Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Coordinated cerebellar climbing fiber activity signals learned sensorimotor predictions

Nature Neuroscience volume 21, pages 1431–1441 (2018)Cite this article

Subjects

Abstract

The prevailing model of cerebellar learning states that climbing fibers (CFs) are both driven by, and serve to correct, erroneous motor output. However, this model is grounded largely in studies of behaviors that utilize hardwired neural pathways to link sensory input to motor output. To test whether this model applies to more flexible learning regimes that require arbitrary sensorimotor associations, we developed a cerebellar-dependent motor learning task that is compatible with both mesoscale and single-dendrite-resolution calcium imaging in mice. We found that CFs were preferentially driven by and more time-locked to correctly executed movements and other task parameters that predict reward outcome, exhibiting widespread correlated activity in parasagittal processing zones that was governed by these predictions. Together, our data suggest that such CF activity patterns are well-suited to drive learning by providing predictive instructional input that is consistent with an unsigned reinforcement learning signal but does not rely exclusively on motor errors.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Cerebellar sensorimotor task for head-fixed mice.
Fig. 2: Learning requires synaptic transmission in lobule simplex.
Fig. 3: Single-photon imaging during cue reaction sessions.
Fig. 4: Lever dynamics and licking do not explain differences in complex spiking across trial types.
Fig. 5: Complex spiking produces larger mean response in individual dendrites and enhanced population responses when movement is correctly timed.
Fig. 6: Complex spiking occurs with higher peak rates and greater synchrony when movements are correctly timed.
Fig. 7: Complex spiking is correlated across parasagittal zones, with higher correlations on correct lever releases.
Fig. 8: Complex spiking is modulated by learned sensorimotor predictions.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Albus, J. S. A theory of cerebellar function. Math. Biosci. 10, 25–61 (1971).

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  3. Marr, D. A theory of cerebellar cortex. J. Physiol. (Lond.) 202, 437–470 (1969).

    CAS  Google Scholar 

  4. Ito, M., Sakurai, M. & Tongroach, P. Climbing fibre induced depression of both mossy fibre responsiveness and glutamate sensitivity of cerebellar Purkinje cells. J. Physiol. (Lond.) 324, 113–134 (1982).

    CAS  Google Scholar 

  5. Ekerot, C. F. & Kano, M. Long-term depression of parallel fibre synapses following stimulation of climbing fibres. Brain Res. 342, 357–360 (1985).

    CAS  PubMed  Google Scholar 

  6. Tank, D. W., Sugimori, M., Connor, J. A. & Llinás, R. R. Spatially resolved calcium dynamics of mammalian Purkinje cells in cerebellar slice. Science 242, 773–777 (1988).

    CAS  PubMed  Google Scholar 

  7. Llinás, R. & Sugimori, M. Electrophysiological properties of in vitro Purkinje cell somata in mammalian cerebellar slices. J. Physiol. (Lond.) 305, 171–195 (1980).

    Google Scholar 

  8. Medina, J. F., Nores, W. L., Ohyama, T. & Mauk, M. D. Mechanisms of cerebellar learning suggested by eyelid conditioning. Curr. Opin. Neurobiol. 10, 717–724 (2000).

    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).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  11. Schultz, S. R., Kitamura, K., Post-Uiterweer, A., Krupic, J. & Häusser, M. Spatial pattern coding of sensory information by climbing fiber-evoked calcium signals in networks of neighboring cerebellar Purkinje cells. J. Neurosci. 29, 8005–8015 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Tsutsumi, S. et al. Structure-function relationships between aldolase C/zebrin II expression and complex spike synchrony in the cerebellum. J. Neurosci. 35, 843–852 (2015).

    PubMed  PubMed Central  Google Scholar 

  13. Ozden, I., Dombeck, D. A., Hoogland, T. M., Tank, D. W. & Wang, S. S. Widespread state-dependent shifts in cerebellar activity in locomoting mice. PLoS One 7, e42650 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. De Gruijl, J. R., Hoogland, T. M. & De Zeeuw, C. I. Behavioral correlates of complex spike synchrony in cerebellar microzones. J. Neurosci. 34, 8937–8947 (2014).

