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.

  • Review Article
  • Published:

The cerebellar microcircuit as an adaptive filter: experimental and computational evidence

Nature Reviews Neuroscience volume 11, pages 30–43 (2010)Cite this article

Key Points

  • The early characterization of the cerebellar cortical microcircuit gave rise to the influential Marr–Albus framework for modelling cerebellar function. This framework has been used in adaptive-filter form in many cerebellar models.

  • However, the initial characterization of the cerebellar microcircuitry in these models was far from complete, and the role of many microcircuit components was left unexplained. Subsequent technical advances have provided extensive new evidence concerning these components and their plasticity.

  • Here the authors relate these advances to recent developments in understanding the computational bases of cerebellar adaptive-filter models, and conclude that there are striking parallels between theory and experiment in the domains of symmetrical long-term potentiation and long-term depression, silent parallel fibre synapses, interneuron plasticity and recurrent mossy fibre connectivity.

  • This convergence encourages further collaboration between empirical and computational approaches in identifying the functional significance of features of the cerebellar microcircuit that are still poorly understood, in particular mossy fibre signalling and granular layer processing, and the nature of climbing fibre signals.

Abstract

Initial investigations of the cerebellar microcircuit inspired the Marr–Albus theoretical framework of cerebellar function. We review recent developments in the experimental understanding of cerebellar microcircuit characteristics and in the computational analysis of Marr–Albus models. We conclude that many Marr–Albus models are in effect adaptive filters, and that evidence for symmetrical long-term potentiation and long-term depression, interneuron plasticity, silent parallel fibre synapses and recurrent mossy fibre connectivity is strikingly congruent with predictions from adaptive-filter models of cerebellar function. This congruence suggests that insights from adaptive-filter theory might help to address outstanding issues of cerebellar function, including both microcircuit processing and extra-cerebellar connectivity.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: A simplified cerebellar microcircuit as an adaptive filter.
Figure 2: Updated view of the physiological wiring of the cerebellar cortical circuitry.
Figure 3: Learning dynamics with covariance learning rule.

Similar content being viewed by others

References

  1. Eccles, J. C., Ito, M. & Szentágothai, J. The Cerebellum as a Neuronal Machine (Springer, Berlin, 1967).

    Book  Google Scholar 

  2. Andersson, G. & Oscarsson, O. Climbing fiber microzones in cerebellar vermis and their projection to different groups of cells in lateral vestibular nucleus. Exp. Brain Res. 32, 565–579 (1978).

    CAS  PubMed  Google Scholar 

  3. Ekerot, C. F., Garwicz, M. & Schouenborg, J. Topography and nociceptive receptive fields of climbing fibres projecting to the cerebellar anterior lobe in the cat. J. Physiol. (Lond.) 441, 257–274 (1991).

    Article  CAS  Google Scholar 

  4. Apps, R. & Hawkes, R. Cerebellar cortical organization: a one-map hypothesis. Nature Rev. Neurosci. 10, 670–681 (2009).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  7. Dow, R. S. & Moruzzi, G. The Physiology and Pathology of the Cerebellum (Univ. Minnesota Press, Minneapolis, 1958).

    Google Scholar 

  8. Kettner, R. E. et al. Prediction of complex two-dimensional trajectories by a cerebellar model of smooth pursuit eye movement. J. Neurophysiol. 77, 2115–2130 (1997).

    Article  CAS  PubMed  Google Scholar 

  9. Yamamoto, K., Kobayashi, Y., Takemura, A., Kawano, K. & Kawato, M. Computational studies on acquisition and adaptation of ocular following responses based on cerebellar synaptic plasticity. J. Neurophysiol. 87, 1554–1571 (2002).

    Article  PubMed  Google Scholar 

  10. Schweighofer, N., Arbib, M. A. & Dominey, P. F. A model of the cerebellum in adaptive control of saccadic gain. 1. The model and its biological substrate. Biol. Cybern. 75, 19–28 (1996).

    Article  CAS  PubMed  Google Scholar 

  11. Ebadzadeh, M. & Darlot, C. Cerebellar learning of bio-mechanical functions of extra-ocular muscles: modeling by artificial neural networks. Neuroscience 122, 941–966 (2003).

    Article  CAS  PubMed  Google Scholar 

  12. Fujita, M. Simulation of adaptive modification of the vestibulo-ocular reflex with an adaptive filter model of the cerebellum. Biol. Cybern. 45, 207–214 (1982).

    Article  CAS  PubMed  Google Scholar 

  13. Gluck, M. A., Reifsnider, E. S. & Thompson, R. F. in Neuroscience and Connectionist Theory (eds Gluck, M. A. & Rumelhart, D. E.) 131–185 (Lawrence Erlbaum, Hillsdale, New Jersey, 1990).

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  15. Coenen, O. J. M. D. & Sejnowski, T. J. in Proceedings of the 3rd Joint Symposium on Neural Computation, Institute of Neural Computation 202–221 (Univ. California, San Diego, 1996).

    Google Scholar 

  16. Ito, M. Cerebellar learning in the vestibulo-ocular reflex. Trends Cogn. Sci. 2, 313–321 (1998).

    Article  CAS  PubMed  Google Scholar 

  17. Dean, P., Porrill, J. & Stone, J. V. Decorrelation control by the cerebellum achieves oculomotor plant compensation in simulated vestibulo-ocular reflex. Proc. R. Soc. Lond. B Biol. Sci. 269, 1895–1904 (2002). Highlights the computational advantages for a motor learning task of having cerebellar inputs carrying a motor efferent copy, and draws attention to the decorrelation nature of the algorithm.

    Article  Google Scholar 

  18. Porrill, J., Dean, P. & Stone, J. V. Recurrent cerebellar architecture solves the motor error problem. Proc. R. Soc. Lond. B Biol. Sci. 271, 789–796 (2004).

