Fronto-parietal visuospatial WM model with HH cells (Edin et al 2007)

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Accession:98017
1) J Cogn Neurosci: 3 structural mechanisms that had been hypothesized to underlie vsWM development during childhood were evaluated by simulating the model and comparing results to fMRI. It was concluded that inter-regional synaptic connection strength cause vsWM development. 2) J Integr Neurosci: Given the importance of fronto-parietal connections, we tested whether connection asymmetry affected resistance to distraction. We drew the conclusion that stronger frontal connections are beneficial. By comparing model results to EEG, we concluded that the brain indeed has stronger frontal-to-parietal connections than vice versa.
Reference:
1 . Edin F, Macoveanu J, Olesen P, Tegnér J, Klingberg T (2007) Stronger synaptic connectivity as a mechanism behind development of working memory-related brain activity during childhood. J Cogn Neurosci 19:750-60 [PubMed]
2 . Edin F, Klingberg T, Stödberg T, Tegnér J (2007) Fronto-parietal connection asymmetry regulates working memory distractibility. J Integr Neurosci 6:567-96 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Abstract Wang-Buzsaki neuron;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Working memory; Attractor Neural Network;
Implementer(s):
Search NeuronDB for information about:  Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
function kappaPlot( APs, netborder, t1, t2, autocross, deriv )

% autocross = 0 if autocoherence
% autocross = 1 if cross coherence
% autocross = 2 if cross correlation
% deriv = 0: coherence function plotted. 
% deriv = 1: its derivative plotted.

n = length( netborder ) - 1;
if autocross
    n1 = n;
    n2 = n;
else
    n1 = ceil( sqrt( n ) );
    n2 = ceil( n/n1 );
end
x = APs(:,1);
y = APs(:,2);
str = 'IE';

nCell = 0;
if autocross
    xx = [];
    yy = [];
    for i = 1:n
        if mod( i, 2 ) == 1  % I cells
    	    ind = find( ( y >= netborder(i) ) & ( y < netborder(i+1) ) );
	        xx = [ xx ; x(ind) ];
	        yy = [ yy ; y(ind)-netborder(i)+nCell(end) ];
            nCell = [ nCell nCell(end)+netborder(i+1)-netborder(i) ];
        else
            histx = [ netborder(i):netborder(i+1)-1 ];
            histy = histc(y, histx);
            f0 = mean(histy);
            hind = histx( find( histy>1.5*f0 ) ); % Find bump cells
            nCell = [ nCell nCell(end)+length( hind ) ];
            for k = 1:length(hind)
                ind = find( y == hind(k) );
                xx = [ xx ; x(ind) ];
                yy = [ yy ; (nCell(end-1)+k-1)*ones(length(ind),1) ];
            end
        end
    end
    if autocross == 2
        [y x] = crcorr3( [xx yy], t1, t2, nCell );
        for i = 1:length(netborder)-1
            for j = 1:i
                subplot( n1, n2, (i-1)*n1+j )
                plot( x, y(:,(i-1)*n1+j) )
                %fi = fas( x, y );
                title( sprintf( '%s-%d to %s-%d', str(mod(i-1,2)+1), ceil(i/2), str(mod(j-1,2)+1), ceil(j/2) ) )
                set( gca, 'YLim', [0 20] )
                set( gca, 'XLim', [min(x) max(x)] )
                grid on
                if i<length(netborder)-1
                    set( gca, 'XTickLabel', [] )
                else
                    xlabel( 'time(ms)' )
                end
            end
        end
    elseif autocross == 1
        for i = 2:length(netborder)-1
            for j = 1:i-1
                ind = find( ( ( yy >= nCell(j) ) & ( yy < nCell(j+1) ) ) );
                xxx = xx(ind);
                yyy = yy(ind) - nCell(j);
                ind = find( ( ( yy >= nCell(i) ) & ( yy < nCell(i+1) ) ) );
                xxx = [ xxx ; xx(ind) ];
                yyy = [ yyy ; yy(ind)-nCell(i)+nCell(j+1)-nCell(j) ];
            	[xxxx yyyy] = kRange( [xxx yyy], t1, t2, [ nCell(j+1)-nCell(j) nCell(i+1)-nCell(i)+nCell(j+1)-nCell(j) ] );
                if deriv
                    xxxx = xxxx(1:end-1) + (xxxx(2)-xxxx(1));
                    yyyy = diff( yyyy );
                end
              	subplot( n1-1, n2-1, (i-2)*(n1-1)+j )
   	            plot( x, y )
                title( sprintf( '%s-%d to %s-%d', str(mod(i-1,2)+1), ceil(i/2), str(mod(j-1,2)+1), ceil(j/2) ) )
                set( gca, 'XLim', [0 tau(end)] )
                if i == length(nCell)-1
                    xlabel( 'time (ms)' )
                end
            end
        end
    end
else
    for i = 1:n
        if mod( i, 2 ) == 1  % I cells
    	    ind = find( ( y >= netborder(i) ) & ( y < netborder(i+1) ) );
	        xx = x(ind);
	        yy = y(ind)-netborder(i);
            nCell = netborder(i+1)-netborder(i);
        else
            ind = [];
            xx = [];
            yy = [];
            histx = [ netborder(i):netborder(i+1)-1 ];
            histy = histc(y, histx);
            f0 = mean(histy);
            hind = histx( find( histy>1.5*f0 ) ); % Find bump cells
            nCell = length( hind )
            for j = 1:nCell
                ind = find( y == hind(j) );
                xx = [ xx ; x(ind) ];
                yy = [ yy ; (j-1)*ones(length(ind),1) ];
            end
        end
    
    	[kappa tau] = kRange( [xx yy], t1, t2, nCell );
	    subplot( n1, n2, i )
        if deriv
            dkappa = diff( kappa );
            dtau = tau(1:end-1) + (tau(2)-tau(1));
            plot( dtau, dkappa )
        else
        	plot( tau, kappa )
            set( gca, 'YLim', [0 1] )
        end
        title( sprintf( '%s-cells module %d', str(mod(i-1,2)+1), ceil(i/2) ) )
        set( gca, 'XLim', [0 tau(end)] )
        xlabel( 'time (ms)' )
    end
end