Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks
Identifieur interne : 000949 ( Main/Merge ); précédent : 000948; suivant : 000950Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks
Auteurs : Claudia Casellato [Italie] ; Alberto Antonietti [Italie] ; Jesus A. Garrido [Italie, Espagne] ; Giancarlo Ferrigno [Italie] ; Egidio D'Angelo [Italie] ; Alessandra Pedrocchi [Italie]Source :
- Frontiers in Computational Neuroscience [ 1662-5188 ] ; 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.
Url:
DOI: 10.3389/fncom.2015.00024
PubMed: 25762922
PubMed Central: 4340181
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Mondino</institution><country>Pavia, Italy</country></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Garrido, Jesus A" sort="Garrido, Jesus A" uniqKey="Garrido J" first="Jesus A." last="Garrido">Jesus A. Garrido</name><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Brain Connectivity Center, IRCCS Istituto Neurologico Nazionale C. Mondino</institution><country>Pavia, Italy</country></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff3"><institution>Department of Computer Architecture and Technology, University of Granada</institution><country>Granada, Spain</country></nlm:aff><country xml:lang="fr">Espagne</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Ferrigno, Giancarlo" sort="Ferrigno, Giancarlo" uniqKey="Ferrigno G" first="Giancarlo" last="Ferrigno">Giancarlo Ferrigno</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>NeuroEngineering And Medical Robotics Laboratory, Department Electronics, Information and Bioengineering, Politecnico di Milano</institution><country>Milano, Italy</country></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="D Angelo, Egidio" sort="D Angelo, Egidio" uniqKey="D Angelo E" first="Egidio" last="D'Angelo">Egidio D'Angelo</name><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Brain Connectivity Center, IRCCS Istituto Neurologico Nazionale C. Mondino</institution><country>Pavia, Italy</country></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff4"><institution>Department Brain and Behavioral Sciences, University of Pavia</institution><country>Pavia, Italy</country></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author><author><name sortKey="Pedrocchi, Alessandra" sort="Pedrocchi, Alessandra" uniqKey="Pedrocchi A" first="Alessandra" last="Pedrocchi">Alessandra Pedrocchi</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>NeuroEngineering And Medical Robotics Laboratory, Department Electronics, Information and Bioengineering, Politecnico di Milano</institution><country>Milano, Italy</country></nlm:aff><country xml:lang="fr">Italie</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Computational Neuroscience</title><idno type="eISSN">1662-5188</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Albus, J S" uniqKey="Albus J">J. S. Albus</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Attwell, P J" uniqKey="Attwell P">P. J. Attwell</name></author><author><name sortKey="Cooke, S F" uniqKey="Cooke S">S. F. Cooke</name></author><author><name sortKey="Yeo, C H" uniqKey="Yeo C">C. H. Yeo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Attwell, P J" uniqKey="Attwell P">P. J. Attwell</name></author><author><name sortKey="Rahman, S" uniqKey="Rahman S">S. Rahman</name></author><author><name sortKey="Yeo, C H" uniqKey="Yeo C">C. H. 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