Distilling the logic of behavioral dynamics using automated inference

Biological systems that produce stereotyped, reproducible dynamics are often still difficult to model because they are controlled by a large number of unknown heterogeneous interactions. Phenomenological coarse-grained fits can be useful as descriptors, but they are often unprincipled or not interpretable as dynamical systems. In contrast, recent innovations in statistical inference allow for the principled discovery of dynamical systems that reproduce given time series data, even when details about the underlying interaction structure are unknown. This allows for the prediction of responses to unseen dynamical stimuli, and more importantly provides a window into the phase space structure that defines the system's coarse-grained logic. We demonstrate this approach using data from the stereotyped movement of C. elegans in response to a heat stimulus. The resulting dynamical models predict the existence of distinct behavioral states that are not directly observed in the time series data.