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

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Accession:226475
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
References:
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]
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;
Gene(s):
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 gmail.com];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; AMPA; Gaba; Cholinergic Receptors; I_K,Na; Na/K pump; Acetylcholine; Norephinephrine; Gaba;
#NM_Cortex_SR

This repository contains the reference implementation of the model proposed in Schellenberger Costa et al. 2016, available 
here http://link.springer.com/article/10.1007%2Fs10827-016-0602-z

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.


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