Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation
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AuthorLuque Sola, Niceto Rafael; Garrido Alcázar, Jesús Alberto; Carrillo Sánchez, Richard Rafael; D'Angelo, Egidio; Ros Vidal, Eduardo
Frontiers Research Foundation
Cerebellar nucleiInferior oliveLong-term synaptic plasticityLearning consolidationModeling
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]
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.
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.