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Cerebellar associative learning underlies skilled reach adaptation

Nature Neuroscience volume 26, pages 1068–1079 (2023)Cite this article

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Abstract

The cerebellum is hypothesized to refine movement through online adjustments. We examined how such predictive control may be generated using a mouse reach paradigm, testing whether the cerebellum uses within-reach information as a predictor to adjust reach kinematics. We first identified a population-level response in Purkinje cells that scales inversely with reach velocity, pointing to the cerebellar cortex as a potential site linking kinematic predictors and anticipatory control. Next, we showed that mice can learn to compensate for a predictable reach perturbation caused by repeated, closed-loop optogenetic stimulation of pontocerebellar mossy fiber inputs. Both neural and behavioral readouts showed adaptation to position-locked mossy fiber perturbations and exhibited aftereffects when stimulation was removed. Surprisingly, position-randomized stimulation schedules drove partial adaptation but no opposing aftereffects. A model that recapitulated these findings suggests that the cerebellum may decipher cause-and-effect relationships through time-dependent generalization mechanisms.

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Fig. 1: Net population activity in PCs predicts reach velocity.
Fig. 2: Reaches with Cspks have erroneous kinematics and elevated simple spike rates.
Fig. 3: Adaptation to mossy fiber stimulation during reach.
Fig. 4: PCs show electrophysiological correlates of behavioral adaptation over the stimulation and washout blocks.
Fig. 5: Dissociation of adaptation and aftereffects with randomized stimulation position.
Fig. 6: A cerebellar model accounts for adaptation and aftereffect dissociation.

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Data availability

Source data used to make each of the figures are provided with this paper. Raw data are available upon reasonable request to the authors.

Code availability

The code for cerebellar model and custom analysis code can be found at https://github.com/dycala.

References

  1. Holmes, G. The symptoms of acute cerebellar injuries due to gunshot injuries. Brain 40, 461–535 (1917).

    Article  Google Scholar 

  2. Becker, M. I. & Person, A. L. Cerebellar control of reach kinematics for endpoint precision. Neuron 103, 335–348.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Baizer, J. S., Kralj-Hans, I. & Glickstein, M. Cerebellar lesions and prism adaptation in macaque monkeys. J. Neurophysiol. 81, 1960–1965 (1999).

    Article  CAS  PubMed  Google Scholar 

  4. 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).

    Article  PubMed  Google Scholar 

  5. Morton, S. M. & Bastian, A. J. Cerebellar contributions to locomotor adaptations during splitbelt treadmill walking. J. Neurosci. 26, 9107–9116 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  8. Kawato, M. & Gomi, H. The cerebellum and VOR/OKR learning models. Trends Neurosci. 15, 445–453 (1992).

    Article  CAS  PubMed  Google Scholar 

  9. Ohyama, T., Nores, W. L., Murphy, M. & Mauk, M. D. What the cerebellum computes. Trends Neurosci. 26, 222–227 (2003).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  11. Lee, K. H. et al. Circuit mechanisms underlying motor memory formation in the cerebellum. Neuron 86, 529–540 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lavond, D. G., Knowlton, B. J., Steinmetz, J. E. & Thompson, R. F. Classical conditioning of the rabbit eyelid response with a mossy-fiber stimulation CS: II. lateral reticular nucleus stimulation. Behav. Neurosci. 101, 676–682 (1987).

    Article  CAS  PubMed  Google Scholar 

  13. Mauk, M. D., Steinmetz, J. E. & Thompson, R. F. Classical conditioning using stimulation of the inferior olive as the unconditioned stimulus. Proc. Natl Acad. Sci. USA 83, 5349–5353 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Coesmans, M., Weber, J. T., De Zeeuw, C. I. & Hansel, C. Bidirectional parallel fiber plasticity in the cerebellum under climbing fiber control. Neuron 44, 691–700 (2004).

    Article  CAS  PubMed  Google Scholar 

  15. Ito, M. & Kano, M. Long-lasting depression of parallel fiber-Purkinje cell transmission induced by conjunctive stimulation of parallel fibers and climbing fibers in the cerebellar cortex. Neurosci. Lett. 33, 253–258 (1982).

