Skip to main content
Log in

Cerebellar Representations of Errors and Internal Models

  • Mini-Review
  • Published:
The Cerebellum Aims and scope Submit manuscript

Abstract

After decades of study, a comprehensive understanding of cerebellar function remains elusive. Several hypotheses have been put forward over the years, including that the cerebellum functions as a forward internal model. Integrated into the forward model framework is the long-standing view that Purkinje cell complex spike discharge encodes error information. In this brief review, we address both of these concepts based on our recordings of cerebellar Purkinje cells over the last decade as well as newer findings from the literature. During a high-dimensionality tracking task requiring continuous error processing, we find that complex spike discharge provides a rich source of non-error signals to Purkinje cells, indicating that the classical error encoding role ascribed to climbing fiber input needs revision. Instead, the simple spike discharge of Purkinje cells carries robust predictive and feedback signals of performance errors, as well as kinematics. These simple spike signals are consistent with a forward internal model. We also show that the information encoded in the simple spike is dynamically adjusted by the complex spike firing. Synthesis of these observations leads to the hypothesis that complex spikes convey behavioral state changes, possibly acting to select and maintain forward models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Popa LS, Streng ML, Ebner TJ. Long-term predictive and feedback encoding of motor signals in the simple spike discharge of Purkinje cells. eNeuro. 2017;4(2):0036–17.

    Article  Google Scholar 

  2. Wolpert DM, Miall RC, Kawato M. Internal models in the cerebellum. Trends Cogn Sci. 1998;2:338–47.

    Article  CAS  PubMed  Google Scholar 

  3. Shadmehr R, Smith MA, Krakauer JW. Error correction, sensory prediction, and adaptation in motor control. Annu Rev Neurosci. 2010;33:89–108.

    Article  CAS  PubMed  Google Scholar 

  4. Pasalar S, Roitman AV, Durfee WK, Ebner TJ. Force field effects on cerebellar Purkinje cell discharge with implications for internal models. Nat Neurosci. 2006;9:1404–11.

    Article  CAS  PubMed  Google Scholar 

  5. Wolpert DM, Gthahramani Z. Computational principles of movement neuroscience. Nat Neurosci. 2000;3:1212–7.

    Article  CAS  PubMed  Google Scholar 

  6. Ito M. Historical review of the significance of the cerebellum and the role of Purkinje cells in motor learning. Ann N Y Acad Sci. 2002;978:273–88.

    Article  PubMed  Google Scholar 

  7. Gao Z, van Beugen BJ, De Zeeuw CI. Distributed synergistic plasticity and cerebellar learning. Nat Rev Neurosci. 2012;13(9):619–35.

    Article  CAS  PubMed  Google Scholar 

  8. Popa LS, Streng ML, Ebner TJ. The errors of our ways: understanding error representations in cerebellar-dependent motor learning. Cerebellum. 2016;15(2):93–103.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Llinas RR. The olivo-cerebellar system: a key to understanding the functional significance of intrinsic oscillatory brain properties. Front Neural Circuits. 2013;7:96.

    PubMed  Google Scholar 

  10. Catz N, Dicke PW, Their P. Cerebellar complex spike firing is suitable to induce as well as to stabilize motor learning. Curr Biol. 2005;15(24):2179–89.

    Article  CAS  PubMed  Google Scholar 

  11. Streng ML, Popa LS, Ebner TJ. Complex spike wars: a new hope. Cerebellum. 2018;17(6):735–46.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ke MC, Guo CC, Raymond JL. Elimination of climbing fiber instructive signals during motor learning. Nat Neurosci. 2009;12(9):1171–9.

    Article  CAS  PubMed  Google Scholar 

  13. Shin SL, Zhao GQ, Raymond JL. Signals and learning rules guiding oculomotor plasticity. J Neurosci. 2014;34(32):10635–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nguyen-Vu TD, Kimpo RR, Rinaldi JM, Kohli A, Zeng H, Deisseroth K, Raymond JL. Cerebellar Purkinje cell activity drives motor learning. Nat Neurosci. 2013;16(12):1734–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hewitt AL, Popa LS, Ebner TJ. Changes in Purkinje cell simple spike encoding of reach kinematics during adaptation to a mechanical perturbation. J Neurosci. 2015;35(3):1106–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Streng ML, Popa LS, Ebner TJ. Climbing fibers predict movement kinematics and performance errors. J Neurophysiol. 2017;118(3):1888–902.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Frens MA, Mathoera AL, van der Steen SJ. Floccular complex spike response to transparent retinal slip. Neuron. 2001;30(3):795–801.

