Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks Casellato, Claudia Antonietti, Alberto Garrido Alcázar, Jesús Alberto Ferrigno, Giancarlo D'Angelo, Egidio Pedrocchi, Alessandra Cerebellar model Neurorobot Motor learning Distributed plasticity Long term plasticity 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. 2015-05-04T10:44:49Z 2015-05-04T10:44:49Z 2015 info:eu-repo/semantics/article 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] 1662-5188 http://hdl.handle.net/10481/35875 10.3389/fncom.2015.00024 eng info:eu-repo/grantAgreement/EC/FP7/270434 http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Frontiers Foundation