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

Nature Neuroscience volume 18, pages 1310–1317 (2015)Cite this article

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Abstract

There is considerable evidence that the cerebellum has a vital role in motor learning by constructing an estimate of the sensory consequences of movement. Theory suggests that this estimate is compared with the actual feedback to compute the sensory prediction error. However, direct proof for the existence of this comparison is lacking. 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 tracked 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.

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Figure 1: Experimental design.
Figure 2: Learning procedure.
Figure 3: Average head velocity and sensitivity for our population of rFN neurons (n = 21) during the learning phase.
Figure 4: Extinction phase.
Figure 5: Average head velocity and sensitivity for our population of rFN neurons (n = 21) during the extinction phase.
Figure 6: Activity of an example neuron recorded in the VN.
Figure 7: Average head velocity and neuronal sensitivity for our population of neurons recorded in the VN (n = 20).

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References

  1. Bhushan, N. & Shadmehr, R. Computational nature of human adaptive control during learning of reaching movements in force fields. Biol. Cybern. 81, 39–60 (1999).

    CAS  PubMed  Google Scholar 

  2. Kawato, M. Internal models for motor control and trajectory planning. Curr. Opin. Neurobiol. 9, 718–727 (1999).

    CAS  PubMed  Google Scholar 

  3. Shadmehr, R., Smith, M.A. & Krakauer, J.W. Error correction, sensory prediction and adaptation in motor control. Annu. Rev. Neurosci. 33, 89–108 (2010).

    CAS  PubMed  Google Scholar 

  4. Wolpert, D.M. & Ghahramani, Z. Computational principles of movement neuroscience. Nat. Neurosci. 3 (suppl.) 1212–1217 (2000).

    CAS  PubMed  Google Scholar 

  5. Wolpert, D.M. & Miall, R.C. Forward models for physiological motor control. Neural Netw. 9, 1265–1279 (1996).

    PubMed  Google Scholar 

  6. Berniker, M. & Kording, K. Estimating the sources of motor errors for adaptation and generalization. Nat. Neurosci. 11, 1454–1461 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Todorov, E. & Jordan, M.I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002).

    CAS  PubMed  Google Scholar 

  8. Mazzoni, P. & Krakauer, J.W. An implicit plan overrides an explicit strategy during visuomotor adaptation. J. Neurosci. 26, 3642–3645 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Wong, A.L. & Shelhamer, M. Sensorimotor adaptation error signals are derived from realistic predictions of movement outcomes. J. Neurophysiol. 105, 1130–1140 (2011).

    PubMed  Google Scholar 

  10. Ebner, T.J., Hewitt, A.L. & Popa, L.S. What features of limb movements are encoded in the discharge of cerebellar neurons? Cerebellum 10, 683–693 (2011).

    PubMed  PubMed Central  Google Scholar 

  11. Medina, J.F. The multiple roles of Purkinje cells in sensori-motor calibration: to predict, teach and command. Curr. Opin. Neurobiol. 21, 616–622 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Pasalar, S., Roitman, A.V., Durfee, W.K. & Ebner, T.J. Force field effects on cerebellar Purkinje cell discharge with implications for internal models. Nat. Neurosci. 9, 1404–1411 (2006).

    CAS  PubMed  Google Scholar 

  13. Popa, L.S., Hewitt, A.L. & Ebner, T.J. Predictive and feedback performance errors are signaled in the simple spike discharge of individual Purkinje cells. J. Neurosci. 32, 15345–15358 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Krakauer, J.W. & Mazzoni, P. Human sensorimotor learning: adaptation, skill and beyond. Curr. Opin. Neurobiol. 21, 636–644 (2011).

    CAS  PubMed  Google Scholar 

  15. Wolpert, D.M., Goodbody, S.J. & Husain, M. Maintaining internal representations: the role of the human superior parietal lobe. Nat. Neurosci. 1, 529–533 (1998).