    Article  Google Scholar 

  19. Dean, P. & Porrill, J. Oculomotor anatomy and the motor-error problem: the role of the paramedian tract nuclei. Prog. Brain Res. 171, 177–186 (2008).

    Article  PubMed  Google Scholar 

  20. Haith, A. & Vijayakumar, S. Implications of different classes of sensorimotor disturbance for cerebellar-based motor learning models. Biol. Cybern. 100, 81–95 (2009).

    Article  PubMed  Google Scholar 

  21. Moore, J. W., Desmond, J. E. & Berthier, N. E. Adaptively timed conditioned responses and the cerebellum: a neural network approach. Biol. Cybern. 62, 17–28 (1989).

    Article  CAS  PubMed  Google Scholar 

  22. Bartha, G. T., Thompson, R. F. & Gluck, M. A. in Visual Structures and Integrated Functions (eds Arbib, M. & Ewert, J.-P.) 381–396 (Springer, Berlin, 1991).

    Book  Google Scholar 

  23. Bullock, D., Fiala, J. C. & Grossberg, S. A neural model of timed response learning in the cerebellum. Neural Netw. 7, 1101–1114 (1994).

    Article  Google Scholar 

  24. Medina, J. F., Garcia, K. S., Nores, W. L., Taylor, N. M. & Mauk, M. D. Timing mechanisms in the cerebellum: testing predictions of a large-scale computer simulation. J. Neurosci. 20, 5516–5525 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Medina, J. F. & Mauk, M. D. Computer simulation of cerebellar information processing. Nature Neurosci. 3, 1205–1211 (2000).

    Article  CAS  PubMed  Google Scholar 

  26. Medina, J. F., Nores, W. L. & Mauk, M. D. Inhibition of climbing fibres is a signal for the extinction of conditioned eyelid responses. Nature 416, 330–333 (2002).

    Article  CAS  PubMed  Google Scholar 

  27. Ohyama, T., Medina, J. F., Nores, W. L. & Mauk, M. D. Trying to understand the cerebellum well enough to build one. Ann. NY Acad. Sci. 978, 425–438 (2002).

    Article  PubMed  Google Scholar 

  28. Kawato, M. & Gomi, H. A computational model of four regions of the cerebellum based on feedback-error learning. Biol. Cybern. 68, 95–103 (1992). Introduced the feedback error learning model, an influential example of an adaptive-filter model that has been analysed in theoretical detail in subsequent papers.

    Article  CAS  PubMed  Google Scholar 

  29. Abbas, J. J. & Chizeck, H. J. Neural network control of functional neuromuscular stimulation systems: computer simulation studies. IEEE Trans. Biomed. Eng. 42, 1117–1127 (1995).

    Article  CAS  PubMed  Google Scholar 

  30. Contreras-Vidal, J. L., Grossberg, S. & Bullock, D. A neural model of cerebellar learning for arm movement control: cortico-spino-cerebellar dynamics. Learn. Mem. 3, 475–502 (1997).

    Article  CAS  PubMed  Google Scholar 

  31. Schweighofer, N., Arbib, M. A. & Kawato, M. Role of the cerebellum in reaching movements in humans. I. Distributed inverse dynamics control. Eur. J. Neurosci. 10, 86–94 (1998).

    Article  CAS  PubMed  Google Scholar 

  32. Schweighofer, N., Spoelstra, J., Arbib, M. A. & Kawato, M. Role of the cerebellum in reaching movements in humans. II. A neural model of the intermediate cerebellum. Eur. J. Neurosci. 10, 95–105 (1998).

    Article  CAS  PubMed  Google Scholar 

  33. Barto, A. G., Fagg, A. H., Sitkoff, N. & Houk, J. C. A cerebellar model of timing and prediction in the control of reaching. Neural Comput. 11, 565–594 (1999).

    Article  CAS  PubMed  Google Scholar 

  34. Spoelstra, J., Schweighofer, N. & Arbib, M. A. Cerebellar learning of accurate predictive control for fast-reaching movements. Biol. Cybern. 82, 321–333 (2000).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  36. Wolpert, D. M., Miall, R. C. & Kawato, M. Internal models in the cerebellum. Trends Cogn. Sci. 2, 338–347 (1998). An influential review that describes the evidence for and potential power of a cerebellum that can learn forward and inverse models of its environment.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  38. Ito, M. Bases and implications of learning in the cerebellum — adaptive control and internal model mechanism. Prog. Brain Res. 148, 95–109 (2005).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  40. Kawato, M. From 'Understanding the Brain by Creating the Brain' towards manipulative neuroscience. Philos. Trans. R Soc. Lond. B Biol. Sci. 363, 2201–2214 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ito, M. Control of mental activities by internal models in the cerebellum. Nature Rev. Neurosci. 9, 304–313 (2008).

    Article  CAS  Google Scholar 

  42. Paulin, M. G. in The Cerebellum and Cognition (ed. Schmahmann, J. D.) 515–533 (Academic, San Diego, 1997).

    Google Scholar 

  43. Paulin, M. G. Evolution of the cerebellum as a neuronal machine for Bayesian state estimation. J. Neural Eng. 2, S219–S234 (2005).

    Article  CAS  PubMed  Google Scholar 

  44. Miall, R. C., Weir, D. J., Wolpert, D. M. & Stein, J. F. Is the cerebellum a Smith predictor? J. Motor Behav. 25, 203–216 (1993).

    Article  CAS  Google Scholar 

  45. Fujita, M. Adaptive filter model of the cerebellum. Biol. Cybern. 45, 195–206 (1982). This paper extends the Marr–Albus formalism beyond its original interpretation as a pattern matcher, developing an adaptive-filter description that is much more relevant to continuous time problems such as motor control.

