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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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 DD=getDelayMatrix(C,n,distfunc,hdtime)

%check if matlabpool is open and if not then open it
if  matlabpool('size') == 0
    matlabpool open
end

% width of the square
nsub=n^2;


%lay out coordinates
[coordx,coordy] = meshgrid(1:n,1:n);
coorx=reshape(coordx,nsub,1);
coory=reshape(coordy,nsub,1);


%elements that need euclidian distance calculating:
[nEx,nEy]=find(C>0);
nel=length(nEx);

% Calculate the locations of the sparse elements

%D=zeros(nel,3);
parfor k=1:nel
    
    i=nEx(k);
    j=nEy(k);
    % call distfinc (i.e. sheet, torus etc
    distM=(distfunc([coorx(i) coory(i)],[coorx(j) coory(j)],n));
    D(k,:)=[i,j,distM*50];%gives the distance in micrometers
    
end



nTS=ceil(max(D(:,3))/hdtime);
DD={};
for k=1:nTS
    toconvert=D(k-1<=D(:,3)/hdtime & D(:,3)/hdtime<k,:);
    length(toconvert);
    DD{k}=sparse(toconvert(:,1),toconvert(:,2),1,nsub,nsub);
    
end





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