Neural mass model based on single cell dynamics to model pathophysiology (Zandt et al 2014)

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Accession:155130
The model code as described in "A neural mass model based on single cell dynamics to model pathophysiology, Zandt et al. 2014, Journal of Computational Neuroscience" A Neural mass model (NMM) derived from single cell dynamics in a bottom up approach. Mean and standard deviation of the firing rates in the populations are calculated. The sigmoid is derived from the single cell FI-curve, allowing for easy implementation of pathological conditions. NMM is compared with a detailed spiking network model consisting of HH neurons. NMM code in Matlab. The network model is simulated using Norns (ModelDB # 154739)
Reference:
1 . Zandt BJ, Visser S, van Putten MJ, Ten Haken B (2014) A neural mass model based on single cell dynamics to model pathophysiology. J Comput Neurosci 37:549-68 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neural mass;
Brain Region(s)/Organism:
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; MATLAB; Brian; Python; Norns - Neural Net Studio;
Model Concept(s): Simplified Models; Methods; Pathophysiology; Connectivity matrix; Brain Rhythms;
Implementer(s): Zandt, Bas-Jan [Bas-Jan.Zandt at biomed.uib.no];
% calculate a discrete fourier tranform and use the sample time to create
% a frequency axis. Also, correct the amplitude for the sample time.
% [Y,f] = myfour(y,Dt,N); if y is 2D, then the fourier transform of all
% !columns! are calculated.

function [Y,f]=myfour(y,Dt,N)
if nargin < 3; N = length(y); end
    
if length(Dt)> 1; Dt = Dt(2)-Dt(1); end; % if complete time axis is given, assume evenly spaced samples

[a,b] = size(y);
if a == 1 || b == 1
    Y = fftshift(fft(y,N));
else
Y = fftshift(fft(y,N),1);
end
Y = Y*Dt; % to get the right amplitude

Df = 1/(Dt*N);
f = ((0:N-1)-N/2)*Df;

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