    Article  CAS  PubMed  Google Scholar 

  46. Ito, M. Neurophysiological aspects of the cerebellar motor control system. Int. J. Neurol. (Montevideo) 7, 162–176 (1970).

    CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  48. Sejnowski, T. J. Storing covariance with nonlinearly interacting neurons. J. Math. Biol. 4, 303–321 (1977). Introduces the computationally powerful covariance learning rule in which both LTP and LTD at a synapse are driven by correlations between inputs (or between inputs and outputs).

    Article  CAS  PubMed  Google Scholar 

  49. Eskiizmirliler, S., Forestier, N., Tondu, B. & Darlot, C. A model of the cerebellar pathways applied to the control of a single-joint robot arm actuated by McKibben artificial muscles. Biol. Cybern. 86, 379–394 (2002).

    Article  CAS  PubMed  Google Scholar 

  50. Dean, P., Porrill, J. & Stone, J. V. Visual awareness and the cerebellum: possible role of decorrelation control. Prog. Brain Res. 144, 61–75 (2004).

    Article  PubMed  Google Scholar 

  51. Nakanishi, J. & Schaal, S. Feedback error learning and nonlinear adaptive control. Neural Netw. 17, 1453–1465 (2004).

    Article  PubMed  Google Scholar 

  52. McKinstry, J. L., Edelman, G. M. & Krichmar, J. L. A cerebellar model for predictive motor control tested in a brain-based device. Proc. Natl Acad. Sci. USA 103, 3387–3392 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Guenthner, W., Glasauer, S., Wagner, P. & Ulbrich, H. Biomimetic control for adaptive camera stabilization in driver-assistance systems. J. Mech. Sci. Technol. 21, 930–934 (2007).

    Article  Google Scholar 

  54. Porrill, J. & Dean, P. Recurrent cerebellar loops simplify adaptive control of redundant and nonlinear motor systems. Neural Comput. 19, 170–193 (2007).

    Article  PubMed  Google Scholar 

  55. Carrillo, R. R., Ros, E., Boucheny, C. & Coenen, O. J. M. D. A real-time spiking cerebellum model for learning robot control. Biosystems 94, 18–27 (2008).

    Article  PubMed  Google Scholar 

  56. Miyamura, A. & Kimura, H. Stability of feedback error learning scheme. Syst. Control Lett. 45, 303–316 (2002).

    Article  Google Scholar 

  57. Porrill, J. & Dean, P. Cerebellar motor learning: when is cortical plasticity not enough? PloS Comput. Biol. 3, 1935–1950 (2007).

    Article  CAS  PubMed  Google Scholar 

  58. Porrill, J. & Dean, P. Silent synapses, LTP and the indirect parallel-fibre pathway: computational consequences of optimal noise processing. PloS Comput. Biol. 4, e1000085 (2008). Shows that the preponderance of silent synapses of PFs on PCs is a natural consequence of the optimality of the cerebellar learning rule with respect to noise inputs.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Dean, P. & Porrill, J. Adaptive filter models of the cerebellum: computational analysis. Cerebellum 7, 567–571 (2008).

    Article  PubMed  Google Scholar 

  60. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  62. Linden, D. J., Dickinson, M. H., Smeyne, M. & Connor, J. A. A long-term depression of AMPA currents in cultured cerebellar Purkinje neurons. Neuron 7, 81–89 (1991).

    Article  CAS  PubMed  Google Scholar 

  63. Ito, M. The Cerebellum and Neural Control (Raven, New York, 1984).

    Google Scholar 

  64. Ito, M. Cerebellar long-term depression: characterization, signal transduction, and functional roles. Physiol. Rev. 81, 1143–1195 (2001).

    Article  CAS  PubMed  Google Scholar 

  65. Ito, M. The molecular organization of cerebellar long-term depression. Nature Rev. Neurosci. 3, 896–902 (2002).

    Article  CAS  Google Scholar 

  66. Jörntell, H. & Hansel, C. Synaptic memories upside down: bidirectional plasticity at cerebellar parallel fiber-Purkinje cell synapses. Neuron 52, 227–238 (2006).

    Article  PubMed  CAS  Google Scholar 

  67. Sakurai, M. Synaptic modification of parallel fibre-Purkinje cell transmission in in vitro guinea-pig cerebellar slices. J. Physiol. 394, 463–480 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Salin, P. A., Malenka, R. C. & Nicoll, R. A. Cyclic AMP mediates a presynaptic form of LTP at cerebellar parallel fiber synapses. Neuron 16, 797–803 (1996).

    Article  CAS  PubMed  Google Scholar 

  69. Lev-Ram, V., Mehta, S. B., Kleinfeld, D. & Tsien, R. Y. Reversing cerebellar long-term depression. Proc. Natl Acad. Sci. USA 100, 15989–15993 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Lev-Ram, V., Wong, S. T., Storm, D. R. & Tsien, R. Y. A new form of cerebellar long-term potentiation is postsynaptic and depends on nitric oxide but not cAMP. Proc. Natl Acad. Sci. USA 99, 8389–8393 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 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). This study extended previous findings (references 69 and 70) of the existence of a postsynaptic form of PF–LTP in PCs, by showing that the postsynaptic forms of LTP and LTD in PCs are under CF control. This was an important indication that the PF plasticity in PCs adhered to the principles of the covariance learning rule.

    Article  CAS  PubMed  Google Scholar 

  72. Jörntell, H. & Ekerot, C. F. Reciprocal bidirectional plasticity of parallel fiber receptive fields in cerebellar Purkinje cells and their afferent interneurons. Neuron 34, 797–806 (2002). This paper tested predictions (in reference 85) that PF synaptic plasticity controlled by CFs is present not only in PCs but also in interneurons. The large receptive field changes obtained after appropriate stimulation indicated that these plasticity processes are potent in adults. It was also the first demonstration of PF plasticity in interneurons and potentiation of PF input in PCs in vivo.

