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

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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.
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;
Gap Junctions: Gap junctions;
Simulation Environment: NEURON;
Model Concept(s): Working memory; Attractor Neural Network;
Search NeuronDB for information about:  Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell;
function [ dr, popv ] = driftdiffusion( x, y, ang, binw, time )

% drift calculates the drift of the population vector. Drift 
% is here defined as the standard deviation of diffusion process
% of the population vector of the bump. This function uses popvec.m,
% which needs to be in the same catalog.
% x    : vector of time points of action potentials
% y    : vector of cell indices of action potentials
% ang  : Matrix. left column: cell index, right colums: angle or
%        whichever value one wants to measure
% binw : time bin
% time : two-vector with start and stop times

tid = time(1):binw:time(2);
popv = popvec( x, y, ang, tid );
if popv == -1
    dr = -1;
ddtpopv = ( diff( popv ) ./ (diff( tid(1:end-1) )/1000) );
wddt = ddtpopv.^2.*diff( tid(1:end-1) ) / (tid(end-1)-tid(1));
dr = sqrt( sum( wddt ));
%subplot( 3,1,1 )
%plot( tid( 1:end-1), popv )
%subplot( 3,1,2 )
%plot( tid( 1:end-2 ), ddtpopv )
%subplot( 3,1,3 )
plot( tid( 1:end-2 ), wddt )
disp( dr )