Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion

Abstract

There is considerable evidence that the cerebellum plays a vital role in motor learning by constructing an estimate of the sensory consequences of movement. Theory suggests this estimate is compared with the actual sensory feedback to drive motor learning. However, direct proof for the existence of this comparison is still lacking. Here we carried out a trial-by-trial analysis of cerebellar neurons during the execution and adaptation of voluntary head movements, and found that neuronal sensitivities dynamically track the comparison of predictive and feedback signals. When the relationship between the motor command and resultant movement was altered, neurons robustly responded to sensory input as if the movement was externally-generated. Neuronal sensitivities then declined with the same time course as the concurrent behavioral learning. These findings demonstrate the output of an elegant computation in which rapid updating of an internal model enables the motor system to learn to expect unexpected sensory inputs.

A comparative approach to cerebellar function: insights from electrosensory systems

“…Vestibular afferents respond identically in the two conditions, suggesting that self-generated vestibular signals are cancelled centrally. Recently, Brooks et al took these findings a step further by devising a motor adaptation paradigm that made it possible to track the cancellation of self-generated vestibular inputs in individual cerebellar neurons in real-time[64]. Initially neurons showed no response to self-generated head movements, consistent with previous findings.…”

Individual Differences in Motor Noise and Adaptation Rate Are Optimally Related

“…When Purkinje cell predictions of the upcoming kinematic properties are inaccurate, activity of neurons in the cerebellar nuclei is proportional to the prediction error. This is apparently because inhibitory Purkinje cell input cannot cancel the excitatory input from mossy fibers and the inferior olive (Brooks et al, 2015). The sensory prediction error calculated by the cerebellar nuclei could be used to update either (1) motor commands in a feedback loop with (pre)motor areas (Kelly and Strick, 2003) or (2) state estimates of the limb in the parietal cortex (Grafton et al, 1999; Clower et al, 2001).…”

The Cerebellum: Adaptive Prediction for Movement and Cognition

“…LTD modifies signal processing in the cerebellar cortex, sculpting the powerful, converging inhibition by Purkinje cells on the deep cerebellar nuclear cells. Hence, the Purkinje cell simple spike activity could be viewed as constituting the sensory prediction [45, 46, 63] (although it may also contribute to the motor command), while the climbing fiber activity resulting in Purkinje cell complex spikes provides a representation of sensory prediction errors [64–66], driving cerebellar learning. It should be noted that, although this model has been highly influential in theories of cerebellar-based learning, there remains considerable debate over the functional role of the climbing fiber signals and their interaction with simple spike activity [50, 64, 67].…”