    Article  PubMed  Google Scholar 

  73. Roberts, P. D. & Bell, C. C. Spike timing dependent synaptic plasticity in biological systems. Biol. Cybern. 87, 392–403 (2002).

    Article  PubMed  Google Scholar 

  74. Safo, P. & Regehr, W. G. Timing dependence of the induction of cerebellar LTD. Neuropharmacology 54, 213–218 (2008).

    Article  CAS  PubMed  Google Scholar 

  75. Sugihara, I., Wu, H. & Shinoda, Y. Morphology of single olivocerebellar axons labeled with biotinylated dextran amine in the rat. J. Comp. Neurol. 414, 131–148 (1999).

    Article  CAS  PubMed  Google Scholar 

  76. Jörntell, H. & Ekerot, C. F. Receptive field plasticity profoundly alters the cutaneous parallel fiber synaptic input to cerebellar interneurons in vivo. J. Neurosci. 23, 9620–9631 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Szapiro, G. & Barbour, B. Multiple climbing fibers signal to molecular layer interneurons exclusively via glutamate spillover. Nature Neurosci. 10, 735–742 (2007). A thorough examination of the CF responses in molecular-layer interneurons in vitro , corroborating previous in vivo demonstrations of this input. This paper showed that the CF input evokes an NMDA-dependent response in the interneuron, which could be the mechanism behind the CF dependency of its PF plasticity.

    Article  CAS  PubMed  Google Scholar 

  78. Liu, S. Q. & Cull-Candy, S. G. Synaptic activity at calcium-permeable AMPA receptors induces a switch in receptor subtype. Nature 405, 454–458 (2000).

    Article  CAS  PubMed  Google Scholar 

  79. Liu, S. J. & Cull-Candy, S. G. Activity-dependent change in AMPA receptor properties in cerebellar stellate cells. J. Neurosci. 22, 3881–3889 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Rancillac, A. & Crepel, F. Synapses between parallel fibres and stellate cells express long-term changes in synaptic efficacy in rat cerebellum. J. Physiol. (Lond.) 554, 707–720 (2004).

    Article  CAS  Google Scholar 

  81. Liu, S. J. & Cull-Candy, S. G. Subunit interaction with PICK and GRIP controls Ca2+ permeability of AMPARs at cerebellar synapses. Nature Neurosci. 8, 768–775 (2005).

    Article  CAS  PubMed  Google Scholar 

  82. Smith, S. L. & Otis, T. S. Pattern-dependent, simultaneous plasticity differentially transforms the input-output relationship of a feedforward circuit. Proc. Natl Acad. Sci. USA 102, 14901–14906 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Soler-Llavina, G. J. & Sabatini, B. L. Synapse-specific plasticity and compartmentalized signaling in cerebellar stellate cells. Nature Neurosci. 9, 798–806 (2006).

    Article  CAS  PubMed  Google Scholar 

  84. Wulff, P. et al. Synaptic inhibition of Purkinje cells mediates consolidation of vestibulo-cerebellar motor learning. Nature Neurosci. 8, 1042–1049 (2009).

    Article  CAS  Google Scholar 

  85. Ekerot, C. F. & Jörntell, H. Parallel fibre receptive fields of Purkinje cells and interneurons are climbing fibre-specific. Eur. J. Neurosci. 13, 1303–1310 (2001).

    Article  CAS  PubMed  Google Scholar 

  86. Barmack, N. H. & Yakhnitsa, V. Functions of interneurons in mouse cerebellum. J. Neurosci. 28, 1140–1152 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Gao, W., Chen, G., Reinert, K. C. & Ebner, T. J. Cerebellar cortical molecular layer inhibition is organized in parasagittal zones. J. Neurosci. 26, 8377–8387 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Sultan, F. & Bower, J. M. Quantitative Golgi study of the rat cerebellar molecular layer interneurons using principal component analysis. J. Comp. Neurol. 393, 353–373 (1998).

    Article  CAS  PubMed  Google Scholar 

  89. Kano, M., Rexhausen, U., Dreessen, J. & Konnerth, A. Synaptic excitation produces a long-lasting rebound potentiation of inhibitory synaptic signals in cerebellar Purkinje cells. Nature 356, 601–604 (1992).

    Article  CAS  PubMed  Google Scholar 

  90. Mittmann, W. & Hausser, M. Linking synaptic plasticity and spike output at excitatory and inhibitory synapses onto cerebellar Purkinje cells. J. Neurosci. 27, 5559–5570 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Kerchner, G. A. & Nicoll, R. A. Silent synapses and the emergence of a postsynaptic mechanism for LTP. Nature Rev. Neurosci. 9, 813–825 (2008).

    Article  CAS  Google Scholar 

  92. Wang, S. S., Khiroug, L. & Augustine, G. J. Quantification of spread of cerebellar long-term depression with chemical two-photon uncaging of glutamate. Proc. Natl Acad. Sci. USA 97, 8635–8640 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Isope, P. & Barbour, B. Properties of unitary granule cell - > Purkinje cell synapses in adult rat cerebellar slices. J. Neurosci. 22, 9668–9678 (2002). One of the most convincing experimental demonstrations of the presence of silent synapses in the brain. Taking advantage of the fact that all granule cell axons or PFs that pass a PC make synapses with it, these authors made simultaneous patch-clamp recordings from a postsynaptic cell and its afferent (presynaptic) cells to show that most of these granule cell inputs are silent in the adult cerebellar cortex.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Garwicz, M., Jorntell, H. & Ekerot, C. F. Cutaneous receptive fields and topography of mossy fibres and climbing fibres projecting to cat cerebellar C3 zone. J. Physiol. (Lond.) 512, 277–293 (1998).