    Article  CAS  PubMed  Google Scholar 

  16. Blazquez, P. M., Hirata, Y., Heiney, S. A., Green, A. M. & Highstein, S. M. Cerebellar signatures of vestibulo-ocular reflex motor learning. J. Neurosci. 23, 9742–9751 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yang, Y. & Lisberger, S. G. Interaction of plasticity and circuit organization during the acquisition of cerebellum-dependent motor learning. eLife 2, e01574 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Herzfeld, D. J., Kojima, Y., Soetedjo, R. & Shadmehr, R. Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum. Nat. Neurosci. 21, 736–743 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Raymond, J. L., Lisberger, S. G. & Mauk, M. D. The cerebellum: a neuronal learning machine? Science 272, 1126–1131 (1996).

    Article  CAS  PubMed  Google Scholar 

  20. Gandolfo, F., Li, C. S. R., Benda, B. J., Padoa Schioppa, C. & Bizzi, E. Cortical correlates of learning in monkeys adapting to a new dynamical environment. Proc. Natl Acad. Sci. USA 97, 2259–2263 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Li, C. S. R., Padoa-Schioppa, C. & Bizzi, E. Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron 30, 593–607 (2001).

    Article  CAS  PubMed  Google Scholar 

  22. Mathis, M. W., Mathis, A. & Uchida, N. Somatosensory cortex plays an essential role in forelimb motor adaptation in mice. Neuron 93, 1493–1503.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Proville, R. D. et al. Cerebellum involvement in cortical sensorimotor circuits for the control of voluntary movements. Nat. Neurosci. 17, 1233–1239 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Hewitt, A. L., Popa, L. S. & Ebner, T. J. Changes in Purkinje cell simple spike encoding of reach kinematics during adaption to a mechanical perturbation. J. Neurosci. 35, 1106–1124 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ito, M. Neural design of the cerebellar motor control system. Brain Res. 40, 81–84 (1972).

    Article  CAS  PubMed  Google Scholar 

  26. Weiler, J., Gribble, P. L. & Pruszynski, J. A. Spinal stretch reflexes support efficient hand control. Nat. Neurosci. 22, 529–533 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Low, A. Y. T. et al. Precision of discrete and rhythmic forelimb movements requires a distinct neuronal subpopulation in the interposed anterior nucleus. Cell Rep. 22, 2322–2333 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Guo, J.-Z. et al. Disrupting cortico-cerebellar communication impairs dexterity. eLife 10, e65906 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Becker, M. I., Calame, D. J., Wrobel, J. & Person, A. L. Online control of reach accuracy in mice. J. Neurophysiol. 124, 1637–1655 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 58, 267–288 (1996).

    Google Scholar 

  31. Popa, L. S., Streng, M. L. & Ebner, T. J. Long-term predictive and feedback encoding of motor signals in the simple spike discharge of Purkinje cells. eNeuro 4, ENEURO.0036-17.2017 (2017).

  32. Streng, M. L., Popa, L. S. & Ebner, T. J. Climbing fibers control Purkinje cell representations of behavior. J. Neurosci. 37, 1997–2009 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Coltz, J. D., Johnson, M. T. & Ebner, T. J. Cerebellar Purkinje cell simple spike discharge encodes movement velocity in primates during visuomotor arm tracking. J. Neurosci. 19, 1782–1803 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Roitman, A. V., Pasalar, S., Johnson, M. T. V. & Ebner, T. J. Position, direction of movement, and speed tuning of cerebellar Purkinje cells during circular manual tracking in monkey. J. Neurosci. 25, 9244–9257 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Herzfeld, D. J., Kojima, Y., Soetedjo, R. & Shadmehr, R. Encoding of action by the Purkinje cells of the cerebellum. Nature 526, 439–441 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Brown, S. T. & Raman, I. M. Sensorimotor integration and amplification of reflexive whisking by well-timed spiking in the cerebellar corticonuclear circuit. Neuron 99, 564–575.e2 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Person, A. L. & Raman, I. M. Purkinje neuron synchrony elicits time-locked spiking in the cerebellar nuclei. Nature 481, 502–505 (2012).

    Article  CAS  Google Scholar 

  40. Thier, P., Dicke, P. W., Haas, R. & Barash, S. Encoding of movement time by populations of cerebellar Purkinje cells. Nature 405, 72–76 (2000).

