Fast convergence of cerebellar learning (Luque et al. 2015)

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Accession:150225
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.
Reference:
1 . 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 8:97 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Simulink;
Model Concept(s): STDP;
Implementer(s): Garrido, Jesus A [jesus.garrido at unipv.it]; Luque, Niceto R. [nluque at ugr.es]; 
STEPS TO BE FOLLOWED:
Software that has been used for the developing this cerebellar architecture:
-MATLAB VERSION 2009b
-MATLAB ROBOTIC TOOLBOx from Peter Corke version 6 
- MICROSOFT VISUAL STUDIO 2008

1. - DOWNLOAD THE MATLAB ROBOTIC TOOLBOX FROM:
http://petercorke.com/Robotics_Toolbox.html. The code has been
developed using and older version of this robotic toolbox Download
Release 6 in pkzip format (2/2007).
2. - Add the toolbox and the supplied matlab code to the matlab Path.
3. - Compile the recursive newton eurler function and locate the
resulting .mex file under the matlab path too. Under 32bit windows
O.S. we can type on the matlab windows command
>> mex frne.c ne.cvmath.c
4.- Compile de cerebellar architecture
mex . CerebellumIODCNAbstract.cpp SimulinkBlockInterface.cpp
5.- Ones all the mex files are availables, open the Launchers in
matlab. LaunchCerebellumSimulinkExternalForce.m &
LaunchCerebellumSimulink.m
6- Run the launchers.

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