    Article  CAS  Google Scholar 

  95. Jörntell, H. & Ekerot, C. F. Properties of somatosensory synaptic integration in cerebellar granule cells in vivo. J. Neurosci. 26, 11786–11797 (2006). This paper turned many theoretical assumptions about the role of the granule cells in vivo upside down: granule cells were shown to have a substantial, MF-driven spontaneous activity (for granule cells with some types of input), exhibiting sustained firing frequencies of more than 500 Hz and integrating inputs from similar rather than dissimilar sources.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  96. Gilbert, P. F. Theories of motor learning and the cerebellum. Trends Neurosci. 16, 177–178 (1993).

    Article  CAS  PubMed  Google Scholar 

  97. Kelly, R. M. & Strick, P. L. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J. Neurosci. 23, 8432–8444 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Ramnani, N. The primate cortico-cerebellar system: anatomy and function. Nature Rev. Neurosci. 7, 511–522 (2006).

    Article  CAS  Google Scholar 

  99. Glickstein, M. & Yeo, C. The cerebellum and motor learning. J. Cogn. Neurosci. 2, 69–80 (1990).

    Article  CAS  PubMed  Google Scholar 

  100. Glickstein, M. Cerebellar agenesis. Brain 117, 1209–1212 (1994).

    Article  PubMed  Google Scholar 

  101. Ohtsuka, K. & Noda, H. Burst discharges of mossy fibers in the oculomotor vermis of macaque monkeys during saccadic eye movements. Neurosci. Res. 15, 102–114 (1992).

    Article  CAS  PubMed  Google Scholar 

  102. Prsa, M., Dash, S., Catz, N., Dicke, P. W. & Thier, P. Characteristics of responses of Golgi cells and mossy fibers to eye saccades and saccadic adaptation recorded from the posterior vermis of the cerebellum. J. Neurosci. 29, 250–262 (2009). A demonstration that in the awake animal, even a relatively simple behaviour is associated with a vast diversity of signalling in MFs. The findings suggest that the diversity of MF responses, and its impact on cerebellar information processing and function, might have been underestimated in models of cerebellar microcircuit computations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Keifer, J. & Houk, J. C. Motor function of the cerebellorubrospinal system. Physiol. Rev. 74, 509–542 (1994).

    Article  CAS  PubMed  Google Scholar 

  104. Oscarsson, O. in Handbook of Sensory Physiology Vol II: Sensory System (ed. Iggo, A.) 339–380 (Springer, New York, 1973).

    Google Scholar 

  105. Bower, J. M. & Parsons, L. M. Rethinking the 'lesser brain'. Sci. Am. 289, 40–47 (2003).

    Article  Google Scholar 

  106. Wu, H., Sugihara, I. & Shinoda, Y. Projection patterns of single mossy fibers originating from the lateral reticular nucleus in the rat cerebellar cortex and nuclei. J. Comp. Neurol. 16, 97–118 (1999).

    Article  Google Scholar 

  107. Chadderton, P. T., Margrie, T. W. & Hausser, M. Integration of quanta in cerebellar granule cells during sensory processing. Nature 428, 856–860 (2004).

    Article  CAS  PubMed  Google Scholar 

  108. Rancz, E. A. et al. High-fidelity transmission of sensory information by single cerebellar mossy fibre boutons. Nature 450, 1245–1248 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Arenz, A., Silver, R. A., Schaefer, A. T. & Margrie, T. W. The contribution of single synapses to sensory representation in vivo. Science 321, 977–980 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Bengtsson, F. & Jorntell, H. Sensory transmission in cerebellar granule cells relies on similarly coded mossy fiber inputs. Proc. Natl Acad. Sci. USA 106, 2389–2394 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. D'Angelo, E., De Filippi, G., Rossi, P. & Taglietti, V. Ionic mechanism of electroresponsiveness in cerebellar granule cells implicates the action of a persistent sodium current. J. Neurophysiol. 80, 493–503 (1998).

    Article  CAS  PubMed  Google Scholar 

  112. Matsushita, M. & Yaginuma, H. Spinocerebellar projections from spinal border cells in the cat as studied by anterograde transport of wheat germ agglutinin-horseradish peroxidase. J. Comp. Neurol. 288, 19–38 (1989).

    Article  CAS  PubMed  Google Scholar 

  113. Yaginuma, H. & Matsushita, M. Spinocerebellar projections from the upper lumbar segments in the cat, as studied by anterograde transport of wheat germ agglutinin-horseradish peroxidase. J. Comp. Neurol. 281, 298–319 (1989).

    Article  CAS  PubMed  Google Scholar 

  114. Matsushita, M. Spinocerebellar projections from the lowest lumbar and sacral-caudal segments in the cat, as studied by anterograde transport of wheat germ agglutinin-horseradish peroxidase. J.Comp. Neurol. 274, 239–254 (1988).

    Article  CAS  PubMed  Google Scholar 

  115. Matsushita, M. & Tanami, T. Spinocerebellar projections from the central cervical nucleus in the cat, as studied by anterograde transport of wheat germ agglutinin-horseradish peroxidase. J. Comp. Neurol. 266, 376–397 (1987).

    Article  CAS  PubMed  Google Scholar 

  116. Matsushita, M. & Ikeda, M. Spinocerebellar projections from the cervical enlargement in the cat, as studied by anterograde transport of wheat germ agglutinin-horseradish peroxidase. J. Comp. Neurol. 263, 223–240 (1987).

    Article  CAS  PubMed  Google Scholar 

  117. Yaginuma, H. & Matsushita, M. Spinocerebellar projections from the thoracic cord in the cat, as studied by anterograde transport of wheat germ agglutinin-horseradish peroxidase. J. Comp. Neurol. 258, 1–27 (1987).

    Article  CAS  PubMed  Google Scholar 

  118. Ikeda, M. & Matsushita, M. Trigeminocerebellar projections to the posterior lobe in the cat, as studied by anterograde transport of wheat germ agglutinin-horseradish peroxidase. J. Comp. Neurol. 316, 221–237 (1992).

