Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)

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Accession:118389
Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not.
Reference:
1 . Hjorth J, Blackwell KT, Kotaleski JH (2009) Gap junctions between striatal fast-spiking interneurons regulate spiking activity and synchronization as a function of cortical activity. J Neurosci 29:5276-86 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell; Synapse; Channel/Receptor; Dendrite;
Brain Region(s)/Organism: Basal ganglia;
Cell Type(s): Neostriatum fast spiking interneuron;
Channel(s): I A; I_K,Na;
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: GENESIS; MATLAB;
Model Concept(s): Activity Patterns; Ion Channel Kinetics; Synchronization; Detailed Neuronal Models;
Implementer(s): Hjorth, Johannes [hjorth at csc.kth.se];
Search NeuronDB for information about:  I A; I_K,Na; Gaba; Glutamate;
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FSGJ_Hjorth2009
matlabScripts
checkAllEqual.m *
correlationByDuplicationOfSpikes.m
correlationByJitteringOfSpikes.m
countSpikesWithNumNeighbourSpikes.m
findSpikes.m *
gaussJitterInputKeepCorr.m
makeAllExternalInputAllUpstate.m
makeDaughterInsignal.m
makeDaughterNoise.m *
makeFSconnectionMatrixOnlyPrimWrappedSetNGJ.m
makeFSconnectionMatrixOnlySecWrappedSetNGJ.m
makeFSMorph.m
makeFSrandomNetwork.m *
makeInputWithCorrShift.m
makeInputWithCorrShift125center.m
makeSCCCplot.m
makeTrainInsignal.m
makeTrainNoise.m *
mixTwoTrainsKeepCorr.m
poissonMaxTime.m *
showFSnetwork.m
writeCurrentInputInfo.m
writeInput.m *
writeParameters.m
                            
%
% Kolla upp vad xcorr gör...
%

% Tanken är att duplicera vissa spikar i spiktåg mellan närliggande
% celler för att se hur detta påverkar synkroniseringen


% allowVar = 1 --> mother/daughter generation (DEFAULT)
% allowVar = 0 --> fixed number of doubletts for all spikes train-version



function m = correlationByDuplicationOfSpikes(corrRudolph, upFreq, ...
                                              noiseFreq, maxTime, ...
                                              allowVar, pMix, ...
                                              randSeed, numCells)

                                  
rand('seed', randSeed)
randSeed = rand('seed');

disp(['Setting random seed to ' num2str(randSeed)])

path = [pwd '/INDATA/'];


nAMPA = 127;
nGABA = 93;

downFreq = 1e-9;

disp(['All upstate input, freq ' num2str(upFreq)])


if(allowVar)
  disp('Generating mother/daughter input')
  
  % Input that is duplicated in several input traces
  
  dupInsignalAMPA = makeDaughterNoise(corrRudolph, nAMPA, ...
                                      upFreq, maxTime);

  dupInsignalGABA = makeDaughterNoise(corrRudolph, nGABA, ...
                                      upFreq, maxTime);
 
  for nCtr = 1:numCells

    % Generate input to neurons that are correlated within the neuron
    % but not correlated between neurons. This input is then mixed
    % with the population shared input.

    % Neuron specific input
  
    uniqueInsignalAMPA{nCtr} = makeDaughterNoise(corrRudolph, nAMPA, ...
                                                 upFreq, maxTime);

    uniqueInsignalGABA{nCtr} = makeDaughterNoise(corrRudolph, nGABA, ...
                                                 upFreq, maxTime);
  end

else                                  
  disp('Generating input with constant number of doubletts')  

  % Input that is duplicated in several input traces 
  
  dupInsignalAMPA = makeTrainNoise(corrRudolph, nAMPA, ...
                                   upFreq, maxTime);

  dupInsignalGABA = makeTrainNoise(corrRudolph, nGABA, ...
                                   upFreq, maxTime);

  for nCtr = 1:numCells

    % Generate input to neurons that are correlated within the neuron
    % but not correlated between neurons. This input is then mixed
    % with the population shared input.

    % Neuron specific input
  
    uniqueInsignalAMPA{nCtr} = makeTrainNoise(corrRudolph, nAMPA, ...
                                            upFreq, maxTime);

    uniqueInsignalGABA{nCtr} = makeTrainNoise(corrRudolph, nGABA, ...
                                            upFreq, maxTime);
  end
                                                        
end

for nCtr = 1:numCells
    
  % Generate uncorrelated noise
    
  if(allowVar)
    allNoiseAMPA{nCtr} = makeDaughterNoise(corrRudolph, nAMPA, ...
                                        noiseFreq, maxTime);
    allNoiseGABA{nCtr} = makeDaughterNoise(corrRudolph, nGABA, ...
                                        noiseFreq, maxTime); 
  else
    allNoiseAMPA{nCtr} = makeTrainNoise(corrRudolph, nAMPA, ...
                                        noiseFreq, maxTime);
    allNoiseGABA{nCtr} = makeTrainNoise(corrRudolph, nGABA, ...
                                        noiseFreq, maxTime);  
  end

end



for nCtr = 1:numCells

  % Mix the duplicate and unique input                                  
  % pMix = 0, only duplicate train
  % pMix = 1, only unique train
                                  
  insignalAMPA = mixTwoTrainsKeepCorr(uniqueInsignalAMPA{nCtr}, ...
                                      dupInsignalAMPA, pMix);
  insignalGABA = mixTwoTrainsKeepCorr(uniqueInsignalGABA{nCtr}, ...
                                      dupInsignalGABA, pMix);
  
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  
  writeInput([path 'AMPAinsignal_' num2str(nCtr) '_%d'], insignalAMPA);
  writeInput([path 'GABAinsignal_' num2str(nCtr) '_%d'], insignalGABA);

  writeInput([path 'AMPAnoise_' num2str(nCtr) '_%d'], allNoiseAMPA{nCtr});
  writeInput([path 'GABAnoise_' num2str(nCtr) '_%d'], allNoiseGABA{nCtr});

  cellNum = nCtr;
  
  noiseAMPA = allNoiseAMPA{nCtr};
  noiseGABA = allNoiseGABA{nCtr};

  eval(['save ' path 'DuplicationCorrInput_' num2str(nCtr) ...
              '_id' num2str(randSeed) ...
              '_pMix' num2str(pMix) '.mat' ...
          ' insignalAMPA insignalGABA ' ...
          ' noiseAMPA noiseGABA randSeed cellNum pMix']);
  
  
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

fid = fopen([path 'inputInfo.txt'], 'w');

fprintf(fid, '%s\n', 'correlationByDuplicationOfSpikes');
fprintf(fid, '%f\n', corrRudolph);
fprintf(fid, '%f\n', upFreq);
fprintf(fid, '%f\n', noiseFreq);
fprintf(fid, '%f\n', maxTime);
fprintf(fid, '%d\n', allowVar);
fprintf(fid, '%d\n', randSeed);
fprintf(fid, '%d\n', numCells);
fprintf(fid, '%f\n', pMix);

fclose(fid);

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