Inferring the logic of biological dynamics
A simple worm has a predictable behavioral response to a heat stimulus; cortical neurons in a macaque brain have predictable dynamics that lead to decision states. Biological systems like these that produce stereotyped, reproducible dynamics are often still difficult to model because they are controlled by a large number of unknown heterogeneous interactions. 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. We use data from stereotyped movements of C. elegans and decision-making neurons in macaques to construct principled phenomenological dynamical models of each system. This allows for the prediction of responses to unseen dynamical stimuli and, using concepts from dynamical systems theory and statistical physics, provides a window into the phase space structure that defines each system's coarse-grained logic.