    PubMed  PubMed Central  Google Scholar 

  15. Tang, T., Suh, C. Y., Blenkinsop, T. A. & Lang, E. J. Synchrony is key: complex spike inhibition of the deep cerebellar nuclei. Cerebellum 15, 10–13 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Histed, M. H., Carvalho, L. A. & Maunsell, J. H. Psychophysical measurement of contrast sensitivity in the behaving mouse. J. Neurophysiol. 107, 758–765 (2012).

    PubMed  Google Scholar 

  18. Chettih, S. N., McDougle, S. D., Ruffolo, L. I. & Medina, J. F. Adaptive timing of motor output in the mouse: the role of movement oscillations in eyelid conditioning. Front. Integr. Neurosci. 5, 72 (2011).

    PubMed  PubMed Central  Google Scholar 

  19. Gaffield, M. A., Amat, S. B., Bito, H. & Christie, J. M. Chronic imaging of movement-related Purkinje cell calcium activity in awake behaving mice. J. Neurophysiol. 115, 413–422 (2016).

    CAS  PubMed  Google Scholar 

  20. Mukamel, E. A., Nimmerjahn, A. & Schnitzer, M. J. Automated analysis of cellular signals from large-scale calcium imaging data. Neuron 63, 747–760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Najafi, F., Giovannucci, A., Wang, S. S. & Medina, J. F. Coding of stimulus strength via analog calcium signals in Purkinje cell dendrites of awake mice. eLife 3, e03663 (2014).

    PubMed  PubMed Central  Google Scholar 

  22. Ozden, I., Lee, H. M., Sullivan, M. R. & Wang, S. S. Identification and clustering of event patterns from in vivo multiphoton optical recordings of neuronal ensembles. J. Neurophysiol. 100, 495–503 (2008).

    PubMed  PubMed Central  Google Scholar 

  23. Ozden, I., Sullivan, M. R., Lee, H. M. & Wang, S. S. Reliable coding emerges from coactivation of climbing fibers in microbands of cerebellar Purkinje neurons. J. Neurosci. 29, 10463–10473 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Najafi, F., Giovannucci, A., Wang, S. S. & Medina, J. F. Sensory-driven enhancement of calcium signals in individual Purkinje cell dendrites of awake mice. Cell Rep. 6, 792–798 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Najafi, F. & Medina, J. F. Beyond “all-or-nothing” climbing fibers: graded representation of teaching signals in Purkinje cells. Front. Neural Circuits 7, 115 (2013).

    PubMed  PubMed Central  Google Scholar 

  26. Yang, Y. & Lisberger, S. G. Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration. Nature 510, 529–532 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Kitamura, K. & Häusser, M. Dendritic calcium signaling triggered by spontaneous and sensory-evoked climbing fiber input to cerebellar Purkinje cells in vivo. J. Neurosci. 31, 10847–10858 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Konnerth, A., Dreessen, J. & Augustine, G. J. Brief dendritic calcium signals initiate long-lasting synaptic depression in cerebellar Purkinje cells. Proc. Natl. Acad. Sci. USA 89, 7051–7055 (1992).

    CAS  PubMed  Google Scholar 

  29. Miyakawa, H., Lev-Ram, V., Lasser-Ross, N. & Ross, W. N. Calcium transients evoked by climbing fiber and parallel fiber synaptic inputs in guinea pig cerebellar Purkinje neurons. J. Neurophysiol. 68, 1178–1189 (1992).

    CAS  PubMed  Google Scholar 

  30. Eilers, J., Augustine, G. J. & Konnerth, A. Subthreshold synaptic Ca2+ signalling in fine dendrites and spines of cerebellar Purkinje neurons. Nature 373, 155–158 (1995).

    CAS  PubMed  Google Scholar 

  31. Hartell, N. A. Strong activation of parallel fibers produces localized calcium transients and a form of LTD that spreads to distant synapses. Neuron 16, 601–610 (1996).

