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Cerebellar granule cells encode the expectation of reward

Nature volume 544, pages 96100 (06 April 2017) | Download Citation

This article has been updated


The human brain contains approximately 60 billion cerebellar granule cells1, which outnumber all other brain neurons combined. Classical theories posit that a large, diverse population of granule cells allows for highly detailed representations of sensorimotor context, enabling downstream Purkinje cells to sense fine contextual changes2,3,4,5,6. Although evidence suggests a role for the cerebellum in cognition7,8,9,10, granule cells are known to encode only sensory11,12,13 and motor14 context. Here, using two-photon calcium imaging in behaving mice, we show that granule cells convey information about the expectation of reward. Mice initiated voluntary forelimb movements for delayed sugar-water reward. Some granule cells responded preferentially to reward or reward omission, whereas others selectively encoded reward anticipation. Reward responses were not restricted to forelimb movement, as a Pavlovian task evoked similar responses. Compared to predictable rewards, unexpected rewards elicited markedly different granule cell activity despite identical stimuli and licking responses. In both tasks, reward signals were widespread throughout multiple cerebellar lobules. Tracking the same granule cells over several days of learning revealed that cells with reward-anticipating responses emerged from those that responded at the start of learning to reward delivery, whereas reward-omission responses grew stronger as learning progressed. The discovery of predictive, non-sensorimotor encoding in granule cells is a major departure from the current understanding of these neurons and markedly enriches the contextual information available to postsynaptic Purkinje cells, with important implications for cognitive processing in the cerebellum.

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  • 05 April 2017

    Fig. 1b was corrected to remove an erroneous dashed line.


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We thank C. Kim for designing and assembling the capacitive lick sensor, L. Kitch for image processing code, J. Lecoq for microscope design, H. Zeng, E. Kim, E. Callaway and members of the Luo laboratory for reagents, mouse lines, and helpful discussions, and W. Newsome and J. Raymond for critical comments on the manuscript. M.J.W. was supported by Epilepsy Training Grant. M.J.S. and L.L. are HHMI investigators. This work was supported by NIH grants and Hughes Collaborative Innovation Award to L.L.

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Author notes

    • Mark J. Wagner
    •  & Tony Hyun Kim

    These authors contributed equally to this work.


  1. Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, California 94305, USA

    • Mark J. Wagner
    • , Tony Hyun Kim
    • , Joan Savall
    • , Mark J. Schnitzer
    •  & Liqun Luo
  2. Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA

    • Tony Hyun Kim
  3. Department of Applied Physics, Stanford University, Stanford, California 94305, USA

    • Mark J. Schnitzer


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M.J.W. designed and executed all experiments and analysed the data. T.H.K. contributed microscopy instrumentation as well as processing of brain imaging and behavioural videos. J.S. contributed to manipulandum design. M.J.S. provided imaging hardware, software, and expertise. L.L. supervised the project. M.J.W. and L.L. wrote the paper with contributions from all authors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Mark J. Wagner or Mark J. Schnitzer or Liqun Luo.

Reviewer Information Nature thanks C. De Zeeuw, T. Knopfel and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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  1. 1.

    Supplementary Information

    This file contains Supplementary Text 1-2, additional references and Supplementary Table 1.


  1. 1.

    Example two-photon Ca2+ imaging of cerebellar granule cells during a forelimb movement task

    The video is 4x temporally down-sampled from the 13.5-Hz acquisition rate.

  2. 2.

    Example body motion tracking during a forelimb movement task

    Side view was used to track right forepaw and base of tail motion while bottom view was used to track motion of each hind paw.

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