Phase oscillator model of inter-limb coordination in free-running mouse locomotion

While locomotion is a basic part of the behavioral repertoire of most animals, it is modulated via a complex consortium of control structures and pathways yet to be fully understood. Mouse studies have revealed important gait motifs and neural control mechanism but have been mostly constrained to a framework of discrete measurements and perturbative experiments. Recent innovations in machine learning and computer vision have allowed for the continuous tracking of body part trajectories in natural settings, which has opened up a new dimension of possibilities for the detailed study of animal behavior in high spatiotemporal resolution. We track mouse limb trajectories in spontaneous, unconstrained locomotion in open arenas and model the associated limb dynamics as a system of phase-coupled oscillators. This linearized Kuramoto description outputs not only traditional gait descriptors such as phase offset and speed, but also novel measures such as inter-limb coupling and underlying noise structures. Using these model parameters, we generate a description and comparison of inter-limb dynamics at various locomotion speeds and turn angles, and investigate the source of naturally occurring variations in locomotion.