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 [ bmp, s2, s2S ] = isbump( x, y, time, ind, window, Qt, s2S )

% isbump finds out whether there is a bump in a time interval.
% The criterion for having a bump is that the standard deviation
% surpasses an empirically found (but arbitrary) threshold of 1.5.
% Variance of activity is measured as the average variance in time
% windows of size window to eliminate effects of bump drift.
%
% x      : Vector with action potential time points
% y      : Vector with action potential angles, or whatever
% time   : 2-vector with beginning and end of measure
% ind    : vectors with cell indices of interest
% window : Time window in which variance is measured
% Qt     : Time window for spontaneous activity is 500 ms <= t < Qt

s2 = 0;

% Find only action potentials of interest
yind = [];
for i = 1:length( ind )
  yind = [ yind find( y == ind(i) ) ];
end
x = x(ind);
y = y(ind);

% Calculate standard deviation of spontaneous activity if none exists
if nargin < 7
    s2S = 0;
    timS = 500:window:Qt; 
    cnt = 0;
    for i = 1:length( timS ) -1
        indt = find( x>=timS(i) & x<timS(i+1) );
        if isempty( indt )
            cnt = cnt + 1;
        else
            xtmp = x( indt );
            ytmp = y( indt );
            rate = histc( ytmp, ind ) / (diff(timS(i:i+1))/1000); %Kolla om detta ar statistiskt riktigt
            tmp =  var( rate ) / mean( rate );
            s2S = s2S + tmp;
        end
    end
    cnt = length( timS )-1-cnt;
    if cnt > 0
        s2S = s2S / cnt;
    else
        s2S = 0
    end
end

time = time(1):window:time(2);
cnt = 0;
for i = 1:length( time ) -1
    indt = find( x>=time(i) & x<time(i+1) );
    if isempty( indt )
        cnt = cnt + 1;
    else
        ytmp = y(indt);
        xtmp = x(indt);
        rate = histc( ytmp, ind ) / (diff(time(i:i+1))/1000);
        tmp =  var( rate ) / mean( rate );
        s2 = s2 + tmp;
    end
end
cnt = length( time )-1-cnt;
if cnt > 0
    s2 = s2 / cnt;
else
    s2 = 0;
end
bmp = s2>1.5*s2S;  % Note, 1.5 is arbitrary border