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ZENODO
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License: CC BY
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License: CC BY
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Emergence and fragmentation of the alpha-band driven by neuronal network dynamics

Authors: Zonca, Lou;

Emergence and fragmentation of the alpha-band driven by neuronal network dynamics

Abstract

We show that connecting three neuronal populations to reproduce the interactions in the thalamo-cortical loop, is the minimal topology required to yield the emergence of specific oscillatory rhythms such as the alpha band observed in the EEG during general anesthesia under propofol. We investigate the emergence and fragmentation of the alpha band and its relation to Up and Down states in cortical networks. We provide here the Matlab code for models of 1, 2 and 3 interacting neuronal populations (excitatory and/or inhibitory) that we used to generate the results presented in Emergence and fragmentation of the alpha-band driven by neuronal network dynamics by L. Zonca & D. Holcman published in PLoS Computational Biology (2021, in production). The Matlab source code is organized in 3 sub folders: *01_Data* Contains the simulated data presented in the paper, the model(s) parameters, and the 2 real human EEG data traces from https://vitaldb.net/ presented in fig. 1 of the paper. *02_Functions* Contains 1 function file per model (1-2&3 populations) that can be run individually + the functions used to segment the time series into Up and Down states periods and to collect the statistics plotted in the paper. *03_Figures* Contains 1 script per figure of the paper, these script can be run directly from the root of the code folder and will load the correct parameter values and data (if necessary) to reproduce the figures presented in the article. Example stochastic time-series for each model can also be simulated using these scripts. Please quote the Zenodo Object Identifier in publications when using the code.

Keywords

Alpha band, Up and Down states, Thalamo-cortical loop, General anesthesia, Stochastic dynamical systems, Brain oscillations, Mathematical modeling, EEG, Propofol

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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