A cortical sheet mesoscopic model for investigating focal seizure onset dynamics (Wang et al. 2014)

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Accession:155565
The model uses realistically coupled, discretised, Wilson-Cowan units to describe the spatio-temporal activity of a cortical sheet. This model has been used the investigate the dynamic onset mechanisms of focal seizures.
Reference:
1 . Wang Y, Goodfellow M, Taylor PN, Baier G (2014) Dynamic mechanisms of neocortical focal seizure onset PLoS Computational Biology 10(8):e1003787 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neural mass;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Oscillations; Spatio-temporal Activity Patterns; Epilepsy; Delay; Brain Rhythms; Bifurcation;
Implementer(s): Wang, Yujiang [yujiang.wang at newcastle.ac.uk];
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WebPublication
lib
ConnLocGaussian.m *
ConnPatchyRemOverlap.m *
Conns_n150.mat
convolve2.m *
distSheet.m *
distTorus.m *
exindex.m *
FilterEEG.m
Gaussian.m *
GaussianLocConnFunc.m
generatePatchesOverlap.m *
getDelayMatrix.m
getNoise.m
getParam.m *
getParamDelay.m
makeCellCluster.m *
makeCellClusterToroidal.m *
MayColourMap.mat *
meanMacroCol.m *
runSheet.m *
runSheetDelay.m *
runSheetPRamp.m *
Sigm.m *
                            
function parameters=getParamDelay(n,CeRem,CeLoc,CeLocI,h,propspeed)


parameters.n=n;%sheet is nxn


parameters.Inh2Py=25*speye(n^2);%in this parameter set the increase in this prolongs the onset & prevents too much going to the upper fixed point


parameters.PyInput=-2.5*(ones(n^2,1));
parameters.InhInput=-5*ones(n^2,1);
    


parameters.tauPy=1*ones(n^2,1)/25;%time scale of the populations
parameters.tauInh=0.5*ones(n^2,1)/25;
%parameters.tauInFast=0.1*ones(n^2,1);

parameters.SigThresh=4*ones(n^2,1);%sigmoid parameters
parameters.SigSteepness=1*ones(n^2,1);

parameters.h=h;

hdtime=h*propspeed*1000*1000; %stepsize times delaytime

CeLocDelay=getDelayMatrix(CeLoc,n,@distTorus,hdtime);
CeLocIDelay=getDelayMatrix(CeLocI,n,@distTorus,hdtime);
CeRemDelay=getDelayMatrix(CeRem,n,@distTorus,hdtime);

maxTS=max([length(CeLocDelay),length(CeRemDelay)]);
minTS=min([length(CeLocDelay),length(CeRemDelay)]);


parameters.Py2Py{1}=10*speye(n^2)+.15*CeLocDelay{1}+.05*CeRemDelay{1};%conn matrix Py to Py at time step 1 distance
for k=2:maxTS
    if k>minTS
        parameters.Py2Py{k}=.05*CeRemDelay{k};%conn matrix Py to Py at time step minTS until maxTS distance
    else
        parameters.Py2Py{k}=.15*CeLocDelay{k}+.05*CeRemDelay{k};%conn matrix Py to Py  at time step 2 until minTS distance
    end
end

parameters.Py2Inh{1}=0.1*CeLocIDelay{1}+15*speye(n^2);%
for k=2:length(CeLocIDelay)
    parameters.Py2Inh{k}=0.1*CeLocIDelay{k};%
end

parameters.delaysteps=maxTS;
end

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