Find models by
Find models for
Find models of
Electrical synapses (gap junctions)
SenseLab mailing list
ModelDB related resources
Computational neuroscience ecosystem
Models in a git repository
Fast convergence of cerebellar learning (Luque et al. 2015)
Download zip file
Help downloading and running models
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) Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation.
Front Comput Neurosci
(Click on a link to find other models with that property)
Garrido, Jesus A [jesus.garrido at unipv.it];
Luque, Niceto R. [nluque at ugr.es];
File not selected
<- Select file from this column.
Loading data, please wait...