Article

Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning

  • Nature Neuroscience volume 20, pages 727734 (2017)
  • doi:10.1038/nn.4531
  • Download Citation
Received:
Accepted:
Published:

Abstract

Cerebellar granule cells, which constitute half the brain's neurons, supply Purkinje cells with contextual information necessary for motor learning, but how they encode this information is unknown. Here we show, using two-photon microscopy to track neural activity over multiple days of cerebellum-dependent eyeblink conditioning in mice, that granule cell populations acquire a dense representation of the anticipatory eyelid movement. Initially, granule cells responded to neutral visual and somatosensory stimuli as well as periorbital airpuffs used for training. As learning progressed, two-thirds of monitored granule cells acquired a conditional response whose timing matched or preceded the learned eyelid movements. Granule cell activity covaried trial by trial to form a redundant code. Many granule cells were also active during movements of nearby body structures. Thus, a predictive signal about the upcoming movement is widely available at the input stage of the cerebellar cortex, as required by forward models of cerebellar control.

  • Subscribe to Nature Neuroscience for full access:

    $59

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    Cerebellar circuitry as a neuronal machine. Prog. Neurobiol. 78, 272–303 (2006).

  2. 2.

    , & Sensory representations in cerebellar granule cells. Curr. Opin. Neurobiol. 19, 445–451 (2009).

  3. 3.

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

  4. 4.

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

  5. 5.

    & Questioning the role of sparse coding in the brain. Trends Neurosci. 38, 417–427 (2015).

  6. 6.

    et al. Convergence of pontine and proprioceptive streams onto multimodal cerebellar granule cells. Elife 2, e00400 (2013).

  7. 7.

    & Cerebellar premotor output neurons collateralize to innervate the cerebellar cortex. J. Comp. Neurol. 523, 2254–2271 (2015).

  8. 8.

    , , , & Cerebellar-dependent expression of motor learning during eyeblink conditioning in head-fixed mice. J. Neurosci. 34, 14845–14853 (2014).

  9. 9.

    , , , & Afferent volleys in limb nerves influencing impulse discharges in cerebellar cortex. I. In mossy fibers and granule cells. Exp. Brain Res. 13, 15–35 (1971).

  10. 10.

    , , & Synaptic representation of locomotion in single cerebellar granule cells. Elife 4 e07290 (2015).

  11. 11.

    , , , & High frequency burst firing of granule cells ensures transmission at the parallel fiber to Purkinje cell synapse at the cost of temporal coding. Front. Neural Circuits 7, 95 (2013).

  12. 12.

    & Computer simulation of cerebellar information processing. Nat. Neurosci. 3 (Suppl.), 1205–1211 (2000).

  13. 13.

    & Neural circuitry and plasticity mechanisms underlying delay eyeblink conditioning. Learn. Mem. 18, 666–677 (2011).

  14. 14.

    , & Error correction, sensory prediction, and adaptation in motor control. Annual Rev. Neurosci. 33, 89–108 (2010).

  15. 15.

    , & Internal models in the cerebellum. Trends Cogn. Sci. 2, 338–347 (1998).

  16. 16.

    , , , & Widespread state-dependent shifts in cerebellar activity in locomoting mice. PLoS One 7, e42650 (2012).

  17. 17.

    , , , & An amplified promoter system for targeted expression of calcium indicator proteins in the cerebellar cortex. Front. Neural Circuits 6, 49 (2012).

  18. 18.

    et al. Characterization of the properties of seven promoters in the motor cortex of rats and monkeys after lentiviral vector-mediated gene transfer. Hum. Gene Ther. Methods 24, 333–344 (2013).

  19. 19.

    , & Cerebellar and extracerebellar involvement in mouse eyeblink conditioning: the ACDC model. Front. Cell. Neurosci. 3, 19 (2010).

  20. 20.

    & Organization of memory traces in the mammalian brain. Annu. Rev. Neurosci. 17, 519–549 (1994).

  21. 21.

    , , & Precise control of movement kinematics by optogenetic inhibition of Purkinje cell activity. J. Neurosci. 34, 2321–2330 (2014).

  22. 22.

    Correspondence between climbing fibre input and motor output in eyeblink-related areas in cat cerebellar cortex. J. Physiol. (Lond.) 476, 229–244 (1994).

  23. 23.

    , , , & Electrophysiological localization of eyeblink-related microzones in rabbit cerebellar cortex. J. Neurosci. 30, 8920–8934 (2010).

  24. 24.

    , , & Sensory-driven enhancement of calcium signals in individual Purkinje cell dendrites of awake mice. Cell Rep. 6, 792–798 (2014).