    Article  CAS  PubMed  Google Scholar 

  18. Ohmae S, Medina JF. Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice. Nat Neurosci. 2015;18(12):1798–803.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. ten Brinke MM, Boele HJ, Spanke JK, Potters JW, Kornysheva K, Wulff P, Ijpelaar ACHG, Koekkoek SKE, De Zeeuw CI. Evolving models of Pavlovian conditioning: cerebellar cortical dynamics in awake behaving mice. Cell Rep. 2015;13(9):1977–88.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Heffley W, Hull C. Coordinated cerebellar climbing fiber activity signals learned sensorimotor predictions. Nat Neurosci. 2018;21(10):1431–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Heffley W, Hull C. Classical conditioning drives learned reward prediction signals in climbing fibers across the lateral cerebellum. Elife. 2019;11:8e46764.

    Google Scholar 

  22. Kostadinov D, Beau M, Blanco-Pozo M, Hausser M. Predictive and reactive reward signals conveyed by climbing fiber inputs to cerebellar Purkinje cells. Nat Neurosci. 2019;22(6):950–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wagner MJ, Luo L. Neocortex-cerebellum circuits for cognitive processing. Trends in Neuroscience. 2020;43(1):42–54.

    Article  CAS  Google Scholar 

  24. Bina L, Romano V, Hoogland TM, Bosman LWJ, De Zeeuw CI. Purkinje cells translate subjective salience into readiness to act and choice performance. Cell Rep. 2021;37(11):110116.

    Article  CAS  PubMed  Google Scholar 

  25. De Zeeuw CI, Lisberger SG, Raymond JL. Diversity and dynamism in the cerebellum. Nat Neurosci. 2021;24(2):160–7.

    Article  PubMed  CAS  Google Scholar 

  26. Colin F, Manil J, Desclin JC. The olivocerebellar system. I. Delayed and slow inhibitory effects: an overlooked salient feature of cerebellar climbing fibers. Brain Res. 1980;187(1):3–27.

    Article  CAS  PubMed  Google Scholar 

  27. Llinas R. Inferior olive: its role in motor learing. Science. 1975;190(4220):1230–1.

    Article  CAS  PubMed  Google Scholar 

  28. Welsh JP, Lang EJ, Suglhara I, Llinas R. Dynamic organization of motor control within the olivocerebellar system. Nature. 1995;374(6521):453–7.

    Article  CAS  PubMed  Google Scholar 

  29. Kitamura K, Kano M. Dendritic calcium signaling in cerebellar Purkinje cell. Neural Netw. 2013;47:11–7.

    Article  PubMed  Google Scholar 

  30. Streng ML, Popa LS, Ebner TJ. Climbing fibers control Purkinje cell representations of behavior. J Neurosci. 2017;37(8):1997–2009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Popa LS, Aronson JD, Ebner TJ. States are A-changing, complex spikes proclaim. In: Mizusawa H, Kakei S. (eds) Cerebellum as a CNS Hub. Contemporary Clinical Neuroscience. Springer, Cham. 2021;259–275.

  32. Popa LS, Streng ML, Ebner TJ. Purkinje cell representations of behavior: diary of a busy neuron. Neuroscientist. 2019;25(3):241–57.

    Article  PubMed  Google Scholar 

  33. Greger B, Norris N. Simple spike firing in the posterior lateral cerebellar cortex of Macaque Mulatta was correlated with success-failure during a visually guided reaching task. Exp Brain Res. 2005;167(4):660–5.

    Article  PubMed  Google Scholar 

  34. Roitman AV, Pasalar S, Ebner TJ. Single trial coupling of Purkinje cell activity to speed and error signals during circular manual tracking. Exp Brain Res. 2009;192(2):241–51.