    Article  CAS  PubMed  Google Scholar 

  41. Medina, J. F. & Lisberger, S. G. Links from complex spikes to local plasticity and motor learning in the cerebellum of awake-behaving monkeys. Nat. Neurosci. 11, 1185–1192 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kimpo, R. R., Rinaldi, J. M., Kim, C. K., Payne, H. L. & Raymond, J. L. Gating of neural error signals during motor learning. eLife 3, e02076 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Avraham, G., Taylor, J. A., Breska, A., Ivry, R. B. & McDougle, S. D. Contextual effects in sensorimotor adaptation adhere to associative learning rules. eLife 11, e75801 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kitazawa, S., Kimura, T. & Yin, P. B. Cerebellar complex spikes encode both destinations and errors in arm movements. Nature 392, 494–497 (1998).

    Article  CAS  PubMed  Google Scholar 

  45. Gaffield, M. A., Sauerbrei, B. A. & Christie, J. M. Cerebellum encodes and influences the initiation, performance, and termination of discontinuous movements in mice. eLife 11, e71464 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chabrol, F. P., Blot, A. & Mrsic-Flogel, T. D. Cerebellar contribution to preparatory activity in motor neocortex. Neuron 103, 506–519.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Miall, R. C., Keating, J. G., Malkmus, M. & Thach, W. T. Simple spike activity predicts occurrence of complex spikes in cerebellar Purkinje cells. Nat. Neurosci. 1, 13–15 (1998).

    Article  CAS  PubMed  Google Scholar 

  48. Brodal, P. & Bjaalie, J. G. Organization of the pontine nuclei. Neurosci. Res. 13, 83–118 (1992).

    Article  CAS  PubMed  Google Scholar 

  49. Sillitoe, R. V., Fu, Y. H. & Watson, C. in The Mouse Nervous System (eds Watson, C. et al.) Ch. 11 (Elsevier, 2012).

  50. Rasmussen, A., Jirenhed, D. A., Wetmore, D. Z. & Hesslow, G. Changes in complex spike activity during classical conditioning. Front. Neural Circuits 8, 90 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Herzfeld, D. J., Hall, N. J., Tringides, M. & Lisberger, S. G. Principles of operation of a cerebellar learning circuit. eLife 9, e55217 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Mauk, M. D. & Buonomano, D. V. The neural basis of temporal processing. Annu. Rev. Neurosci. 27, 307–340 (2004).

    Article  CAS  PubMed  Google Scholar 

  53. Shimansky, Y., Wang, J. J., Bauer, R. A., Bracha, V. & Bloedel, J. R. On-line compensation for perturbations of a reaching movement is cerebellar dependent: Support for the task dependency hypothesis. Exp. Brain Res. 155, 156–172 (2004).

    Article  PubMed  Google Scholar 

  54. Albergaria, C., Silva, N. T., Darmohray, D. M. & Care, M. R. Locomotor activity modulates associative learning in mouse cerebellum. Nat. Neurosci. 21, 725–735 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Nashef, A., Cohen, C., Isreal, Z., Harel, R. & Prut, Y. Cerebellar shaping of motor cortical firing is correlated with timing of motor actions. Cell Rep. 23, 1275–1285 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. White, J. J. et al. An optimized surgical approach for obtaining stable extracellular single-unit recordings from the cerebellum of head-fixed behaving mice. J. Neurosci. Methods 262, 21–31 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  57. TestArduino (2015); https://github.com/cortex-lab/phy

  58. Yu, B., Gabriel, D., Noble, L. & An, K. N. Estimate of the optimum cut-off frequency for the Butterworth low-pass digital filter. J. Appl. Biomech. 15, 318–329 (1999).

    Article  Google Scholar 

  59. Pachitariu, M., Steinmetz, N., Kadir, S., Carandini, M. & Harris, K. D. Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels. Preprint at bioRxiv https://doi.org/10.1101/061481

  60. Hensbroek, R. A. et al. Identifying Purkinje cells using only their spontaneous simple spike activity. J. Neurosci. Methods 232, 173–180 (2014).

    Article  PubMed  Google Scholar 

  61. Kennedy, A. et al. A temporal basis for predicting the sensory consequences of motor commands in an electric fish. Nat. Neurosci. 17, 416–422 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Lanore, F., Cayco-Gajic, N. A., Gurnani, H., Coyle, D. & Silver, R. A. Cerebellar granule cell axons support high-dimensional representations. Nat. Neurosci. 24, 1142–1150 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Wagner, M. J. et al. Shared cortex-cerebellum dynamics in the execution and learning of a motor task. Cell 177, 669–682 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the members of A.L.P.’s laboratory for critical feedback on the manuscript; the Neurotechnology Center at the University of Colorado Anschutz Medical campus for use of core facilities, including the Advanced Light Microscopy Core and the Optogenetics and Neural Engineering Core. We thank M. Spindle and E. Judd for technical assistance. Work was supported by F31 NS113395-01 to D.J.C.; and NS114430, National Science Foundation CAREER grant 1749568, and by a grant from the Simons Foundation as part of the Simons-Emory International Consortium on Motor Control to A.L.P.