    CAS  PubMed  Google Scholar 

  32. Apps, R. & Garwicz, M. Anatomical and physiological foundations of cerebellar information processing. Nat. Rev. Neurosci. 6, 297–311 (2005).

    CAS  PubMed  Google Scholar 

  33. Ohmae, S. & Medina, J. F. Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice. Nat. Neurosci. 18, 1798–1803 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Ackerley, R., Pardoe, J. & Apps, R. A novel site of synaptic relay for climbing fibre pathways relaying signals from the motor cortex to the cerebellar cortical C1 zone. J. Physiol. (Lond.) 576, 503–518 (2006).

    CAS  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  36. Zhou, H. et al. Cerebellar modules operate at different frequencies. eLife 3, e02536 (2014).

    PubMed  PubMed Central  Google Scholar 

  37. Paukert, M., Huang, Y. H., Tanaka, K., Rothstein, J. D. & Bergles, D. E. Zones of enhanced glutamate release from climbing fibers in the mammalian cerebellum. J. Neurosci. 30, 7290–7299 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Blenkinsop, T. A. & Lang, E. J. Synaptic action of the olivocerebellar system on cerebellar nuclear spike activity. J. Neurosci. 31, 14708–14720 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Bengtsson, F., Ekerot, C. F. & Jörntell, H. In vivo analysis of inhibitory synaptic inputs and rebounds in deep cerebellar nuclear neurons. PLoS One 6, e18822 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Person, A. L. & Raman, I. M. Purkinje neuron synchrony elicits time-locked spiking in the cerebellar nuclei. Nature 481, 502–505 (2011).

    PubMed  PubMed Central  Google Scholar 

  41. McElvain, L. E., Bagnall, M. W., Sakatos, A. & du Lac, S. Bidirectional plasticity gated by hyperpolarization controls the gain of postsynaptic firing responses at central vestibular nerve synapses. Neuron 68, 763–775 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Pugh, J. R. & Raman, I. M. Potentiation of mossy fiber EPSCs in the cerebellar nuclei by NMDA receptor activation followed by postinhibitory rebound current. Neuron 51, 113–123 (2006).

    CAS  PubMed  Google Scholar 

  43. Miles, F. A. & Lisberger, S. G. Plasticity in the vestibulo-ocular reflex: a new hypothesis. Annu. Rev. Neurosci. 4, 273–299 (1981).

    CAS  PubMed  Google Scholar 

  44. Matias, S., Lottem, E., Dugué, G. P. & Mainen, Z. F. Activity patterns of serotonin neurons underlying cognitive flexibility. eLife 6, e20552 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. Belova, M. A., Paton, J. J., Morrison, S. E. & Salzman, C. D. Expectation modulates neural responses to pleasant and aversive stimuli in primate amygdala. Neuron 55, 970–984 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Hayden, B. Y., Heilbronner, S. R., Pearson, J. M. & Platt, M. L. Surprise signals in anterior cingulate cortex: neuronal encoding of unsigned reward prediction errors driving adjustment in behavior. J. Neurosci. 31, 4178–4187 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Pearce, J. M. & Hall, G. A model for Pavlovian learning: variations in the effectiveness of conditioned but not of unconditioned stimuli. Psychol. Rev. 87, 532–552 (1980).

    CAS  Google Scholar 

  48. Roesch, M. R., Esber, G. R., Li, J., Daw, N. D. & Schoenbaum, G. Surprise! Neural correlates of Pearce-Hall and Rescorla-Wagner coexist within the brain. Eur. J. Neurosci. 35, 1190–1200 (2012).

    PubMed  PubMed Central  Google Scholar 

  49. Raymond, J. L. & Medina, J. F. Computational principles of supervised learning in the cerebellum. Annu. Rev. Neurosci. 41, 233–253 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. de Solages, C. et al. High-frequency organization and synchrony of activity in the Purkinje cell layer of the cerebellum. Neuron 58, 775–788 (2008).

