Distributed synaptic plasticity and spike timing (Garrido et al. 2013)

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Accession:149913
Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway.
Reference:
1 . Garrido JA, Ros E, D'Angelo E (2013) Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study. Front Comput Neurosci 7:64 [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:
Cell Type(s): Cerebellum interneuron granule GLU cell; Cerebellum golgi cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB; EDLUT;
Model Concept(s): Long-term Synaptic Plasticity;
Implementer(s): Garrido, Jesus A [jesus.garrido at unipv.it];
Search NeuronDB for information about:  Cerebellum interneuron granule GLU cell;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%                           GenerateInputFileBurst.m                      %
%                           ------------------------                      %
% copyright            : (C) 2013 by Jesus Garrido                        %
% email                : jesus.garrido@unipv.it                           %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function GenerateInputFileBurst(FileName, MFInit, MFNumber, FirstBurstTime, IntraBurstFrequency, SpikeProbability, SpikeTimeStd, SpikesPerBurst, InterBurstFrequency, NumberOfBursts, NoiseMatrix)
    fid=fopen(FileName,'w');
    fprintf(fid,'// Generated by GenerateInputFileGaussian\n');
    % Number of inputs
   
    NumberOfTrains = SpikesPerBurst*NumberOfBursts;
    
    Spiker = rand(NumberOfBursts,SpikesPerBurst,MFNumber);
    ref = find(Spiker<=SpikeProbability);
    SpikeNumber = length(ref);
    
    InterBurstPeriod = 1/InterBurstFrequency;
    IntraBurstPeriod = 1/IntraBurstFrequency;
        
    fprintf(fid,'%i\n',SpikeNumber+size(NoiseMatrix,1));
    Values = randn(NumberOfBursts,SpikesPerBurst,MFNumber).*SpikeTimeStd;
    
    for iBurst=1:1:NumberOfBursts,
        for iSpike=1:1:SpikesPerBurst,
            for neuron=0:1:MFNumber-1,
                if (Spiker(iBurst,iSpike,neuron+1)<=SpikeProbability)
                    fprintf(fid,'%f 1 0 %i 1\n',FirstBurstTime+InterBurstPeriod*(iBurst-1)+IntraBurstPeriod*(iSpike-1)+Values(neuron+1),MFInit+neuron);
                end
            end            
        end
    end
    
    for i=1:1:size(NoiseMatrix,1),
        fprintf(fid,'%f 1 0 %i 1\n',NoiseMatrix(i,1),NoiseMatrix(i,2));
    end
    fclose(fid);