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];
function [sys,x0,str,ts] = inverse(t,x,u,flag)

% Dispatch the flag. The switch function controls the calls to 
% S-function routines at each simulation stage of the S-function.
%
switch flag,
  %%%%%%%%%%%%%%%%%%
  % Initialization %
  %%%%%%%%%%%%%%%%%%
  % Initialize the states, sample times, and state ordering strings.
  case 0
    [sys,x0,str,ts]=mdlInitializeSizes;

  %%%%%%%%%%%
  % Outputs %
  %%%%%%%%%%%
  % Return the outputs of the S-function block.
  case 3
    sys=mdlOutputs(t,x,u);

  %%%%%%%%%%%%%%%%%%%
  % Unhandled flags %
  %%%%%%%%%%%%%%%%%%%
  % There are no termination tasks (flag=9) to be handled.
  % Also, there are no continuous or discrete states,
  % so flags 1,2, and 4 are not used, so return an emptyu
  % matrix 
  case { 1, 2, 4, 9 }
    sys=[];

  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  % Unexpected flags (error handling)%
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  % Return an error message for unhandled flag values.
  otherwise
    error(['Unhandled flag = ',num2str(flag)]);

end

% end timestwo

%
%=============================================================================
% mdlInitializeSizes
% Return the sizes, initial conditions, and sample times for the S-function.
%=============================================================================
%
function [sys,x0,str,ts] = mdlInitializeSizes()

sizes = simsizes;
sizes.NumContStates  = 0;
sizes.NumDiscStates  = 0;
sizes.NumOutputs     = -1;  % dynamically sized
sizes.NumInputs      = -1;  % dynamically sized
sizes.DirFeedthrough = 1;   % has direct feedthrough
sizes.NumSampleTimes = 1;

sys = simsizes(sizes);
str = [];
x0  = [];
ts  = [-1 0];   % inherited sample time

% end mdlInitializeSizes

%
%=============================================================================
% mdlOutputs
% Return the output vector for the S-function
%=============================================================================
%
function sys = mdlOutputs(t,x,u)
I=find(u>0);
unew=u(I).^-1;
u(I)=unew;
sys = u;

% end mdlOutputs


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