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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/33430

Title: Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation
Authors: Luque Sola, Niceto Rafael
Garrido Alcázar, Jesús
Carrillo Sánchez, Richard
D'Angelo, Egidio
Ros, Eduardo
Issue Date: 2014
Abstract: The 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.
Sponsorship: This 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.
Publisher: Frontiers Research Foundation
Keywords: Cerebellar nuclei
Inferior olive
Long-term synaptic plasticity
Learning consolidation
Modeling
URI: http://hdl.handle.net/10481/33430
ISSN: 1662-5188
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Luque, 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]
Appears in Collections:OpenAIRE (Open Access Infrastructure for Research in Europe)
DATC - Artículos

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