    Article  CAS  PubMed  Google Scholar 

  119. Matsushita, M., Tanami, T. & Yaginuma, H. Differential distribution of spinocerebellar fiber terminals within the lobules of the cerebellar anterior lobe in the cat: an anterograde WGA-HRP study. Brain Res. 305, 157–161 (1984).

    Article  CAS  PubMed  Google Scholar 

  120. Matsushita, M. & Hosoya, Y. Spinocerebellar projections to lobules III to V of the anterior lobe in the cat, as studied by retrograde transport of horseradish peroxidase. J. Comp. Neurol. 208, 127–143 (1982).

    Article  CAS  PubMed  Google Scholar 

  121. Matsushita, M. & Okado, N. Spinocerebellar projections to lobules I and II of the anterior lobe in the cat, as studied by retrograde transport of horseradish peroxidase. J. Comp. Neurol. 197, 411–424 (1981).

    Article  CAS  PubMed  Google Scholar 

  122. Miles, F. A., Fuller, J. H., Braitman, D. J. & Dow, B. M. Long-term adaptive changes in primate vestibuloocular reflex. III. Electrophysiological observations in flocculus of normal monkeys. J. Neurophysiol. 43, 1437–1476 (1980).

    Article  CAS  PubMed  Google Scholar 

  123. Holtzman, T., Mostofi, A., Phuah, C. L. & Edgley, S. A. Cerebellar Golgi cells in the rat receive multimodal convergent peripheral inputs via the lateral funiculus of the spinal cord. J. Physiol. (Lond.) 577, 69–80 (2006).

    Article  CAS  Google Scholar 

  124. Holtzman, T., Rajapaksa, T., Mostofi, A. & Edgley, S. A. Different responses of rat cerebellar Purkinje cells and Golgi cells evoked by widespread convergent sensory inputs. J. Physiol. (Lond.) 574, 491–507 (2006).

    Article  CAS  Google Scholar 

  125. Vos, B. P., Volny-Luraghi, A. & De Schutter, E. Cerebellar Golgi cells in the rat: receptive fields and timing of responses to facial stimulation. Eur. J. Neurosci. 11, 2621–2634 (1999).

    Article  CAS  PubMed  Google Scholar 

  126. Jirenhed, D. A., Bengtsson, F. & Hesslow, G. Acquisition, extinction, and reacquisition of a cerebellar cortical memory trace. J. Neurosci. 27, 2493–2502 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Kalmbach, B. E., Ohyama, T., Kreider, J. C., Riusech, F. & Mauk, M. D. Interactions between prefrontal cortex and cerebellum revealed by trace eyelid conditioning. Learn. Mem. 16, 86–95 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Coenen, O. J.-M. D., Arnold, M. P., Sejnowski, T. J. & Jabri, M. A. Parallel fiber coding in the cerebellum for life-long learning. Auton. Robots 11, 291–297 (2001).

    Article  Google Scholar 

  129. Schweighofer, N., Doya, K. & Lay, F. Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control. Neuroscience 103, 35–50 (2001).

    Article  CAS  PubMed  Google Scholar 

  130. Philipona, D., O'Regan, J. K., Nadal, J. P. & Coenen, O. J.-M. D. Perception of the structure of the physical world using unknown multimodal sensors and effectors. Adv. Neural Inf. Process. Syst. 16, 945–952 (2004).

    Google Scholar 

  131. Yamazaki, T. & Tanaka, S. A spiking network model for passage-of-time representation in the cerebellum. Eur. J. Neurosci. 26, 2279–2292 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Kanichay, R. T. & Silver, R. A. Synaptic and cellular properties of the feedforward inhibitory circuit within the input layer of the cerebellar cortex. J. Neurosci. 28, 8955–8967 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. D'Angelo, E. & De Zeeuw, C. I. Timing and plasticity in the cerebellum: focus on the granular layer. Trends Neurosci. 32, 30–40 (2009).

    Article  CAS  PubMed  Google Scholar 

  134. Imamizu, H. et al. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature 403, 192–195 (2000).

    Article  CAS  PubMed  Google Scholar 

  135. Imamizu, H., Kuroda, T., Miyauchi, S., Yoshioka, T. & Kawato, M. Modular organization of internal models of tools in the human cerebellum. Proc. Natl Acad. Sci. USA 100, 5461–5466 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  137. Widrow, B. & Stearns, S. D. Adaptive Signal Processing (Prentice-Hall, Engelwood Cliffs, 1985).

    Google Scholar 

  138. Ito, M. Movement and thought: identical control mechanisms by the cerebellum. Trends Neurosci. 16, 448–450 (1993).

    Article  CAS  PubMed  Google Scholar 

  139. Boyden, E. S., Katoh, A. & Raymond, J. L. Cerebellum-dependent learning: the role of multiple plasticity mechanisms. Annu. Rev. Neurosci. 27, 581–609 (2004).

    Article  CAS  PubMed  Google Scholar 

  140. Kehoe, E. J. Repeated acquisitions and extinctions in classical conditioning of the rabbit nictitating membrane response. Learn. Mem. 13, 366–375 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Koekkoek, S. K. et al. Cerebellar LTD and learning-dependent timing of conditioned eyelid responses. Science 301, 1736–1739 (2003).

    Article  CAS  PubMed  Google Scholar 

  142. Hansel, C. et al. αCaMKII is essential for cerebellar LTD and motor learning. Neuron 51, 835–843 (2006).

    Article  CAS  PubMed  Google Scholar 

  143. Gilbert, P. F. C. & Thach, W. T. Purkinje cell activity during motor learning. Brain Res. 128, 309–328 (1977).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  145. Winkelman, B. & Frens, M. Motor coding in floccular climbing fibers. J. Neurophysiol. 95, 2342–2351 (2006).

