, and (2) models behavioral dynamics as continuously evolving mixtures of these motifs. We validate MCD on a multi-task gridworld, a labyrinth navigation task, and freely moving animal behavior. Across settings, it identifies reusable motif components, captures continuous compositional dynamics, and generates realistic trajectories beyond the capabilities of traditional discrete segmentation models. By providing a generative account of how complex animal behaviors emerge from dynamic combinations of fundamental motor motifs, our approach advances the quantitative study of natural behavior.