Background: The ability to distinguish sensory signals that register unexpected events (exafference) from those generated by voluntary actions (reafference) during self-motion is essential for accurate perception and behavior. The cerebellum is most commonly considered in relation to its contributions to the fine tuning of motor commands and sensorimotor calibration required for motor learning. During unexpected motion, however, the sensory prediction errors that drive motor learning potentially provide a neural basis for the computation underlying the distinction between reafference and exafference.
Results: Recording from monkeys during voluntary and applied self-motion, we demonstrate that individual cerebellar output neurons encode an explicit and selective representation of unexpected self-motion by means of an elegant computation that cancels the reafferent sensory effects of self-generated movements. During voluntary self-motion, the sensory responses of neurons that robustly encode unexpected movement are canceled. Neurons with vestibular and proprioceptive responses to applied head and body movements are unresponsive when the same motion is self-generated. When sensory reafference and exafference are experienced simultaneously, individual neurons provide a precise estimate of the detailed time course of exafference.
Conclusions: These results provide an explicit solution to the longstanding problem of understanding mechanisms by which the brain anticipates the sensory consequences of our voluntary actions. Specifically, by revealing a striking computation of a sensory prediction error signal that effectively distinguishes between the sensory consequences of self-generated and externally produced actions, our findings overturn the conventional thinking that the sensory errors coded by the cerebellum principally contribute to the fine tuning of motor activity required for motor learning.
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