  Parallel developments in neuroscience and deep learning have led to mutually
productive exchanges, pushing our understanding of real and artificial neural
networks in sensory and cognitive systems. However, this interaction between
fields is less developed in the study of motor control. In this work, we
develop a virtual rodent as a platform for the grounded study of motor activity
in artificial models of embodied control. We then use this platform to study
motor activity across contexts by training a model to solve four complex tasks.
Using methods familiar to neuroscientists, we describe the behavioral
representations and algorithms employed by different layers of the network
using a neuroethological approach to characterize motor activity relative to
the rodent's behavior and goals. We find that the model uses two classes of
representations which respectively encode the task-specific behavioral
strategies and task-invariant behavioral kinematics. These representations are
reflected in the sequential activity and population dynamics of neural
subpopulations. Overall, the virtual rodent facilitates grounded collaborations
between deep reinforcement learning and motor neuroscience.