    CAS  PubMed  Google Scholar 

  16. Bastian, A.J. Learning to predict the future: the cerebellum adapts feedforward movement control. Curr. Opin. Neurobiol. 16, 645–649 (2006).

    CAS  PubMed  Google Scholar 

  17. Taylor, J.A., Klemfuss, N.M. & Ivry, R.B. An explicit strategy prevails when the cerebellum fails to compute movement errors. Cerebellum 9, 580–586 (2010).

    PubMed  PubMed Central  Google Scholar 

  18. Tseng, Y.W., Diedrichsen, J., Krakauer, J.W., Shadmehr, R. & Bastian, A.J. Sensory prediction errors drive cerebellum-dependent adaptation of reaching. J. Neurophysiol. 98, 54–62 (2007).

    PubMed  Google Scholar 

  19. Popa, L.S., Hewitt, A.L. & Ebner, T.J. Purkinje cell simple spike discharge encodes error signals consistent with a forward internal model. Cerebellum 12, 331–333 (2013).

    PubMed  PubMed Central  Google Scholar 

  20. Batton, R.R. III, Jayaraman, A., Ruggiero, D. & Carpenter, M.B. Fastigial efferent projections in the monkey: an autoradiographic study. J. Comp. Neurol. 174, 281–305 (1977).

    PubMed  Google Scholar 

  21. Carleton, S.C. & Carpenter, M.B. Afferent and efferent connections of the medial, inferior and lateral vestibular nuclei in the cat and monkey. Brain Res. 278, 29–51 (1983).

    CAS  PubMed  Google Scholar 

  22. Homma, Y., Nonaka, S., Matsuyama, K. & Mori, S. Fastigiofugal projection to the brainstem nuclei in the cat: an anterograde PHA-L tracing study. Neurosci. Res. 23, 89–102 (1995).

    CAS  PubMed  Google Scholar 

  23. Shimazu, H. & Smith, C.M. Cerebellar and labyrinthine influences on single vestibular neurons identified by natural stimuli. J. Neurophysiol. 34, 493–508 (1971).

    CAS  PubMed  Google Scholar 

  24. Brooks, J.X. & Cullen, K.E. Multimodal integration in rostral fastigial nucleus provides an estimate of body movement. J. Neurosci. 29, 10499–10511 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Roy, J.E. & Cullen, K.E. Selective processing of vestibular reafference during self-generated head motion. J. Neurosci. 21, 2131–2142 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Brooks, J.X. & Cullen, K.E. The primate cerebellum selectively encodes unexpected self-motion. Curr. Biol. 23, 947–955 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Kluzik, J., Diedrichsen, J., Shadmehr, R. & Bastian, A.J. Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm? J. Neurophysiol. 100, 1455–1464 (2008).

    PubMed  PubMed Central  Google Scholar 

  28. Lackner, J.R. & DiZio, P. Adaptation to Coriolis force perturbation of movement trajectory; role of proprioceptive and cutaneous somatosensory feedback. Adv. Exp. Med. Biol. 508, 69–78 (2002).

    PubMed  Google Scholar 

  29. Scheidt, R.A., Conditt, M.A., Secco, E.L. & Mussa-Ivaldi, F.A. Interaction of visual and proprioceptive feedback during adaptation of human reaching movements. J. Neurophysiol. 93, 3200–3213 (2005).

    PubMed  Google Scholar 

  30. Zago, M. et al. Fast adaptation of the internal model of gravity for manual interceptions: evidence for event-dependent learning. J. Neurophysiol. 93, 1055–1068 (2005).

    PubMed  Google Scholar 

  31. Lackner, J.R. & DiZio, P.A. Adaptation to rotating artificial gravity environments. J. Vestib. Res. 13, 321–330 (2003).

    PubMed  Google Scholar 

  32. Shadmehr, R. & Mussa-Ivaldi, F.A. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14, 3208–3224 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Shadmehr, R. Control of movements and temporal discounting of reward. Curr. Opin. Neurobiol. 20, 726–730 (2010).

