Vestibulo-Ocular Reflex model in Matlab (Clopath at al. 2014)

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" ... We then introduce a minimal model that consists of learning at the parallel fibers to Purkinje cells with the help of the climbing fibers. Although the minimal model reproduces the behavior of the wild-type animals and is analytically tractable, it fails at reproducing the behavior of mutant mice and the electrophysiology data. Therefore, we build a detailed model involving plasticity at the parallel fibers to Purkinje cells' synapse guided by climbing fibers, feedforward inhibition of Purkinje cells, and plasticity at the mossy fiber to vestibular nuclei neuron synapse. The detailed model reproduces both the behavioral and electrophysiological data of both the wild-type and mutant mice and allows for experimentally testable predictions. "
1 . Clopath C, Badura A, De Zeeuw CI, Brunel N (2014) A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice. J Neurosci 34:7203-15 [PubMed]
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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 Purkinje GABA cell;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Synaptic Plasticity;
Implementer(s): Clopath, Claudia [c.clopath at];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell;
% This code is written by Claudia Clopath, Imperial College London %
% Please cite: Clopath et al. Journal of Neuroscience 2014
% "A Cerebellar Learning Model of Vestibulo-Ocular Reflex
% Adaptation in Wild-Type and Mutant Mice"

% Loop over time
Dmean = 2.25;
Dt = gain*0.25*cos((1+T_pat*3/4:T_pat+T_pat*3/4)*2*pi/T_pat)+1; % target D (ie. target MVN output)
for t = previous_t:Simul_t+ previous_t
    P = w_GP' * G - w_IP' * In;             % Purkinje cells, In is the interneuron, G the granuel cells
    D = w_MD* 2*(M-Mmean)+Dmean -M -P;      % Medial Vestibular Nuclei (MVN) cells 
    Cf = light*(Dt-D)+(M-Mmean)*cf_vest;    % Climbing Fibers (CF)
    Cf = circshift(Cf,[0,delay]);           % Delay in the CF
    % plasticity of G to P synapses
    w_GP = w_GP + alphai * (-1)*sum(((ones(N_inp,1)*Cf)+CF_noise*randn(N_inp, T_pat)).* G,2); % update 
    w_GP = (w_GP-(BL/N_inp)).*((w_GP-(BL/N_inp))>0) +(BL/N_inp);    % lower bound on the weights
    w_GP = (w_GP-(BH/N_inp)).*((w_GP-(BH/N_inp))<0)+(BH/N_inp);     % upper bound on the weights
    w_GP =  w_GP + alphaf *(win-w_GP);                              % update of decay
    % plasticity of MF to MVM synapses
    w_MD = w_MD + alphad*sum((-M+Mmean).*(P-Pmean));
    w_MD = w_MD*(w_MD>0);                   % lower bound at zero
    % record phase and gain of D
    [D_G( t), D_P(t)]=max(D); 
    D_G(t) = D_G(t)-mean(D);
previous_t = t;