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Authors and Affiliations

  1. Neuroscience Graduate Program, University of Colorado School of Medicine, Aurora, CO, USA

    Dylan J. Calame & Matthew I. Becker

  2. Medical Scientist Training Program, University of Colorado School of Medicine, Aurora, CO, USA

    Dylan J. Calame & Matthew I. Becker

  3. Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO, USA

    Abigail L. Person

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Contributions

D.J.C. and A.L.P. designed experiments, interpreted data and wrote the manuscript. D.J.C. performed experiments, built the computational model, made figures and analyzed data. M.I.B. developed the closed-loop system, edited the manuscript and contributed to early conceptualization of the project.

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Correspondence to Abigail L. Person.

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Extended data

Extended Data Fig. 1 Reach tracking and reach performance over sessions.

a. The right hand was tracked with high-speed cameras as mice reached upwards and outwards towards a food pellet. Positional outreach trajectories from a single session viewed are shown from a lateral (left) or bottom-up (right) vantage point with traces colored by the magnitude of outward velocity. b. Mice were trained for a minimum of 15 days on the reaching task. Pellet retrieval success was tracked throughout training for each mouse, mean is shown in red. c. Quantification of success rate on day 1 of training and day 15. (p-values: (c) 6.4×10-8, paired t-test; Sample size: n = 19 animals; * indicates p-value < 0.05).

Source data

Extended Data Fig. 2 PC identification by firing rate characteristics.

a. Cerebellar recordings using single electrodes were first anatomically targeted to cerebellar cortex. If a recorded cell had visible Cspks they were classified as PCs. Otherwise, if cells had a firing rate > 40 Hz, a median absolute difference firing rate from the median interspike interval (MAD) < 0.008, and a CV2 > 0.2, they were classified as PCs81. b. Neuropixel-recorded single units were crosscorrelated with nearby (<200 microns) low firing rate (<5 Hz) single units. If this crosscorrelation exhibited the characteristic firing rate pause seen in PC simple spikes after a Cspk, these units were classified as the simple spikes and Cspks of a single PC. If no pause was seen, cells that exhibited the same firing rate, MAD, and CV2 profile described in a were classified as PCs. c. Example simple spike pause aligned to the time of a Cspk from a Neuropixel recording. d. Embedding MAD, CV2, and FR into a two-dimensional space using tSNE shows two distinct clusters, one corresponding largely to cells that were identified using the criteria in a and b and the other corresponding to other cells (n = 1268 sorted cells). e. Three example cells from a single session showing a neuron that was classified as a PC due to the presence of complex spikes (red, left), a neuron that was classified as a PC using firing rate criteria (blue, middle), and a neuron that was classified as other (grey, right). The simple spike raster and averaged simple spike firing rate PETH are shown on the bottom and top, respectively.

Source data

Extended Data Fig. 3 Single cell and population reach encoding supplemental information.

a. Schematic of LASSO regressions. 23 kinematic variables were regressed against firing rate at different lags from 0 to -300 ms. The lag that minimized the mean squared error (MSE) of the regressions was selected. b. Peak modulation time of all cells across all reaches. c. Optimal lags of the LASSO regression for each cell. d. Fraction of the unique contribution to total variance explained for each regressor. e. Fraction of regressions with each variable selected (mean shown for each regressor). f. Same as in Fig. 1d but only analyzing the subset of PCs that had Cspks identified. g. PCs with Cspks show no changes in model error across the reach, consistent with the total PC dataset. h. PCs with Cspks display kinematic variables with similar relative contributions to model variance explained compared to the total PC dataset. i. Variables included in the LASSO model in PCs with Cspks are consistent with data in the total PC dataset (mean shown for each regressor). j. Same as in Fig. 1h but only analyzing PCs with Cspks. The top and bottom 50% of outward reach velocities are analyzed. k. Quantification of the simple spike suppression of the data in f. l. Time of FR suppression for the data in f. (p-values: (f) empirical vs. reach shuffle: 4.4 ×10-11, empirical vs spike shuffle: 2.4 ×10-11, Wilcoxon signed rank test (k) 3.7 ×10-2, Wilcoxon signed rank test; Sample size: (b-e) N = 11 animals, 465 cells (f-k) N = 8 animals, 59 cells; * indicates p-value < 0.05; all error bars and bands represent mean ± SEM; in box and whiskers plots box denotes median and 25th/75th percentiles, whiskers denote 10th and 90th percentiles, circle indicates mean).

