SenseLab
Computational model
  Data
Fast convergence of cerebellar learning (Luque et al. 2015)
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.
  • Luque NR, Garrido JA, Carrillo RR, D'Angelo E, Ros E (2014) Show Other
  • Garrido, Jesus A [jesus.garrido at unipv.it] Show Other
  • Luque, Niceto R. [nluque at ugr.es] Show Other
nluque@ugr.es
CEREBELLAR MODEL
Luque Niceto R. & Garrido Jesus A.
False
False
Other categories referring to Fast convergence of cerebellar learning (Luque et al. 2015)
Revisions: 8
Last Time: 7/8/2015 5:22:18 PM
Reviewer: Tom Morse - MoldelDB admin
Owner: Tom Morse - MoldelDB admin