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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/35875

Title: Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks
Authors: Casellato, Claudia
Antonietti, Alberto
Garrido Alcázar, Jesús
Ferrigno, Giancarlo
D'Angelo, Egidio
Pedrocchi, Alessandra
Issue Date: 2015
Abstract: The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.
Sponsorship: This work was supported by grants of European Union: REALNET (FP7-ICT270434) and Human Brain Project (HBP-604102).
Publisher: Frontiers Foundation
Keywords: Cerebellar model
Neurorobot
Motor learning
Distributed plasticity
Long term plasticity
URI: http://hdl.handle.net/10481/35875
ISSN: 1662-5188
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Casellato, C.; et al. Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks. Frontiers in Computational Neuroscience, 9:24 (2015). [http://hdl.handle.net/10481/35875]
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