Source data

Extended Data Fig. 4 Kinematic and simple spike correlates of early Cspks.

a. Cspks in the 500 ms before reach onset were not associated with differences in target error as assessed with euclidean distance form session median compared to non-Cspk trials. b. No difference in peak outward velocity was observed between Cspks and non-Cspk trials. c. Simple spike firing rate in trials with early Cspk and non-Cspk trials. d. No difference in simple spike rate during outreach was seen in early Cspk trials compared with non-Cspk trials. e. No difference in simple spike rate per outward velocity was seen in early Cspk trials compared with non-Cspk trials. f. Simple spike firing aligned to the time of early Cspks compared to similarly aligned trials without early Cspk trials.g. No difference in simple spike rate in the 100 ms preceding early Cspks was seen compared to the similarly aligned previous or next trial. (p-values: (a) 0.32, Wilcoxon signed rank test (b) 0.75, paired t-test (d) 0.37, paired t-test (e) 0.29, Wilcoxon signed rank test (g) 0.47, RM one-way ANOVA, previous trial vs cspk trial: 0.97, cspk trial vs next trial: 0.42, Tukey’s multiple comparisons test; Sample size: N = 8 animals, 58 cells; all error bars and bands represent mean ± SEM).

Source data

Extended Data Fig. 5 Changes in PC firing during optogenetic stimulation of mossy fibers.

a. Mossy fiber boutons expressing hSyn-ChR2-mCherry in the cerebellar cortex. b. Simple spike responses to mossy fiber stimulation. Left: examples of single-cell simple spike responses to mossy fiber stimulation. Right: quantification of simple spike responses to all recorded cells. Significance of differences are indicated by the color and corresponding p-value map. c. Cspk responses to mossy fiber stimulation. Left: PSTH of the population of recorded cells with Cspks binned at 50 ms. A single trace showing a Cspk after stimulation is shown above. Right: Quantification of Cspk probability in the 250 ms after stimulation and non-stimulated epochs for each cell. (p-values: (b) paired t-test, (c) 5.6 ×10-3, paired t-test Sample size: (a) 1 of 4 mice displayed, (b) N = 4 animals, 151 cells (c) N = 4 animals, 39 cells; * indicates p-value < 0.05; all error bars and bands represent mean ± SEM).

Source data

Extended Data Fig. 6 Opsin expression for mice in behavioral experiments.

a. Histological section showing ChR2-mCherry expression at the injection site in the left pontine nuclei (Pn: pontine nuclei; RtTg: reticulotegmental nuclei; PnO: pontine reticular nuclei, oral part; PnC: pontine reticular nuclei, caudal part; 1 of 7 mice displayed). b. Contours of ChR2 expression in the pontine nuclei for mice used in behavioral experiments. c. Right cerebellum of the animal shown in a. Mossy fiber axons (grey arrow) and boutons (white arrow) can be seen expressing ChR2 in the cerebellar cortex. The approximate location of the optical fiber and recording site path are shown in white (1 of 7 mice displayed). d. Location of fiber placement in a representative section for animals used in behavioral experiments.

Extended Data Fig. 7 Fixed-position stimulation supplemental data.

a. Two example mice with differing effects of stimulation on early reaches in the stimulation block. To account for diverging effects we define the direction of deviation with stimulation as positive and the opposing direction as negative. b. Summary of the relative change in upward position for the same data shown in Fig. 3e. Relative change in upward position was assessed in the 50-ms window following the end of stimulation. c. Summary of the relative change in lateral position for the same data shown in Fig. 3e. d. Summary of the relative change in outward position for in the 50-ms window before stimulation. e. Stimulating with 635-nm light did not cause deviations in position or adaptation profiles. f. Stimulating while the mouse was awake with its hand at rest on the bar produced virtually no movement. (Sample size: (b-d) N = 5 mice; 104 sessions, (e) N = 2 mice; 19 sessions, (f) N = 4 animals, 21 sessions; all error bars and bands represent mean ± SEM; in box and whiskers plots box denotes median and 25th/75th percentiles, whiskers denote 10th and 90th percentiles, circle indicates mean).

