Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks

Frontiers in Computational Neuroscience

Jesus A Garrido¹  Alberto eAntonietti²  Giancarlo eFerrigno²  Alessandra ePedrocchi²  Claudia eCasellato²  Egidio eD‘Angelo³ 

[1] IRCCS Istituto Neurologico Nazionale C. Mondino;Politecnico di Milano;University of Pavia;

关键词: motor learning;    Long Term Plasticity;    cerebellar model;    Distributed plasticity;    Neurorobot;   

DOI  :  10.3389/fncom.2015.00024

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown