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dc.contributor.authorLuque Sola, Niceto Rafael
dc.contributor.authorGarrido Alcázar, Jesús
dc.contributor.authorCarrillo Sánchez, Richard
dc.contributor.authorD'Angelo, Egidio
dc.contributor.authorRos, Eduardo
dc.date.accessioned2014-10-17T09:45:25Z
dc.date.available2014-10-17T09:45:25Z
dc.date.issued2014
dc.identifier.citationLuque, N.R.; et al. Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation. Frontiers in Computational Neuroscience, 8: 97 (2014). [http://hdl.handle.net/10481/33430]es_ES
dc.identifier.issn1662-5188
dc.identifier.urihttp://hdl.handle.net/10481/33430
dc.description.abstractThe cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior olive (IO), can only account for limited aspects of learning. Recently, the role of additional forms of plasticity in the granular layer, molecular layer and deep cerebellar nuclei (DCN) has been considered. In particular, learning at DCN synapses allows for generalization, but convergence to a stable state requires hundreds of repetitions. In this paper we have explored the putative role of the IO-DCN connection by endowing it with adaptable weights and exploring its implications in a closed-loop robotic manipulation task. Our results show that IO-DCN plasticity accelerates convergence of learning by up to two orders of magnitude without conflicting with the generalization properties conferred by DCN plasticity. Thus, this model suggests that multiple distributed learning mechanisms provide a key for explaining the complex properties of procedural learning and open up new experimental questions for synaptic plasticity in the cerebellar network.es_ES
dc.description.sponsorshipThis work was supported by grants from the European Union, Egidio D'Angelo and Eduardo Ros (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to Egidio D'Angelo (RF-2009-1475845) and the Spanish Regional Government, Niceto R. Luque (PYR-2014-16). We thank G. Ferrari and M. Rossin for their technical support.es_ES
dc.language.isoenges_ES
dc.publisherFrontiers Research Foundationes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/238686es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/270434es_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es_ES
dc.subjectCerebellar nucleies_ES
dc.subjectInferior olivees_ES
dc.subjectLong-term synaptic plasticityes_ES
dc.subjectLearning consolidationes_ES
dc.subjectModeling es_ES
dc.titleFast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3389/fncom.2014.00097


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