Abstract
Learning mechanisms inspired by the animal cerebellum have shown promising achievements in artificial motor adaptation, mainly by focusing on the computation performed in the molecular layer. Other sites of cerebellar plasticity however are less explored whereas their understanding could contribute to improved computational solutions. In this study, we address the advantages of a form of plasticity found in the glomerulus, thought to control the temporal gating dynamics of the cerebellar pontine input. We explore this hypothesis from a system-level perspective within a simulated robotic rejection task, by implementing a model of the cerebellar microcircuit where adaptation of the input transformation dynamics, accounting for glomerular information processing, is controlled by a cost function. Our results suggest that glomerular adaptation (1) improves motor learning by adjusting input signal transformation properties towards an optimal configuration and shaping time and magnitude of the cerebellar output, and (2) contributes to fast readaptation during sudden plant perturbations. Finally, we discuss the implications of our results from a neuroscientific and articifical control perspective.
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References
Albus, J.S.: A theory of cerebellar function. Math. Biosci. 10(1–2), 25–61 (1971)
Crowley, J.J., Fioravante, D., Regehr, W.G.: Dynamics of fast and slow inhibition from cerebellar golgi cells allow flexible control of synaptic integration. Neuron 63(6), 843–853 (2009)
De Schutter, E., Bjaalie, J.G.: Coding in the granular layer of the cerebellum. Prog. Brain Res. 130, 279–296 (2001)
Dean, P., Porrill, J., Ekerot, C.F., Jörntell, H.: The cerebellar microcircuit as an adaptive filter: experimental and computational evidence. Nat. Rev. Neurosci. 11(1), 30–43 (2010)
Garrido, J.A., Luque, N.R., D’Angelo, E., Ros, E.: Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation. Front. Neural Circ. 7, 159 (2013)
Herreros, I., Maffei, G., Brandi, S., Sanchez-Fibla, M., Verschure, P.: Speed generalization capabilities of a cerebellar model on a rapid navigation task. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 363–368. IEEE (2013)
Herreros, I., Verschure, P.F.: Nucleo-olivary inhibition balances the interaction between the reactive and adaptive layers in motor control. Neural Netw. 47, 64–71 (2013)
Maffei, G., Sanchez-fibla, M., Herreros, I., Paul, F.M.J.: Acquisition of synergistic motor responses through cerebellar learning in a robotic postural task. In: Biomimetic and Biohybrid Systems (2014)
Maffei, G., Santos-Pata, D., Marcos, E., Sánchez-Fibla, M., Verschure, P.: An embodied biologically constrained model of foraging: from classical and operant conditioning to adaptive real-world behavior in DAC-X. Neural Netw. 72, 88–108 (2015)
Nieus, T.: LTP regulates burst initiation and frequency at mossy fiber-granule cell synapses of rat cerebellum: experimental observations and theoretical predictions. J. Neurophysiol. 95(2), 686–699 (2005)
Nori, F., Traversaro, S., Eljaik, J., Romano, F., Del Prete, A., Pucci, D.: iCub whole-body control through force regulation on rigid non-coplanar contacts. Front. Robot. AI 2, 6 (2015)
Schweighofer, N., Doya, K., Lay, F.: Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control. Neuroscience 103(1), 35–50 (2001)
Shadmehr, R., Smith, M.A., Krakauer, J.W.: Error correction, sensory prediction, and adaptation in motor control. Ann. Rev. Neurosci. 33, 89–108 (2010)
Timmann, D., Horak, F.B.: Perturbed step initiation in cerebellar subjects: 2. Modification of anticipatory postural adjustments. Exp. Brain Res. 141(1), 110–120 (2010)
Wilson, E.D., Assaf, T., Pearson, M.J., Rossiter, J.M., Anderson, S.R., Porrill, J.: Bioinspired adaptive control for artificial muscles. In: Lepora, N.F., Mura, A., Krapp, H.G., Verschure, P.F.M.J., Prescott, T.J. (eds.) Living Machines 2013. LNCS, vol. 8064, pp. 311–322. Springer, Heidelberg (2013)
Xu, W., Edgley, S.A.: Climbing fibre-dependent changes in golgi cell responses to peripheral stimulation. J. Physiol. 586(20), 4951–4959 (2008)
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This work was supported by the European Commission’s Horizon 2020 socSMC under agreement number: socSMC-641321H2020-FETPROACT-2014.
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Maffei, G., Herreros, I., Sanchez-Fibla, M., Verschure, P.F.M.J. (2016). Plasticity in the Granular Layer Enhances Motor Learning in a Computational Model of the Cerebellum. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_32
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DOI: https://doi.org/10.1007/978-3-319-44778-0_32
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