Neural mass model of the sleeping thalamocortical system (Schellenberger Costa et al 2016)

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Accession:226474
This paper generates typical human EEG data of sleep stages N2/N3 as well as wakefulness and REM sleep.
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 . Schellenberger Costa M, Weigenand A, Ngo HV, Marshall L, Born J, Martinetz T, Claussen JC (2016) A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation. PLoS Comput Biol 12:e1005022 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neural mass;
Brain Region(s)/Organism: Thalamus; Neocortex;
Cell Type(s): Neocortex L2/3 pyramidal GLU cell; Thalamus reticular nucleus GABA cell; Thalamus geniculate nucleus/lateral principal GLU cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; Neocortex layer 2-3 interneuron;
Channel(s): I Calcium; Na/K pump; I_K,Na;
Gap Junctions:
Receptor(s): AMPA; Gaba; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: Network; C or C++ program (web link to model); MATLAB (web link to model);
Model Concept(s): Calcium dynamics; Sleep; Activity Patterns; Oscillations; Bifurcation; Spindles; Audition;
Implementer(s): Schellenberger Costa, Michael [mschellenbergercosta at gmail.com];
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; Thalamus reticular nucleus GABA cell; Neocortex L2/3 pyramidal GLU cell; Neocortex U1 L6 pyramidal corticalthalamic GLU cell; AMPA; NMDA; Gaba; I Calcium; I_K,Na; Na/K pump;
#NM_TC

This repository contains the reference implementation of the model proposed in Schellenberger Costa and Weigenand et al. 2016, available here http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005022

For convenience we utilize MATLAB for data processing and plotting. Therefore the simulation comes with an additional source-file TC_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 in the "Figures" folder within MATLAB, assuming the mex interface is set up. Afterwards simply run the respective plot functions for the different figures.

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

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