    Article  CAS  PubMed  Google Scholar 

  35. Popa LS, Hewitt AL, Ebner TJ. Predictive and feedback performance errors are signaled in the simple spike discharge of individual Purkinje cells. J Neurosci. 2012;32(44):15345–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Streng ML, Popa LS, Ebner TJ. Modulation of sensory prediction error in Purkinje cells during visual feedback manipulations. Nat Commun. 2018;9(1):1099.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Sendhilnathan N, Semework M, Golbert ME, Ipata AE. Neural correlates of reinforcement learning in mid-lateral cerebellum. Neuron. 2020;106(1):188-198e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bastian AJ. Learning to predict the future: the cerebellum adapts feedforward movement control. Curr Opin Neurobiol. 2006;16(6):645–9.

    Article  CAS  PubMed  Google Scholar 

  39. Lisberger SG. Internal models of eye movement in the floccular complex of the monkey cerebellum. Neuroscience. 2009;162(3):763–76.

    Article  CAS  PubMed  Google Scholar 

  40. Kim G, Laurens J, Yakusheva TA, Blazquez PM. The macaque cerebellar flocculus outputs a forward model of eye movement. Front Integr Neurosci. 2019;13:12.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Markov DA, Petrucco L, Kist AM, Portugues R. A cerebellar internal model calibrates a feedback controller involved in sensorimotor control. Nat Commun. 2021;12(1):6694.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Laurens J, Angelaki DE. Simple spike dynamics of Purkinje cells in the macaque vestibulo-cerebellum during passive whole-body self-motion. Proc Natl Acad Sci. 2020;117(6):3232–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Brooks JX, Cullen KE. The primate cerebellum selectively encodes unexpected self-motion. Curr Biol. 2013;23(11):947–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mackrous I, Carriot J, Jamali M, Cullen KE. Cerebellar prediction of the dynamic sensory consequences of gravity. Curr Biol. 2019;29(16):2698-2710e4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Moberget T, Ivry RB. Cerebellar contributions to motor control and language comprehension: searching for common computational principles. Ann N Y Acad Sci. 2016;1369(1):154–71.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Pleger B, Timmann D. The role of the human cerebellum in linguistic prediction, word generation and verbal working memory: evidence from brain imaging, non-invasive cerebellar stimulation and lesion studies. Neuropsychologia. 2018;115:204–10.

    Article  PubMed  Google Scholar 

  47. Tanaka H, Ishikawa T, Lee J, Kakei S. The cerebro-cerebellum as a locus of forward model: a review. Front Syst Neurosci. 2020;14:19.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Pickering MJ, Clark A. Getting ahead: forward models and their place in cognitive architecture. Trends Cogn Sci. 2014;18(9):451–6.

    Article  PubMed  Google Scholar 

  49. Uusisaari MY, Knopfel T. Diversity of neuronal elements and circuitry in the cerebellar nuclei. Cerebellum. 2012;11(2):420–1.

    Article  PubMed  Google Scholar 

  50. Judd EN, Lewis SM, Person AL. Diverse inhibitory projections from the cerebellar interposed nucleus. Elife. 2021;20:10e66231.

    Google Scholar 

  51. Fujita H, Kodama T, du Lac S. Modular output circuits of the fastigial nucleus for diverse motor and nonmotor functions of the cerebellar vermis. ELife. 2020;8:9e58613.

    Google Scholar 

  52. Streng ML, Tetzlaff MR, Krook-Magnuson E. Distinct fastigial output channels and their impact on temporal lobe seizures. J Neurosci. 2021;41(49):10091–107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Musall S, Kaufman MT, Juavinett AL, Gluf S, Churchland AK. Single-trial neural dynamics are dominated by richly varied movements. Nat Neurosci. 2019;22(10):1677–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ren C, Komiyama T. Characterizing cortex-wide dynamics with wide-field calcium imaging. J Neurosci. 2021;41(19):4160–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. West SL, Aronson JD, Popa LS, Feller KD, Carter RE, Chiesl WM, Gerhart ML, Shekhar AC, Ghanbari L, Kodandaramaiah SB, Ebner TJ. Wide-field calcium imaging of dynamic cortical networks during locomotion. Cereb Cortex. 2021;bhab373

Download references

Acknowledgements

We would like to thank Kathleen Beterams for her help with the manuscript. Supported in part by NIH grant R01 NS18338.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Timothy J. Ebner.

Ethics declarations

Conflict of Interest Statement

The authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Streng, M.L., Popa, L.S. & Ebner, T.J. Cerebellar Representations of Errors and Internal Models. Cerebellum 21, 814–820 (2022). https://doi.org/10.1007/s12311-022-01406-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12311-022-01406-3

Keywords

Navigation