DBIO Dissertation Award (2021): Physics of Behavior Across Scales

Animal movement exhibits multiple time scales: from fine scale posture movements, to changes in navigation strategies driven by internal states, all the way up to aging. These disparate scales are often studied separately: long time behaviors are typically described through body centroid kinematics, while finer-scale behaviors are studied at the level of posture. Here we introduce a unifying formalism that bridges between these scales. Starting from posture measurements, we reconstruct a state space by concatenating measurements in time, building a maximum entropy partition of the resulting sequences, and choosing the sequence length to maximize predictive information. Trading non-linear trajectories for linear, ensemble evolution, we analyze reconstructed dynamics through transfer operators. The eigenvalues of the inferred transfer operators reveal a hierarchy of time scales, while the respective eigenvectors capture metastable states that correspond to long-lived behavioral states. Leveraging this analysis, we uncover slowly changing navigation strategies and predict large scale diffusive properties of centroid trajectories directly from fine-scale posture measurements. Additionally, we introduce slow non-ergodic drives that reflect changing internal states, resulting in behavioral dynamics that evolve in potential landscapes that fluctuate in time. We explore the consequences of such a picture and recover a number of statistical properties observed across behaving organisms, such as long-range correlations, heavy-tailed dwell-time distributions and non-Markovianity.