Abstract
The cerebellum plays a major role in motor control. It is thought to mediate the acquisition of forward and inverse internal models of the bodyenvironment interaction [1]. In this study, the main processing components of the cerebellar microcomplex are modelled as a network of spiking neural populations. The model cerebellar circuit is shown to be suitable for learning both forward and inverse models. A new coupling scheme is put forth to optimise online adaptation and support offline learning. The proposed model is validated on two procedural tasks and the simulation results are consistent with data from human experiments on adaptive motor control and sleep-dependent consolidation [2, 3]. This work corroborates the hypothesis that both forward and inverse internal models can be learnt and stored by the same cerebellar circuit, and that their coupling favours online and offline learning of procedural memories.
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References
Ito, M.: The Cerebellum and Neural Control. Raven Press, New York (1984)
Walker, M.P., Stickgold, R.: Sleep-dependent learning and memory consolidation. Neuron 44(1), 121–133
Huber, R., Ghilardi, M.F., Massimini, M., Tononi, G.: Local sleep and learning. Nature 430(6995), 78–81
Kawato, M., Furukawa, K., Suzuki, R.: A hierarchical neural-network model for control and learning of voluntary movement. Biol. Cybern. 57, 169–185 (1987)
Kawato, M.: Internal models for motor control and trajectory planning. Curr. Opin. Neurobiol. 9, 718–727
Lalazar, H., Vaadia, E.: Neural basis of sensorimotor learning: modifying internal models. Current Opinion in Neurobiology 18(6), 573–581
Darlot, C., Zupan, L., Etard, O., Denise, P., Maruani, A.: Computation of inverse dynamics for the control of movements. Biological Cybernetics 75(2), 173–186
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 Neuroscience 9(11), 1404–1411
Dean, P., Porrill, J., Ekerot, C., Jorntell, H.: The cerebellar microcircuit as an adaptive filter: experimental and computational evidence. Nat. Rev. Neurosci. 11(1), 30–43 (2010)
Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11(7-8), 1317–1329
Kawato, M., Kuroda, T., Imamizu, H., Nakano, E., Miyauchi, S., Yoshioka, T.: Internal forward models in the cerebellum: fMRI study on grip force and load force coupling. Prog. Brain Res. 142, 171–188 (2003)
Stickgold, R.: Sleep-dependent memory consolidation. Nature 437(7063), 1272–1278
Ito, M.: Historical review of the significance of the cerebellum and the role of purkinje cells in motor learning. Ann. N. Y. Acad. Sci. 978, 273–288
Maquet, P., Schwartz, S., Passingham, R., Frith, C.: Sleep-related consolidation of a visuomotor skill: brain mechanisms as assessed by functional magnetic resonance imaging. The Journal of Neuroscience 23(4), 1432–1440
Ros, E., Carrillo, R., Ortigosa, E.M., Barbour, B., Agis, R.: Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics. Neural Computation 18(12), 2959–2993
Ito, M.: Cerebellar circuitry as a neuronal machine. Prog. Neurobiol. 78, 272–303 (2006)
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. PNAS 99(12), 8389–8393
Ito, M., Sakurai, M., Tongroach, P.: Climbing fibre induced depression of both mossy fibre responsiveness and glutamate sensitivity of cerebellar purkinje cells. The Journal of Physiology 324(1), 113–134 (1982)
Carrillo, R.R., Ros, E., Boucheny, C., Coenen, O.J.D.: A real-time spiking cerebellum model for learning robot control. Bio. Systems 94(1-2), 18–27 (2008); PMID: 18616974
Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, Cambridge
Viviani, P., Flash, T.: Minimum-jerk, two-thirds power law, and isochrony: converging approaches to movement planning. J. Exp. Psychol. Hum. Percept. Perform. 21, 32–53
Blakemore, S.J., Sirigu, A.: Action prediction in the cerebellum and in the parietal lobe. Exp. Brain Res. 153, 239–245
MacDonald, P.A., Paus, T.: The role of parietal cortex in awareness of self-generated movements: a transcranial magnetic stimulation study. Cereb. Cortex 13, 962–967
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Passot, JB., Luque, N., Arleo, A. (2010). Internal Models in the Cerebellum: A Coupling Scheme for Online and Offline Learning in Procedural Tasks. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, JA., Mouret, JB. (eds) From Animals to Animats 11. SAB 2010. Lecture Notes in Computer Science(), vol 6226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15193-4_41
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DOI: https://doi.org/10.1007/978-3-642-15193-4_41
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