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]
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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 [ popv, cut ] = popvec( x, y, ang, time )

% popvec calculates the population vector. To calculate the
% population vector in a ring, one first needs to find
% the angle section with the lowest variance. Then one calculates the
% mean position of the activity.
%
% x   : Action position times
% y   : Action position cell index
% ang : Matrix. left column: cell index, right colums: angle or
%       whichever value one wants to measure
% time: Vector of time intervals where the population vector is to
%       be calculated.
% popv: Population vector in radians
% cut: The angle of the cut of the ring that produces the smallest variance

popv = zeros( 1, length( time )-1 );
% for every time step, do
one = ones( size( y ) );
for i = 1:length( time )-1
  
    % only action potentials within the right time window
    ytmp = y( find( x>=time(i) & x < time(i+1) ) );
    
    % only action potentials from the right cells
    for j = 1:size( ang, 1 )
        cnt = length( find( ytmp == ang( j, 1 ) ) );
        dist(j) = cnt;
    end
    if sum( dist ) == 0 % no rate
        disp( 'Error: no rate' )
        popv = -1;
        cut = 0;
        return
    end
    dist = dist / sum( dist );
    dist = dist(:);
    
    % sort vector so that vectors can be rotated properly
    [ angl, ind ] = sort( ang(:,2) );
    dist = dist( ind );
    
    % find minimum variance
    s2 = sum( dist.*angl.*angl ) - sum( dist.*angl )*sum( dist.*angl );
    popv(i) = sum(angl.*dist);
    len = length( angl );
    cut = 1;
    for j = 1: len-1
        angl = [ angl(end) ; angl(1:end-1) ];
        s22 = sum( dist.*angl.*angl ) - sum( dist.*angl )*sum( dist.*angl );
    	% If variance smallest so far, calculate mean activity position.
        if s22 < s2
            s2 = s22;
            popv(i) = mod( (sum( angl.*dist )+j), len );
            cut = j+1;
        end
    end
    angl = [ angl(end) ; angl(1:end-1) ];
end
cut = angl( cut );