    CAS  PubMed  Google Scholar 

  34. Roy, J.E. & Cullen, K.E. Dissociating self-generated from passively applied head motion: neural mechanisms in the vestibular nuclei. J. Neurosci. 24, 2102–2111 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Rabe, K. et al. Adaptation to visuomotor rotation and force field perturbation is correlated to different brain areas in patients with cerebellar degeneration. J. Neurophysiol. 101, 1961–1971 (2009).

    CAS  PubMed  Google Scholar 

  36. Smith, M.A. & Shadmehr, R. Intact ability to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration. J. Neurophysiol. 93, 2809–2821 (2005).

    PubMed  Google Scholar 

  37. Werner, S., Bock, O. & Timmann, D. The effect of cerebellar cortical degeneration on adaptive plasticity and movement control. Exp. Brain Res. 193, 189–196 (2009).

    PubMed  Google Scholar 

  38. Barash, S. et al. Saccadic dysmetria and adaptation after lesions of the cerebellar cortex. J. Neurosci. 19, 10931–10939 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Rambold, H., Churchland, A., Selig, Y., Jasmin, L. & Lisberger, S.G. Partial ablations of the flocculus and ventral paraflocculus in monkeys cause linked deficits in smooth pursuit eye movements and adaptive modification of the VOR. J. Neurophysiol. 87, 912–924 (2002).

    CAS  PubMed  Google Scholar 

  40. Izawa, J. & Shadmehr, R. Learning from sensory and reward prediction errors during motor adaptation. PLOS Comput. Biol. 7, e1002012 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Gardner, E.P. & Fuchs, A.F. Single-unit responses to natural vestibular stimuli and eye movements in deep cerebellar nuclei of the alert rhesus monkey. J. Neurophysiol. 38, 627–649 (1975).

    CAS  PubMed  Google Scholar 

  42. Shaikh, A.G., Ghasia, F.F., Dickman, J.D. & Angelaki, D.E. Properties of cerebellar fastigial neurons during translation, rotation, and eye movements. J. Neurophysiol. 93, 853–863 (2005).

    PubMed  Google Scholar 

  43. Lisberger, S.G. Internal models of eye movement in the floccular complex of the monkey cerebellum. Neuroscience 162, 763–776 (2009).

    CAS  PubMed  Google Scholar 

  44. Hewitt, A.L., Popa, L.S., Pasalar, S., Hendrix, C.M. & Ebner, T.J. Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks. J. Neurophysiol. 106, 2232–2247 (2011).

    PubMed  PubMed Central  Google Scholar 

  45. Barresi, M., Bruschini, L., Li Volsi, G. & Manzoni, D. Effects of leg-to-body position on the responses of rat cerebellar and vestibular nuclear neurons to labyrinthine stimulation. Cerebellum 11, 212–222 (2012).

    PubMed  Google Scholar 

  46. Wilson, V.J., Yamagata, Y., Yates, B.J., Schor, R.H. & Nonaka, S. Response of vestibular neurons to head rotations in vertical planes. III. Response of vestibulocollic neurons to vestibular and neck stimulation. J. Neurophysiol. 64, 1695–1703 (1990).

    CAS  PubMed  Google Scholar 

  47. Kammermeier, S., Kleine, J. & Buttner, U. Vestibular-neck interaction in cerebellar patients. Ann. NY Acad. Sci. 1164, 394–399 (2009).

    PubMed  Google Scholar 

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Acknowledgements

We thank M. Chacron, M. Jamali, D. Mitchell, A. Dale and I. Mackrous for helpful discussions and critical reading of the manuscript, W. Kucharski for mechanical expertise and S. Nuara for animal care assistance. This study was supported by grants from the Canadian Institute of Health Research (MOP-42440), US National Institutes of Health (R01 DC002390) and from the Fonds Québécois de la Recherche sur la Nature et les Technologies to J.X.B.

