TY - JOUR

T1 - A locally adaptive time stepping algorithm for the solution to reaction diffusion equations on branched structures

AU - Kublik, Richard A.

AU - Chopp, David L.

N1 - Publisher Copyright:
© 2016, Springer Science+Business Media New York.

PY - 2016/6/1

Y1 - 2016/6/1

N2 - In this paper, we present a numerical method for solving reaction-diffusion equations on one dimensional branched structures. Through the use of a simple domain decomposition scheme, the many branches are decoupled so that the equations can be solved as a system of smaller problems that are tri-diagonal. This technique allows for locally adaptive time stepping, in which the time step used in each branch is determined by local activity. Though the method is presented in the specific context of electrical activity in neural systems, it is sufficiently general that it can be applied to other classes of reaction-diffusion problems and higher dimensions. Information in neurons, which can be effectively modeled as one-dimensional branched structures, is carried in the form of electrical impulses called action potentials. The model equations, based on the Hodgkin-Huxley cable equations, are a set of reaction equations coupled to a single diffusion process. Locally adaptive time stepping schemes are well suited to neural simulations due to the spatial localization of activity. The algorithm significantly reduces the computational cost compared to existing methods, especially for large scale simulations.

AB - In this paper, we present a numerical method for solving reaction-diffusion equations on one dimensional branched structures. Through the use of a simple domain decomposition scheme, the many branches are decoupled so that the equations can be solved as a system of smaller problems that are tri-diagonal. This technique allows for locally adaptive time stepping, in which the time step used in each branch is determined by local activity. Though the method is presented in the specific context of electrical activity in neural systems, it is sufficiently general that it can be applied to other classes of reaction-diffusion problems and higher dimensions. Information in neurons, which can be effectively modeled as one-dimensional branched structures, is carried in the form of electrical impulses called action potentials. The model equations, based on the Hodgkin-Huxley cable equations, are a set of reaction equations coupled to a single diffusion process. Locally adaptive time stepping schemes are well suited to neural simulations due to the spatial localization of activity. The algorithm significantly reduces the computational cost compared to existing methods, especially for large scale simulations.

KW - Adaptive time-stepping

KW - Hodgkin-Huxley equation

KW - Reaction-diffusion equations

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U2 - 10.1007/s10444-015-9437-9

DO - 10.1007/s10444-015-9437-9

M3 - Article

AN - SCOPUS:84957545845

SN - 1019-7168

VL - 42

SP - 621

EP - 649

JO - Advances in Computational Mathematics

JF - Advances in Computational Mathematics

IS - 3

ER -