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank L. Glickfeld for helpful discussions and input on calcium-imaging approaches and analyses, S. Lisberger, G. Field, K. Franks, and F. Wang for feedback on early versions of this manuscript, and members of the Hull and Glickfeld labs for input and technical assistance throughout the project. This work was supported by grants from the NIH NINDS (5R01NS096289-02, C.H.) and (F31NS103425, W.H.), the Sloan Foundation (C.H.), and the Whitehall Foundation (C.H.).

Author information

Author notes

  1. Andrew McKinney

    Present address: Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA

  2. Mati Joshua

    Present address: Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel

Authors and Affiliations

  1. Department of Neurobiology, Duke University Medical School, Durham, NC, USA

    William Heffley, Eun Young Song, Ziye Xu, Benjamin N. Taylor, Mary Anne Hughes, Andrew McKinney, Mati Joshua & Court Hull

Authors
  1. William Heffley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eun Young Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziye Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Benjamin N. Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mary Anne Hughes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrew McKinney
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mati Joshua
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Court Hull
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

W.H., E.Y.S., and C.H. designed the experiments. W.H., E.Y.S., A.M., B.N.T., M.A.H., and M.J. conducted the experiments. W.H., E.Y.S., Z.X., B.N.T., A.M., M.J., and C.H. analyzed the data. C.H. wrote the manuscript.

Corresponding author

Correspondence to Court Hull.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Mapping of motor output driven by Arch and electrical stimulation in superficial cerebellar cortex.

a) Schematics of the results of Arch stimulation over a grid of sites in lobule simplex (Sim) and Crus I for five mice. Colors denote outcome of stimulation. b) Same as A for three mice tested with electrical microstimulation.

Supplementary Figure 2 Retention and extinguishment of learning for cue-prediction regime.

a) Average reaction times relative to cue for the first (left) and second (right) sessions where animals performed 0.5 s cue prediction on consecutive training days (n = 17 sessions, 5 mice). Error bars are ±SEM across sessions. b) Same as a for 2 s cue prediction sessions (n = 10 sessions, 6 mice). c) Left, example cumulative distributions of reaction times for cue reaction sessions immediately surrounding a cue prediction session (session 0). Trials from the cue prediction session include only the last 1/3 of the session when the animal was anticipating the cue timing. Right, summary of distribution skewness across cue reaction sessions immediately surrounding cue prediction sessions (n = 43 sessions). Error bars are SEM across sessions. d) Same as a for pairs of 0.5 s prediction sessions separated by 3 or more cue reaction sessions (n = 20 sessions, 10 mice).

Supplementary Figure 3 Performance in the cue-prediction condition in control and NBQX sessions.

a-e) Summary across experiments of mean percent correct lever releases, mean baseline reaction time from the visual cue, mean reaction time variance, mean duration to press, mean number of trials performed, and the mean duration of behavioral sessions. n = 44 sessions, 0.5 s; n = 9, 1.0 s; n = 6, 1.5 s; n = 30, 2.0 s; n = 26, Δt; n = 14, 0.5 s NBQX. Error bars are SEM across sessions. For each cue delay, the minimum and maximum (x,y) number of trials performed in single sessions was: 0.5 s: (309, 700) 1 s: (252, 900) 1.5 s: (202, 775) 2 s: (278,1185) Δt: (313, 772) 0.5 s NBQX: (227, 429). fi) Individual (top) and average (bottom) lever trajectories aligned to lever press (left) and release (right) segregated by duration into quartiles (n = 112 trials/quartile) for a representative 0.5 s cue prediction session. fii) Same as fi for a representative 0.5 s cue prediction session in NBQX. (n = 142 trials/quartile). g) Summary of press (left) and release (right) quartiles across experiments for control (top; n = 15 sessions) and NBQX (bottom; n = 14 sessions) experiments. h) Summary of the ratio between press and release ranges (difference between 1st and 4th quartile) for control (black) and NBQX (blue) experiments.