Source data

Extended Data Fig. 8 Assessing unit stability across recording sessions and population responses during fixed-position stimulation experiments.

a. Left: Waveforms templates detected on each Neuropixel electrode for a cell during baseline and during washout. Right: Histogram of waveform correlation of PCs across sessions (red) and of mismatched neighboring cells, across the session (shuffled control, grey). PCs with an across-session waveform correlation that fell below the 99th percentile of the shuffled control (dashed line) were excluded from further analysis. b. Left: Unit displacement for cells across a session. Baseline unit position is shown in grey and washout position is shown in red. Right: Histogram of unit displacement of PCs across sessions (red) and of mismatched neighboring cells, across the session (shuffled control, grey). PCs with an across-session displacement that fell below the 1st percentile of the shuffled control (dashed line) were excluded from further analysis. c. Same as data shown in Fig. 4g with the last 5 stimulated and washout reaches included. The initial stimulation and washout effects are reduced across the stimulation and washout blocks, respectively. d. Cspks analyzed during fixed-position stimulation experiments for the baseline, stimulation, and washout blocks. e. Same as the analysis shown a but only including PCs with Cspks. These cells show similar negative deflections with stimulation then adaptation upwards over the stimulation block compared to the total PC dataset. f. Quantification of simple spike firing rates in the stimulation window for the data shown in c. (p-values: (f) 5.1 ×10-5, RM one-way ANOVA, end baseline vs. first wash: 0.022, end baseline vs. first 5 wash: 0.047, first stim vs. last 5 stim: 0.043, first stim vs. first wash: 6.1 ×10-4, first stim vs. last 5 wash: 3.8 ×10-3, last 5 stim vs. first wash: 0.034, Tukey’s multiple comparisons test; Sample size: (c) n = 159 cells (d-f) n = 13 cells; * indicates p-value < 0.05; all error bars and bands represent mean ± SEM).

Source data

Extended Data Fig. 9 Temporal analysis of early washout effect for fixed-position and random-stimulation experiments.

a. Analysis of fixed-position stimulation experiment early washout reaches in 50-ms time windows across the reach. Each window is shifted 10 ms from the adjacent time window. Aftereffect emerges around the time stimulation was delivered in the stimulation block. b. Same as a but for random-position stimulation experiments. Consistent aftereffects relative to baseline reaches do not emerge in any of the analyzed windows. (Sample size: (a) N = 5 mice, 104 sessions, (b) N = 5 mice, 60 sessions; in box and whiskers plots box denotes median and 25th/75th percentiles, whiskers denote 10th and 90th percentiles, circle indicates mean).

Source data

Extended Data Fig. 10 Cerebellar model adaptation to a negative-going perturbation.

a. Model as described in Fig. 6. In this case the number of stimulated MLIs is greater than the number of parallel fibers (bottom) leading to a net negative stimulation effect. Negative simple spike error lowers the probability of Cspks below baseline, leading to LTP (top). b. PC simple spike activity during the stimulation block and washout block of fixed-position stimulation as described in Fig. 6b. Here the stimulation reduces firing rate. c. Same as described in Fig. 6c. Here parallel fiber weight changes increase to compensate for the stimulation. Note that while not displayed the quantification of the adaptation is identical to the data displayed in Fig. 6d. d. Comparison of model output to empirical observations for fixed-position stimulus conditions (Fig. 3). Model closely matches behavioral adaptation. e. Same as b. but here the stimulation window is randomized across the reach. Note that while not displayed the quantification of the adaptation is identical to the data displayed in Fig. 6d.

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Supplementary information

Reporting Summary

Supplementary Video 1

Example of electrophysiological recording during mouse reaching movements.

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Source Data Figs. 1–6

Statistical source data.

Source Data Extended Data Figs. 1–5 and 7–10

Statistical source data.

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Calame, D.J., Becker, M.I. & Person, A.L. Cerebellar associative learning underlies skilled reach adaptation. Nat Neurosci 26, 1068–1079 (2023). https://doi.org/10.1038/s41593-023-01347-y

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