Neural mass model of the neocortex under sleep regulation (Costa et al 2016)

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This model generates typical human EEG patterns of sleep stages N2/N3 as well as wakefulness and REM. It further contains a sleep regulatory component, that lets the model transition between those stages independently
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]
2 . Costa MS, Born J, Claussen JC, Martinetz T (2016) Modeling the effect of sleep regulation on a neural mass model. J Comput Neurosci 41:15-28 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neural mass;
Brain Region(s)/Organism: Brainstem; 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; Cholinergic Receptors;
Transmitter(s): Acetylcholine; Norephinephrine; Gaba;
Simulation Environment: Network; C or C++ program (web link to model); MATLAB (web link to model);
Model Concept(s): Simplified Models; Temporal Pattern Generation; Sleep; Activity Patterns; Oscillations; Bifurcation; Electrical-chemical; Neuromodulation;
Implementer(s): Schellenberger Costa, Michael [mschellenbergercosta at];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; AMPA; Gaba; Cholinergic Receptors; I_K,Na; Na/K pump; Acetylcholine; Norephinephrine; Gaba;

This repository contains the reference implementation of the model proposed in Schellenberger Costa et al. 2016, available 

For convenience we utilize MATLAB for data processing and plotting. Therefore the simulation comes with an additional source-file 
Cortex_SR_mex.cpp that can be compiled within MATLAB to utilize their C++-mex interface. The easiest way to reproduce the figures 
in the paper is to simply run the Create_Data() function within MATLAB, assuming the mex interface is setup. Afterwards simply run the Create_Figures() function to generate the different figures. 

Please note that due to the stochastic nature of the simulation the time series will differ.