Neural mass model of the sleeping cortex (Weigenand et al 2014)

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Accession:226472
Generates typical EEG data of sleeping Humans for sleep stages N2/N3 as well as wakefulness
Reference:
1 . Weigenand A, Schellenberger Costa M, Ngo HV, Claussen JC, Martinetz T (2014) Characterization of K-complexes and slow wave activity in a neural mass model. PLoS Comput Biol 10:e1003923 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neural mass;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L2/3 pyramidal GLU cell; Neocortex layer 2-3 interneuron;
Channel(s): I_K,Na; Na/K pump;
Gap Junctions:
Receptor(s): AMPA; Gaba;
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program (web link to model); MATLAB;
Model Concept(s): Bifurcation; Sleep; Activity Patterns;
Implementer(s): Schellenberger Costa, Michael [mschellenbergercosta at gmail.com];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; AMPA; Gaba; I_K,Na; Na/K pump;
function Data_Stimulation
% This function creates the model data depicted in Figure 4 of
%
% Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model
% A Weigenand, M Schellenberger Costa, H-VV Ngo, JC Claussen, T Martinetz
% PLoS Computational Biology. 2014;10:e1003923

% To ensure availability of the simulation routine it should be called from Create_Data.m

% Parameter settings
Param_N3    = [ 6.5;	% sigma_e
                2;	% g_KNa
                0E-3];	% dphi


Param_N2    = [ 4.6;	% sigma_e
                1.33;	% g_KNa
                0E-3];	% dphi

% stimulation parameters
% first number is the mode of stimulation
% 0 == none
% 1 == semi-periodic
% 2 == phase dependend
var_stim = [1;          % mode of stimulation
	    100;        % strength of the stimulus              in Hz (spikes per second)
	    100;       	% duration of the stimulus              in ms
	    7;          % time between stimulation events       in s  (ISI)
	    2;          % range of ISI                          in s  [ISI-range,ISI+range]
	    1;          % Number of stimuli per event
	    0;          % time between stimuli within a event   in ms
	    0];         % time until stimuli after minimum      in ms
            
% Number of runs for stimulation, as well as color set            
N = 15;

% time of the stimulation
T     = 4;
Ve_N2 = cell(N,1);
Ve_N3 = cell(N,1);

for i=1:N 
    var_stim(2)= i*10;
    [Ve_N2{i}, ~]    = Cortex_mex(T, Param_N2, var_stim);
    [Ve_N3{i}, ~]    = Cortex_mex(T, Param_N3, var_stim);
end

save('Data/Stimulation.mat', 'Ve_N2', 'Ve_N3');
end

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