  In motor neuroscience, artificial recurrent neural networks models often
complement animal studies. However, most modeling efforts are limited to
data-fitting, and the few that examine virtual embodied agents in a
reinforcement learning context, do not draw direct comparisons to their
biological counterparts. Our study addressing this gap, by uncovering
structured neural activity of a virtual robot performing legged locomotion that
directly support experimental findings of primate walking and cycling. We find
that embodied agents trained to walk exhibit smooth dynamics that avoid
tangling -- or opposing neural trajectories in neighboring neural space -- a
core principle in computational neuroscience. Specifically, across a wide suite
of gaits, the agent displays neural trajectories in the recurrent layers are
less tangled than those in the input-driven actuation layers. To better
interpret the neural separation of these elliptical-shaped trajectories, we
identify speed axes that maximizes variance of mean activity across different
forward, lateral, and rotational speed conditions.