  25. 25.

    , , & Coding of stimulus strength via analog calcium signals in Purkinje cell dendrites of awake mice. Elife 3, e03663 (2014).

  26. 26.

    et al. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89, 285–299 (2016).

  27. 27.

    , & Redundancy and synergy of neuronal ensembles in motor cortex. J. Neurosci. 25, 4207–4216 (2005).

  28. 28.

    , , & Redundancy in the population code of the retina. Neuron 46, 493–504 (2005).

  29. 29.

    Cerebellar learning mechanisms. Brain Res. 1621, 260–269 (2015).

  30. 30.

    et al. Excitatory cerebellar nucleocortical circuit provides internal amplification during associative conditioning. Neuron 89, 645–657 (2016).

  31. 31.

    et al. A novel inhibitory nucleo-cortical circuit controls cerebellar Golgi cell activity. Elife 4, e06262 (2015).

  32. 32.

    Cerebellar physiology: links between microcircuitry properties and sensorimotor functions. J. Physiol. (Lond.) 595, 11–27 (2017).

  33. 33.

    & Variation, signal, and noise in cerebellar sensory-motor processing for smooth-pursuit eye movements. J. Neurosci. 27, 6832–6842 (2007).

  34. 34.

    et al. Evolving models of Pavlovian conditioning: cerebellar cortical dynamics in awake behaving mice. Cell Rep. 13, 1977–1988 (2015).

  35. 35.

    & How and why neural and motor variation are related. Curr. Opin. Neurobiol. 33, 110–116 (2015).

  36. 36.

    & Are Purkinje cell pauses drivers of classically conditioned blink responses? Cerebellum 15, 526–534 (2016).

  37. 37.

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

  38. 38.

    , , , & Network structure within the cerebellar input layer enables lossless sparse encoding. Neuron 83, 960–974 (2014).

  39. 39.

    Latent inhibition. Psychol. Bull. 79, 398–407 (1973).

  40. 40.

    et al. High-speed mapping of synaptic connectivity using photostimulation in channelrhodopsin-2 transgenic mice. Proc. Natl. Acad. Sci. USA 104, 8143–8148 (2007).

  41. 41.

    Contrast limited adaptive histogram equalization. in Graphics Gems IV (ed., Heckbert, P.S.) 474–485 (Academic Press Professional, Inc., 1994).

  42. 42.

    & Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 886–893 (2005).

  43. 43.

    A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979).

  44. 44.

    & Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32, 2262–2275 (2010).

  45. 45.

    et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473–478 (2012).

  46. 46.

    et al. Climbing fiber input shapes reciprocity of Purkinje cell firing. Neuron 78, 700–713 (2013).

  47. 47.

    et al. A role for bicaudal-D2 in radial cerebellar granule cell migration. Nat. Commun. 5, 3411 (2014).

  48. 48.

    et al. Cerebellar modules operate at different frequencies. Elife 3, e02536 (2014).

  49. 49.

    & CLARITY for mapping the nervous system. Nat. Methods 10, 508–513 (2013).

  50. 50.

    et al. Cerebellar associative sensory learning defects in five mouse autism models. Elife 4, e06085 (2015).

Download references

Acknowledgements

The authors thank L. Lynch for expert laboratory assistance, A.C.H.G. Ijpelaar for technical assistance and Dr. H. Boele of the Neuroscience department at the Erasmus Medical Center for their input in eyeblink recordings, M.J. Berry II for discussion of the information calculation, D. Dombeck, J.P. Rickgauer and C. Domnisoru for experimental advice, and D. Pacheco-Pinedo, J.L. Verpeut, P. Sanchez-Jauregui and I. Witten for comments and suggestions. This work was supported by National Institutes of Health grants R01 NS045193 (S.W.) and R01 MH093727 (J.M.), New Jersey Council on Brain Injury Research fellowship CBIR12FEL031 (A.G.), the Searle Scholars program (J.M.), DARPA N66001-15-C-4032 (L.P.), National Science Foundation Graduate Research Fellowship DGE-1148900 (T.P.), the Nancy Lurie Marks Family Foundation (S.W.), the Netherlands Organization for Scientific Research (Innovational Research Incentives Scheme Veni; A.B. and Z.G.), the Dutch Fundamental Organization for Medical Sciences (ZonMW; C.I.D.Z.), Life Sciences (NWO-ALW; C.I.D.Z.) and Social and Behavioral Sciences (NWO-MAGW; C.I.D.Z.), as well as ERC-adv and ERC-POC (C.I.D.Z.).

