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Analytic Modeling of Neural Tissue: I. A Spherical Bidomain

Overview of attention for article published in The Journal of Mathematical Neuroscience, September 2016
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Title
Analytic Modeling of Neural Tissue: I. A Spherical Bidomain
Published in
The Journal of Mathematical Neuroscience, September 2016
DOI 10.1186/s13408-016-0041-1
Pubmed ID
Authors

Benjamin L. Schwartz, Munish Chauhan, Rosalind J. Sadleir

Abstract

Presented here is a model of neural tissue in a conductive medium stimulated by externally injected currents. The tissue is described as a conductively isotropic bidomain, i.e. comprised of intra and extracellular regions that occupy the same space, as well as the membrane that divides them, and the injection currents are described as a pair of source and sink points. The problem is solved in three spatial dimensions and defined in spherical coordinates [Formula: see text]. The system of coupled partial differential equations is solved by recasting the problem to be in terms of the membrane and a monodomain, interpreted as a weighted average of the intra and extracellular domains. The membrane and monodomain are defined by the scalar Helmholtz and Laplace equations, respectively, which are both separable in spherical coordinates. Product solutions are thus assumed and given through certain transcendental functions. From these electrical potentials, analytic expressions for current density are derived and from those fields the magnetic flux density is calculated. Numerical examples are considered wherein the interstitial conductivity is varied, as well as the limiting case of the problem simplifying to two dimensions due to azimuthal independence. Finally, future modeling work is discussed.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 22%
Other 1 11%
Student > Doctoral Student 1 11%
Student > Ph. D. Student 1 11%
Student > Bachelor 1 11%
Other 2 22%
Unknown 1 11%
Readers by discipline Count As %
Medicine and Dentistry 2 22%
Engineering 2 22%
Computer Science 1 11%
Mathematics 1 11%
Neuroscience 1 11%
Other 1 11%
Unknown 1 11%