Distributed cerebellar plasticity implements adaptable gain control (Garrido et al., 2013)

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Accession:150067
We tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum.
Reference:
1 . Garrido JA, Luque NR, D'Angelo E, Ros E (2013) Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation Front. Neural Circuits 7:159:1-20 [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): Cerebellum deep nucleus neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; MATLAB; Simulink;
Model Concept(s): Long-term Synaptic Plasticity;
Implementer(s): Garrido, Jesus A [jesus.garrido at unipv.it]; Luque, Niceto R. [nluque at ugr.es];
% 5 jun 2013 - Niceto R. Luque
% Direct Kinematics for a three joints robot
% 8-shaped trajectory
function [x1, y1, z1] = cin_dir_och3joints_funct(qt1,qt2,qt3)
syms q1 q2 q3 l1 l2 l3 x y z
A01=[cos(q1) 0 sin(q1) 0;sin(q1) 0 -cos(q1) 0;0 1 0 l1; 0 0 0 1];
A12=[cos(q2) -sin(q2) 0 l2*cos(q2); sin(q2) cos(q2) 0 l2*sin(q2);0 0 1 0; 0 0 0 1];
A23=[cos(q3) -sin(q3) 0 l3*cos(q3); sin(q3) cos(q3) 0 l3*sin(q3);0 0 1 0; 0 0 0 1];
A03=A01*A12*A23;
A03b=simple(A03);
x=A03b(1,4);
y=A03b(2,4);
z=A03b(3,4);


step = 0.002;
t = 0:step:(step*(length(qt1)-1));
% joint positions 
x1=[];
y1=[];
z1=[];

lr1 = 0.310; 
lr2 = 0.4;
lr3 = 0.390;

x1=subs(x,{q1,q2,q3,l1,l2,l3},{qt1,qt2,qt3,lr1,lr2,lr3});
y1=subs(y,{q1,q2,q3,l1,l2,l3},{qt1,qt2,qt3,lr1,lr2,lr3});
z1=subs(z,{q1,q2,q3,l1,l2,l3},{qt1,qt2,qt3,lr1,lr2,lr3});


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