Author information

Author notes

    • Farzaneh Najafi
    •  & Alexander D Kloth

    Present addresses: Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA (F.N.) and University of North Carolina, Chapel Hill, North Carolina, USA (A.D.K.).

    • Andrea Giovannucci
    •  & Aleksandra Badura

    These authors contributed equally to this work.

Affiliations

  1. Princeton Neuroscience Institute and Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.

    • Andrea Giovannucci
    • , Aleksandra Badura
    • , Ben Deverett
    • , Talmo D Pereira
    • , Ilker Ozden
    • , Alexander D Kloth
    •  & Samuel S-H Wang
  2. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA.

    • Andrea Giovannucci
    •  & Eftychios Pnevmatikakis
  3. Netherlands Institute for Neuroscience, Amsterdam, the Netherlands.

    • Aleksandra Badura
    •  & Chris I De Zeeuw
  4. Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.

    • Ben Deverett
  5. Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Farzaneh Najafi
  6. Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.

    • Zhenyu Gao
    •  & Chris I De Zeeuw
  7. School of Engineering, Brown University, Providence, Rhode Island, USA.

    • Ilker Ozden
  8. Departments of Statistics and Neuroscience, Columbia University, New York, New York, USA.

    • Eftychios Pnevmatikakis
    •  & Liam Paninski
  9. Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA.

    • Javier F Medina

Authors

  1. Search for Andrea Giovannucci in:

  2. Search for Aleksandra Badura in:

  3. Search for Ben Deverett in:

  4. Search for Farzaneh Najafi in:

  5. Search for Talmo D Pereira in:

  6. Search for Zhenyu Gao in:

  7. Search for Ilker Ozden in:

  8. Search for Alexander D Kloth in:

  9. Search for Eftychios Pnevmatikakis in:

  10. Search for Liam Paninski in:

  11. Search for Chris I De Zeeuw in:

  12. Search for Javier F Medina in:

  13. Search for Samuel S-H Wang in:

Contributions

A.G. designed behavioral and imaging experiments, established the behavioral and imaging setup and molecular methods, performed in vivo imaging experiments, developed analyses and drafted the manuscript. A.B., Z.G. and C.I.D.Z. designed and/or performed brain slice experiments. A.B. performed histological and combined behavioral-pharmacological experiments. F.N., I.O., B.D. and A.D.K. established the behavioral and imaging setup and methods. B.D. performed in vivo imaging experiments and developed analyses. T.D.P., E.P. and L.P. developed analyses. J.F.M. and S.S.-H.W. designed experiments and developed analyses. All authors edited the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Javier F Medina or Samuel S-H Wang.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8

  2. 2.

    Supplementary Methods Checklist

Videos

  1. 1.

    A B6.Cg-Tg(NeuroD1-Cre).GN135.Gsat mouse was injected with 200 nL of AAV1.CAG.Flex.GCaMP6f.WPRE.SV40.

    Spontaneous activity in parallel-fiber boutons of lobule VI was recorded in a mouse walking on a cylindrical treadmill. Images were acquired at 512 by 128 pixels (130×30 μm), 10 ms per frame. Playback speed, 20 Hz (5× slower than acquisition).

  2. 2.

    Granule cell identification by constrained matrix factorization.

    Raw Data. Original movie after motion correction (horizontal black bands arise from motion correction). The red circles indicate four granule cells identified by the factorization procedure. Denoised. Data with noise removed and synchronized neuropil activity retained. No Neuropil. Denoised movie with neuropil contribution to each pixel removed. Neuropil synchronized activity. Activity of the neuropil displayed separately from neurons. Representative spatial components. Time course of the four example granule cells. Raw data patches. Corresponding patches of raw data for the spatial components.

  3. 3.

    Wheel tracking corrects for perspective and estimates locomotor velocity.

    Left. A model of the wheel was constructed from physical measurements and manually fitted to the behavioral recording of the animal. Top-right. The parameters of the model defined a projective transform to remap the image pixels to the perspective of the surface normal of the wheel. Bottom-right. The tracked vertical displacement of the reprojected wheel pixels was rescaled to physical units to estimate the true instantaneous locomotor velocity.

  4. 4.

    Snout motion is quantified by tracking snout shape trajectories.

    Left. The outline of the shape of the snout was manually traced to provide a set of seed points that was displaced when the animal generated facial movements. Top-right. The images were thresholded and a robust point set registration algorithm was applied to track the trajectories of the seed points even when segmentation was made difficult by background artifacts. Bottom-right. The magnitude of the seed point velocities was used as a measure of facial movements.