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

 Download zip file 
Help downloading and running models
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 synchronisation as a function of cortical activity J Neurosci 29(16):5276-5286
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;
/
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
                            
% http://en.wikipedia.org/wiki/Exponential_distribution
%
% -ln(u)/lambda
%

function p = poissonMaxTime(lambda, maxTime)

p = cumsum(-log(rand(ceil(lambda*maxTime),1))/lambda);
t = p(end) - log(rand(1))/lambda;

while(t < maxTime)
    p = [p; t];
    t = t - log(rand(1))/lambda;
end

p = p(find(p < maxTime),1);

Loading data, please wait...