From Data-Fitting to Discovery: Interpreting the Neural Dynamics of Motor Control through Reinforcement Learning
This work addresses the problem of interpreting neural dynamics in motor control for computational neuroscientists, though it is incremental in directly comparing virtual agents to biological data.
The study tackled the gap between data-fitting models and biological comparisons in motor neuroscience by training a virtual robot to walk, revealing that its recurrent neural layers exhibit less tangled neural trajectories than input-driven layers, supporting experimental findings in primates.
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.