    Article  PubMed  Google Scholar 

  146. Brickley, S. G., Cull-Candy, S. G. & Farrant, M. Development of a tonic form of synaptic inhibition in rat cerebellar granule cells resulting from persistent activation of GABAA receptors. J. Physiol. 497, 753–759 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Wall, M. J. & Usowicz, M. M. Development of action potential-dependent and independent spontaneous GABAA receptor-mediated currents in granule cells of postnatal rat cerebellum. Eur. J. Neurosci. 9, 533–548 (1997).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  149. Manni, E. & Petrosini, L. A century of cerebellar somatotopy: a debated representation. Nature Rev. Neurosci. 5, 241–249 (2004).

    Article  CAS  Google Scholar 

  150. Palkovits, M., Magyar, P. & Szentágothai, J. Quantitative histological analysis of the cerebellar cortex in the cat. I. Number and arrangement in space of Purkinje cells. Brain Res. 32, 1–13 (1971).

    Article  CAS  PubMed  Google Scholar 

  151. Diño, M. R., Willard, F. H. & Mugnaini, E. Distribution of unipolar brush cells and other calretinin immunoreactive components in the mammalian cerebellar cortex. J. Neurocytol. 28, 99–123 (1999).

    Article  PubMed  Google Scholar 

  152. Stuart, G. & Hausser, M. Initiation and spread of sodium action potentials in cerebellar Purkinje cells. Neuron 13, 703–712 (1994).

    Article  CAS  PubMed  Google Scholar 

  153. Walter, J. T. & Khodakhah, K. The linear computational algorithm of cerebellar Purkinje cells. J. Neurosci. 26, 12861–12872 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Steuber, V. et al. Cerebellar LTD and pattern recognition by Purkinje cells. Neuron 54, 121–136 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Rokni, D., Llinas, R. & Yarom, Y. The morpho/functional discrepancy in the cerebellar cortex: looks alone are deceptive. Front. Neurosci. 2, 192–198 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  156. Bengtsson, F. & Jorntell, H. Ketamine and xylazine depress sensory-evoked parallel fiber and climbing fiber responses. J. Neurophysiol. 98, 1697–1705 (2007).

    Article  CAS  PubMed  Google Scholar 

  157. Sims, R. E. & Hartell, N. A. Differences in transmission properties and susceptibility to long-term depression reveal functional specialization of ascending axon and parallel fiber synapses to Purkinje cells. J. Neurosci. 25, 3246–3257 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Walter, J. T., Dizon, M. J. & Khodakhah, K. The functional equivalence of ascending and parallel fiber inputs in cerebellar computation. J. Neurosci. 29, 8462–8473 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Loewenstein, Y. et al. Bistability of cerebellar Purkinje cells modulated by sensory stimulation. Nature Neurosci. 8, 202–211 (2005).

    Article  CAS  PubMed  Google Scholar 

  160. Schonewille, M. et al. Purkinje cells in awake behaving animals operate at the upstate membrane potential. Nature Neurosci. 9, 459–461 (2006).

    Article  CAS  PubMed  Google Scholar 

  161. Armstrong, D. M. & Edgley, S. A. Discharges of interpositus and Purkinje cells of the cat cerebellum during locomotion under different conditions. J. Physiol. (Lond.) 400, 425–445 (1988).

    Article  CAS  Google Scholar 

  162. Yamamoto, K., Kobayashi, Y., Takemura, A., Kawano, K. & Kawato, M. A mathematical analysis of the characteristics of the system connecting the cerebellar ventral paraflocculus and extraoculomotor nucleus of alert monkeys during upward ocular following responses. Neurosci. Res. 38, 425–435 (2000).

    Article  CAS  PubMed  Google Scholar 

  163. Hoebeek, F. E. et al. Increased noise level of Purkinje cell activities minimizes impact of their modulation during sensorimotor control. Neuron 45, 953–965 (2005).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Norris, S. A., Greger, B., Hathaway, E. N. & Thach, W. T. Purkinje cell spike firing in the posterolateral cerebellum: correlation with visual stimulus, oculomotor response, and error feedback. J. Neurophysiol. 92, 1867–1879 (2004).

    Article  PubMed  Google Scholar 

  166. Yamamoto, K., Kawato, M., Kotosaka, S. & Kitazawa, S. Encoding of movement dynamics by Purkinje cell simple spike activity during fast arm movements under resistive and assistive force fields. J. Neurophysiol. 97, 1588–1599 (2007).

    Article  PubMed  Google Scholar 

  167. Gomi, H. et al. Temporal firing patterns of Purkinje cells in the cerebellar ventral paraflocculus during ocular following responses in monkeys I. Simple spikes. J. Neurophysiol. 80, 818–831 (1998).

    Article  CAS  PubMed  Google Scholar 

  168. Coltz, J. D., Johnson, M. T. V. & 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 

  169. Ghelarducci, B., Ito, M. & Yagi, N. Impulse discharges from flocculus Purkinje cells of alert rabbits during visual stimulation combined with horizontal head rotation. Brain Res. 87, 66–72 (1975).

    Article  CAS  PubMed  Google Scholar 

  170. Yartsev, M. M., Givon-Mayo, R., Maller, M. & Donchin, O. Pausing Purkinje cells in the cerebellum of the awake cat. Front. Syst. Neurosci. 3, 1–9 (2009).

    Article  Google Scholar 

  171. Atkeson, C. G. et al. Using humanoid robots to study human behavior. IEEE Intell. Syst. Appl. 15, 46–55 (2000).

    Article  Google Scholar 

  172. Bower, J. M. & Beeman, D. (eds) The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System (Springer, New York, 1994).