Supplementary Figure 4 Pharmacological modulation of complex spiking in lobule simplex.

(a) Single unit recordings during NBQX application to LS. Top, summary of complex spike rates in control and after application of NBQX for individual cells (black; n = 9 cells, 5 mice) and the average of all cells (red). Error bars are ± SEM across cells. Bottom, same as top for simple spike rates. Note that NBQX strongly reduces complex but not simple spike rates. (b) (top) Raw epifluorescence images from parasagittal cerebellar sections showing fluorescein labeling after surface application to lobule simplex in vivo (Methods, 3 mice). (bottom) Pixel masks from the same images above. Pixels were thresholded at 30% of maximum value to visualize and quantify fluorescein labeling. (c) Summary of mediolateral fluorescein spread across sections. (n = 3 animals). Error bars are SEM across animals.

Supplementary Figure 5 Segmentation of PC dendrites from imaging experiments.

(a) Example single photon imaging field of view (left) is segmented to mask individual dendrites (right) used for subsequent single cell analysis. Scale bar = 100 μm. (b) Example timecourse of raw fluorescence (top) from the circled dendrite in A showing individual calcium transients identified (gray circles) according to peaks in the first derivative of the fluorescence trace (bottom). (c) Same as a for an example two photon field of view. Note that while PC somata are visible (round, top right), image segmentation does not extract activity from these structures. Scale bar = 100 μm. (d) Same as b for the experiment in C. (e) Top, example time course of a single unit PC recording from an awake mouse illustrating detection of complex spikes (gray circles). Bottom, overlay of individual simple (left) and complex (right) spike waveforms (gray) and the average waveform (black). (f) Summary of mean spike rates ± SEM across all two photon dendrite imaging sessions (blue; n = 1146 dendrites) and acute single unit recordings (n = 11 units).

Supplementary Figure 6 Single-unit PC recordings of complex spiking during the cue-prediction condition.

(a) Top, Schematic of the trial structure schematic, where a constant cue delay of 500 ms was imposed on each trial. Middle top, Average simple (SS, left) and complex (CS, right) spike waveforms from an example single unit. Middle bottom, Histogram of SS firing rate aligned to complex spike time revealing post-CS pause, confirming isolation of single PC. Bottom, raster of single trial complex spikes and session PSTH aligned to lever release. (b) Single trial voltage traces (complex spikes- open blue circle) from the cell in A aligned to lever release (correct- black circle; early- red circle). (c) Expansion of 5 consecutive traces from B at time of lever release. (d) Mean normalized complex spike rate (Methods) from single unit recordings (31 PCs, 8 mice) aligned to lever release for correct (black) and early (red) trials during the cue prediction condition. Shaded error is SEM across PCs. (e) Mean normalized complex spike rates aligned to cue presentation for the first 1/3 of trials (black) and the last 1/3 of trials (orange) across cue prediction sessions for correct (left) and early (right) trials (31 PCs, 8 mice). Shaded error is SEM across PCs. (f) Summary of the S.D. of spike times when aligned to either lever release or visual cue for each PC (n = 31). P-values reflect paired t-tests.

Supplementary Figure 7 Differential complex spiking on correct and early-release trials is not driven by motor signals due to licking.

(a) Example imaging data from an inter-trial interval (ITI) showing a lick responsive dendrite (top, dark blue) and a lick unresponsive dendrite (bottom, black) in the same field of view. (b) Averaged lick triggered calcium transients for the lick responsive dendrite (dark blue) and a lick unresponsive dendrite (black) in A). n = 28 licks, Error bars are SEM across lick-triggered events. (c) Mean lick triggered calcium transient across all lick responsive (dark blue) and lick unresponsive (black) dendrites. Shaded error is SEM across dendrites. (d) Summary of the Spearman’s correlation between lick rate and amplitude of the calcium transient in each frame across trials for lick responsive (dark blue) and lick unresponsive dendrites (grey). Red points indicate the mean ± SEM across dendrites (447 dendrites, 6 animals and 10 sessions). (e) Summary comparing the amplitude of calcium transients for lick responsive neurons only in response to either a single lick (blue) or a lick bout (3 or more licks with < 300 ms between licks; cyan). Note that additional licking does not produce larger responses. (f) Summary comparing the amplitude of calcium transients on correct (black) and early (red) lever releases for lick unresponsive dendrites only. (g) Same as f, for lick responsive dendrites. P-values reflect paired t-tests.

