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Journal of Computational Neuroscience

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01-12-2014

A neural mass model based on single cell dynamics to model pathophysiology

Authors: Bas-Jan Zandt, Sid Visser, Michel J. A. M. van Putten, Bennie ten Haken

Published in: Journal of Computational Neuroscience | Issue 3/2014

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Abstract

Neural mass models are successful in modeling brain rhythms as observed in macroscopic measurements such as the electroencephalogram (EEG). While the synaptic current is explicitly modeled in current models, the single cell electrophysiology is not taken into account. To allow for investigations of the effects of channel pathologies, channel blockers and ion concentrations on macroscopic activity, we formulate neural mass equations explicitly incorporating the single cell dynamics by using a bottom-up approach. The mean and variance of the firing rate and synaptic input distributions are modeled. The firing rate curve (F(I)-curve) is used as link between the single cell and macroscopic dynamics. We show that this model accurately reproduces the behavior of two populations of synaptically connected Hodgkin-Huxley neurons, also in non-steady state.

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Available only for authorised users

The simulation code is available from modelDB (Hines et al. 2004), accession nr. 155130 and Researchgate www.researchgate.net/profile/Bas-Jan_Zandt

Available from ModelDB, accession nr. 154739.

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Title: A neural mass model based on single cell dynamics to model pathophysiology
Authors: Bas-Jan Zandt
Sid Visser
Michel J. A. M. van Putten
Bennie ten Haken
Publication date: 01-12-2014
Publisher: Springer US
Published in: Journal of Computational Neuroscience / Issue 3/2014
Print ISSN: 0929-5313
Electronic ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-014-0517-5

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