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

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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.
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):
Gap Junctions:
Simulation Environment: Simulink;
Model Concept(s): STDP;
Implementer(s): Garrido, Jesus A [jesus.garrido at]; Luque, Niceto R. [nluque at];
%CINEMATICADIRECTA	S-function it is used to compute accelerations and velocities of a
% manipulator.
% This S-función calculates the robot acceleration joints.The block entry is 
% the u vector whose components are positions and velocities of articular
% coordinates [q qd] and the robot object called RRed.
%               [sys,x0,str,ts] =CINDIR(t,x,u,flag,RRed)
% RRed is an n-axis robot object and describes the manipulator dynamics and 
% kinematics
% Implementation in Simulink.
% See also: ACCEL_H,ACCEL, ROBOT, ODE45.

%   2007 Niceto Luque Sola

function [sys,x0,str,ts] =CINDIR(t,x,u,flag,RRed)
	switch flag,

	case 0
		% initialize  robot dimensions
		[sys,x0,str,ts] = mdlInitializeSizes(RRed);	% Init
     case {3}
		% come here to calculate derivitives
        q=[u(1:RRed.n)];% articular postitions vector
        qd=[u(RRed.n+1:2*RRed.n)];%articular velocities vector
        tau=[u(2*RRed.n+1:end)]; % external applied torque
        sys = accel(RRed,q,qd,tau);%inverse dynamic
	case {1, 2, 4, 9}
		sys = [];
% mdlInitializeSizes
% Return the sizes, initial conditions, and sample times for the S-function.
function [sys,x0,str,ts]=mdlInitializeSizes(RRed)
% call simsizes for a sizes structure, fill it in and convert it to a
% sizes array.
% Note that in this example, the values are hard coded.  This is not a
% recommended practice as the characteristics of the block are typically
% defined by the S-function parameters.
sizes = simsizes;
sizes.NumContStates  = 0;
sizes.NumDiscStates  = 0;
sizes.NumOutputs     = RRed.n;
sizes.NumInputs      = 3*RRed.n;
sizes.DirFeedthrough = 1;
sizes.NumSampleTimes = 1;   % at least one sample time is needed
sys = simsizes(sizes);
% initialize the initial conditions
x0  = [];
% str is always an empty matrix
str = [];
% initialize the array of sample times
ts  = [0 0];
% end mdlInitializeSizes

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