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Modeling Focal Epileptic Activity in the Wilson–Cowan Model with Depolarization Block

Overview of attention for article published in The Journal of Mathematical Neuroscience, March 2015
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Title
Modeling Focal Epileptic Activity in the Wilson–Cowan Model with Depolarization Block
Published in
The Journal of Mathematical Neuroscience, March 2015
DOI 10.1186/s13408-015-0019-4
Pubmed ID
Authors

Hil G. E. Meijer, Tahra L. Eissa, Bert Kiewiet, Jeremy F. Neuman, Catherine A. Schevon, Ronald G. Emerson, Robert R. Goodman, Guy M. McKhann, Charles J. Marcuccilli, Andrew K. Tryba, Jack D. Cowan, Stephan A. van Gils, Wim van Drongelen

Abstract

Measurements of neuronal signals during human seizure activity and evoked epileptic activity in experimental models suggest that, in these pathological states, the individual nerve cells experience an activity driven depolarization block, i.e. they saturate. We examined the effect of such a saturation in the Wilson-Cowan formalism by adapting the nonlinear activation function; we substituted the commonly applied sigmoid for a Gaussian function. We discuss experimental recordings during a seizure that support this substitution. Next we perform a bifurcation analysis on the Wilson-Cowan model with a Gaussian activation function. The main effect is an additional stable equilibrium with high excitatory and low inhibitory activity. Analysis of coupled local networks then shows that such high activity can stay localized or spread. Specifically, in a spatial continuum we show a wavefront with inhibition leading followed by excitatory activity. We relate our model simulations to observations of spreading activity during seizures. The online version of this article (doi:10.1186/s13408-015-0019-4) contains supplementary material 1.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Netherlands 1 1%
United Kingdom 1 1%
Iran, Islamic Republic of 1 1%
Spain 1 1%
United States 1 1%
Unknown 75 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 26%
Researcher 14 17%
Student > Master 10 12%
Student > Bachelor 6 7%
Professor 6 7%
Other 9 11%
Unknown 15 19%
Readers by discipline Count As %
Neuroscience 19 23%
Engineering 11 14%
Mathematics 7 9%
Agricultural and Biological Sciences 7 9%
Medicine and Dentistry 7 9%
Other 12 15%
Unknown 18 22%