A unified internal model theory to resolve the paradox of active versus passive self-motion sensation

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally-generated ( passive) movements. However, these neurons show reduced responses during self-generated ( active) movements, indicating that the brain computes the predicted sensory consequences of motor commands in order to cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations established during passive movements remain unknown. Here we construct a Kalman filter that incorporates motor commands into a previously-established model of optimal passive self-motion estimation. We find that the simulated sensory error and feedback signals match experimentally measured neuronal response during active and passive head and trunk rotations and translations. We conclude that a single internal model of head motion can process motor commands and sensory afferent signals optimally, and we describe how previously identified neural responses in the brainstem and cerebellum may represent distinct nodes in these computations.