Supplementary Figure 8 Example single-trial and session average calcium transients from two-photon imaging.

(a) Example single trial data from an individual dendrite showing the normalized calcium transient across an early release trial from before lever press to after the lever release. (b) Same as a but for a correct trial, including the time of cue presentation. (c) Mean calcium transients across trials (n = 186) for the dendrite in A and B. Error bars are SEM across trials. (d) Single trial example, aligned to lever release, showing all dendrites from the experiment illustrated in Fig. 5c on a correct trial over 1 second surrounding release (n = 115 dendrites). Lever press is off scale. (e) Same as d, but for an early release trial from the same imaging session.

Supplementary Figure 9 Unexpected reward drives complex spiking in lick-unresponsive and -responsive dendrites.

a-b) Top, Average calcium transient in response to unexpected reward (green, aligned to first lick) and correct lever releases (black, aligned to release). Shaded error is SEM across dendrites. Bottom, Average lick rate for unexpected reward and correct lever releases. Error is SEM across experiments (n = 3). Lick responsiveness was defined according to significant responses in the lick triggered averaged taken from the inter-trial interval (Supp. Figure 7). c) Summary of peak calcium transients in the same neurons for correct lever releases vs unexpected reward trials. Note that responses are proportional, and response amplitude is determined by response probability (Figs 5, 6). P-value reflects paired t-test.

Supplementary Figure 10 Complex spiking scales with lever hold time for early releases in single-photon imaging experiments.

Summary of peak calcium transients in a 500 ms window at the time of release (Methods) across all single photon experiments for correct (black) and early (red) release trials binned according to hold time (250 ms bins). Linear fits were applied to data from each trial type (n = 10 animals, 17 sessions). Error bars are SEM across sessions.

Supplementary Figure 11 Mice have full control over the lever trajectory.

(a) Individual (top, n = 217) and average (bottom, 54 per quartile) lever trajectories aligned to lever press (left) and release (right) for manual lever depression followed by unperturbed gravity return. Shaded error is SEM across trials. (b) Summary of press (left) and release (right) quartiles across experiments for manual lever experiments (n = 4). Note the lack of variability across quartiles. (c) Summary scatterplot comparing 20-80% rise times of lever press and release for experiments where the lever was controlled by mice (black, n = 15) and manual press (green, n = 4) (d) Summary comparison measuring the average difference from the mean of the slowest release quartile and the fastest release quartile for experiments where the lever was controlled by mice (black) and manual press (green). Lever trajectories were sorted according to duration in a window from 200 ms before threshold crossing to the time of a threshold crossing half way between the top and bottom of the total lever displacement. The slowest and fastest 25 trials were extracted for comparison, and their average rise time was normalized by subtracting that of the mean trajectory of the whole session. Error bars are SEM across sessions. These results show that mice produce movements both slower and faster than the mean, indicating full control over the lever trajectory.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11

Reporting Summary

Supplementary Video 1

Optogenetic silencing of superficial PCs in lobule simplex drives ipsilateral forelimb movements. An external fiber coupled laser was activated at the time indicated by the green circle above lobule simplex, resulting in forelimb movement

Supplementary Video 2

Example correct and early release trials from the cue prediction condition. Two example trials from the same mouse in the same training session are shown first in real time, and then repeated at 0.25× video rate

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Heffley, W., Song, E.Y., Xu, Z. et al. Coordinated cerebellar climbing fiber activity signals learned sensorimotor predictions. Nat Neurosci 21, 1431–1441 (2018). https://doi.org/10.1038/s41593-018-0228-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-018-0228-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing