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Modulation of sensory prediction error in Purkinje cells during visual feedback manipulations

Author

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  • Martha L. Streng

    (University of Minnesota
    University of Minnesota)

  • Laurentiu S. Popa

    (University of Minnesota)

  • Timothy J. Ebner

    (University of Minnesota
    University of Minnesota)

Abstract

It is hypothesized that the cerebellum implements a forward internal model that transforms motor commands into predictions about upcoming movements. The predictions are compared with sensory feedback to generate sensory prediction errors critical to controlling movements. The simple spike firing of cerebellar Purkinje cells both lead and lag movement consistent with representations of motor predictions and sensory feedback. This study tests whether this leading and lagging modulation provides the prediction and sensory feedback necessary to compute sensory prediction errors. Two manipulations of the visual feedback are used in rhesus monkeys performing pseudo-random tracking. Consistent with a forward model, delaying the visual feedback demonstrates that the leading simple spike modulation with position error is time-locked to the hand movement. Reducing the feedback shows that the lagged modulation is directly driven by visual inputs. Therefore, Purkinje cell discharge carries both the motor predictions and sensory feedback required of a forward internal model.

Suggested Citation

  • Martha L. Streng & Laurentiu S. Popa & Timothy J. Ebner, 2018. "Modulation of sensory prediction error in Purkinje cells during visual feedback manipulations," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03541-0
    DOI: 10.1038/s41467-018-03541-0
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    Cited by:

    1. Ellen Boven & Joseph Pemberton & Paul Chadderton & Richard Apps & Rui Ponte Costa, 2023. "Cerebro-cerebellar networks facilitate learning through feedback decoupling," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Taisei Sugiyama & Nicolas Schweighofer & Jun Izawa, 2023. "Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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