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Review
. 2016 Apr;15(2):93-103.
doi: 10.1007/s12311-015-0685-5.

The Errors of Our Ways: Understanding Error Representations in Cerebellar-Dependent Motor Learning

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Free PMC article
Review

The Errors of Our Ways: Understanding Error Representations in Cerebellar-Dependent Motor Learning

Laurentiu S Popa et al. Cerebellum. .
Free PMC article

Abstract

The cerebellum is essential for error-driven motor learning and is strongly implicated in detecting and correcting for motor errors. Therefore, elucidating how motor errors are represented in the cerebellum is essential in understanding cerebellar function, in general, and its role in motor learning, in particular. This review examines how motor errors are encoded in the cerebellar cortex in the context of a forward internal model that generates predictions about the upcoming movement and drives learning and adaptation. In this framework, sensory prediction errors, defined as the discrepancy between the predicted consequences of motor commands and the sensory feedback, are crucial for both on-line movement control and motor learning. While many studies support the dominant view that motor errors are encoded in the complex spike discharge of Purkinje cells, others have failed to relate complex spike activity with errors. Given these limitations, we review recent findings in the monkey showing that complex spike modulation is not necessarily required for motor learning or for simple spike adaptation. Also, new results demonstrate that the simple spike discharge provides continuous error signals that both lead and lag the actual movements in time, suggesting errors are encoded as both an internal prediction of motor commands and the actual sensory feedback. These dual error representations have opposing effects on simple spike discharge, consistent with the signals needed to generate sensory prediction errors used to update a forward internal model.

Keywords: Cerebellum; Complex spikes; Internal model; Motor learning; Purkinje cells; Simple spikes.

Figures

Fig. 1
Fig. 1
Simple spike and complex spike modulation during adaptation to a mechanical perturbation. a Example of velocity encoding from a Purkinje cell. R2 profiles are plotted as functions of time between neural activity and behavior. At negative τ values (leads), neural activity leads behavior. At positive τ values (lags), neural activity lags behavior. This example demonstrates changes in timing with learning epochs, from significant simple spike velocity encoding at feedback times during baseline to feedforward only encoding in late adaptation. Graphs depict the four task epochs of baseline, early, middle, and late adaptation and number sign (#) indicates the timing of feedforward (gray) or feedback (black) signals. b Histograms of the dominant kinematic parameter (i.e., greatest R2 value) before and during adaptation to the perturbation. Early learning involves an increase in the position encoding that gradually decreases by late learning. Conversely, velocity encoding decreases in early learning and reverts in late learning. Asterisk (*) indicates significant difference (p<0.05). c Raster and average firing plots of the complex spike discharge of a single Purkinje cell throughout the adapt epoch. Complex spike discharge significantly increased around movement onset and after the perturbation. Significant change in complex spike firing compared to baseline is denoted by black and periods of no change by gray. Vertical gray line denotes movement onset and the time of the mechanical perturbation is denoted by the gray vertical bar. d Complex spike average firing from 49 Purkinje cells aligned on perturbation onset (PO). The overall weak effect of the perturbation on the complex spike firing can also be appreciated from the population averages. All data are from Hewitt et al. [128], with permission

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