Inferring long timescale dynamics in animal behavior

Animal behavior consists of an intricate hierarchy of dynamics, from brief muscle twitches to stereotyped behaviors to longer-lived states like hunger, aggression, and parenting. How does an animal bridge these timescales to create complex sequences of actions? The approach that most researchers take when studying sequences of behaviors tends to be probabilistic, observing how discrete states transition in a largely memoryless fashion. In this talk, we take a different approach, fitting dynamical models to long behavioral sequences from fruit flies and rodents. We show that these models replicate many summary statistics of the underlying behavioral sequence data and that their fixed points have a geometry that mirrors the geometry of the animals' behavioral repertoires. In addition, we show that the long timescales generated by this model are best explained by a hierarchy of interacting dynamical subsystems that is comparable to the hierarchical structure of behavior. These results suggest a new framework for uncovering the hidden states that modulate the behaviors that an animal performs - predicting how physiology may be linked to behavior and how behaviors may evolve.