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Author notes

  1. Jessica X Brooks and Jerome Carriot: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Physiology, Aerospace Medical Research Unit, McGill University, Montreal, Canada

    Jessica X Brooks, Jerome Carriot & Kathleen E Cullen

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Contributions

J.X.B., J.C. and K.E.C. designed the study. J.X.B. and J.C. performed the experiments. J.X.B. and J.C. analyzed the data. K.E.C., J.X.B. and J.C. wrote the paper.

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Correspondence to Kathleen E Cullen.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 A typical bimodal neuron is unresponsive during control active head-on-body motion, and remains so during learning and catch trials.

A. Top row shows the head velocity during control trials, learning phase and catch trials overlaying a minimum of 5 trials. Bottom row shows the firing rates corresponding to the head movements above. Grey lines show individual trials and black lines show the average responses. B. The magnitude of head velocity error during control, learning, and catch trials. During the learning phase, the monkey initially made slower head movements as quantified by an increase in head velocity error. As learning further progressed, head velocity increased nearing control values as indicated by the striking decrease in head velocity error magnitude (light blue bars). C. Normalized sensitivity to corresponding head movements shown above. During the learning phase, the neuron remained insensitive as predicted by its response to active head-on-body motion before learning (light bluebars). Similarly, the minimal response for catch trials (red), was comparable to that measured for active head on body movements in the control condition and during learning. Data in B and C show average and error bars represent ±SEM.

Supplementary Figure 2 Average head velocity and sensitivity for our population of bimodal neurons during the learning phase.

A. Normalized head velocity for control trials before learning, learning phase and catch trials. B. Normalized neuronal sensitivity for control trials, the learning phase and catch trials. Data in A and B show average and error bars represent ±SEM. C. Scatter plot of peak head velocity errors over time for each trial during the learning phase. D. Scatter plot of normalized neuronal sensitivity over time for each trial during the learning phase. Black lines show exponential fits to the data.

Supplementary Figure 3 Trial-by trial analysis of single rFN neuron responses.

A,B. Exponential fits to the head movement error (A) and neuronal sensitivity data (B) for each cell collected during learning (Grey lines). C,D. exponential fits to the head movement error and neuronal sensitivity data collected for each cell collected during learning extinction. Finally, for each panel, the data for the individual example cell is superimposed (blue circles).

Supplementary Figure 4 A typical bimodal neuron remains unresponsive during the extinction of learning.

A. Top row shows the head velocity during the extinction phase overlaying a minimum of 5 trials. Bottom row shows the firing rates corresponding to the head movements above. Grey lines show individual trials and black lines show the average. B. Magnitude of head velocity error during the extinction phase. C. Normalized neuronal sensitivity to head motion during the extinction phase. Data in B and C show average and error bars represent ±SEM. Data from the control (before learning) and catch trials are reproduced here for comparison.

Supplementary Figure 5 Average head velocity and sensitivity for our population of bimodal neurons during the extinction phase.

A. Normalized head velocity for control trials before learning, catch trials and extinction phase. B. Normalized neuronal sensitivity for control trials before learning, catch trials and extinction phase. Data in A and B show average and error bars represent ±SEM. C. Scatter plot of peak head velocity error over time for each trial during the extinction phase. D. Scatter plot of normalized sensitivity over time for each trial during the extinction phase. Black lines show exponential fits to the data.

Supplementary Figure 6 Trial-by trial analysis of single VN neuron responses.

A,B. Exponential fits to the head movement error (A) and neuronal sensitivity data (B) for each cell during learning (Grey lines). C,D. exponential fits to the head movement error and neuronal sensitivity data for each cell collected during learning extinction. Finally, for each panel, the data for the individual example cell is superimposed (blue circles).

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Brooks, J., Carriot, J. & Cullen, K. Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion. Nat Neurosci 18, 1310–1317 (2015). https://doi.org/10.1038/nn.4077

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