    Google Scholar 

  173. De Schutter, E. & Bower, J. M. An active membrane model of the cerebellar Purkinje cell I. Simulation of current clamps in slice. J. Neurophysiol. 71, 375–400 (1994).

    Article  CAS  PubMed  Google Scholar 

  174. Gleeson, P., Steuber, V. & Silver, R. A. neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54, 219–235 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Dean, P., Mayhew, J. E. W. & Langdon, P. Learning and maintaining saccadic accuracy: a model of brainstem-cerebellar interactions. J. Cogn. Neurosci. 6, 117–138 (1994).

    Article  CAS  PubMed  Google Scholar 

  176. Jacobson, G. A., Rokni, D. & Yarom, Y. A model of the olivo-cerebellar system as a temporal pattern generator. Trends Neurosci. 31, 617–625 (2008).

    Article  CAS  PubMed  Google Scholar 

  177. Llinás, R. R., Leznik, E. & Makarenko, V. I. The olivo-cerebellar circuit as a universal motor control system. IEEE J. Oceanic Eng. 29, 631–639 (2004).

    Article  Google Scholar 

  178. Bandyopadhyay, P. R. et al. Synchronization of animal-inspired multiple high-lift fins in an underwater vehicle using olivo-cerebellar dynamics. IEEE J. Oceanic Eng. 33, 563–578 (2009).

    Article  Google Scholar 

  179. Gomi, H. & Kawato, M. Adaptive feedback control models of the vestibulocerebellum and spinocerebellum. Biol. Cybern. 68, 105–114 (1992).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The support of the Biotechnology and Biological Research Council (United Kingdom), the Engineering and Physical Sciences Research Council (United Kingdom), EU FP6 (IST-028,056-SENSOPAC) and the Swedish Medical Research Council is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Psychology, University of Sheffield, Western Bank, Sheffield, S10 2TP, UK

    Paul Dean & John Porrill

  2. Department of Experimental Medical Sciences, Section for Neurophysiology, Lund University, The Biomedical Center F10, Tornavögen 10, Lund, SE-221 84, Sweden

    Carl-Fredrik Ekerot & Henrik Jörntell

Authors
  1. Paul Dean
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Porrill
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carl-Fredrik Ekerot
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Henrik Jörntell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henrik Jörntell.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information S1 (box)

Control architectures and the adaptive filter (PDF 415 kb)

Supplementary information S2 (box)

Adaptive-Filter details (PDF 1232 kb)

Supplementary information S3 (box)

Reduced preparations (PDF 279 kb)

Supplementary information S4 (box)

Granular layer (PDF 417 kb)

Supplementary information S5 (box)

Alternative models of cerebellar microcircuitry (PDF 230 kb)

Related links

Related links

FURTHER INFORMATION

Paul Dean and John Porrill's homepage

Glossary

Purkinje cell

By far the largest neuron of the cerebellum and the sole output of the cerebellar cortex. Receives climbing fibre input and integrates inputs from parallel fibres and interneurons.

Climbing fibre

(CF). Arises from cells in the inferior olive and provides an extraordinarily strong, 'climbing' multi-synaptic contact on Purkinje cells. However, branches of the olivocerebellar axon contact not only Purkinje cells but also other neuron types of the cerebellum. In the latter cases, there is no 'climbing' pattern in the anatomical configuration of the contacts. Nevertheless, for convenience, the input from the olivocerebellar axons to the interneurons is referred to as 'CF' input in the text.

Mossy fibre

(MF). Provides the bulk of the afferent input to the cerebellum and originates from numerous sources in the spinal cord, brain stem and pontine nuclei (the latter mediating input from the cerebral cortex).

Granule cell

Integrates excitatory mossy fibre input from external sources and local inhibitory input from Golgi cells.

Parallel fibre

(PF). Arises from granule cells and provides excitatory input to Purkinje cells and molecular layer interneurons.

Microzone

A narrow longitudinal strip of the cerebellar cortex, just a few Purkinje cells wide but up to hundreds of Purkinje cells long, in which all the Purkinje cells receive climbing fibres driven by the same input.

Vestibulo-ocular reflex

Reflex movement of the eyes elicited by vestibular stimulation. Its purpose is to keep the retinal image stable, preventing degradation of visual processing. The reflex is under the control of the floccular region of the cerebellum.

Engineering control theory

A branch of engineering science concerned with the control of dynamic systems (including aircraft, chemical reactions and robots).

Silent synapses

Synapses that can be structurally identified but which provide no synaptic currents in the postsynaptic cell.

Golgi cell

Inhibitory interneurons in the granular layer that synapse with granule cells. They receive excitatory input from mossy fibres and parallel fibres.

Glutamate uncaging

The process by which chemically caged glutamate can be released by focal light. It is used to study the effects of postsynaptic activation with high temporal and spatial control.

Beam

A bundle of parallel fibres (PFs). A term typically applied to experiments using electrical stimulation of PFs in which the local population of PFs around the stimulating electrode is activated.

Image slip

Movement of the entire image across the retina, usually produced by movement of the eyes.

Distal error problem

The natural error signal for learning motor commands is the difference between actual and correct commands ('motor error'). However, in autonomous systems the correct command is typically unknown; only information about the sensory consequences of incorrect commands is available, such as the position of a pointing finger relative to a target ('distal error'). How to use this information to drive motor learning is the distal error (or motor error) problem.

Coincidence detector

A neuron that acts as a coincidence detector responds only when two or more of its synaptic inputs are activated together.

Hysteresis

A system has hysteresis when its current behaviour depends on its history. An example of hysteresis in learning is the phenomenon of savings, in which relearning takes place much more quickly than first-time learning.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dean, P., Porrill, J., Ekerot, CF. et al. The cerebellar microcircuit as an adaptive filter: experimental and computational evidence. Nat Rev Neurosci 11, 30–43 (2010). https://doi.org/10.1038/nrn2756

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrn2756

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