1. A locally adaptive time stepping algorithm for the solution to reaction diffusion equations on branched structures
- Author
-
Richard Alexander Kublik and David L. Chopp
- Subjects
Independent equation ,Applied Mathematics ,Numerical analysis ,Domain decomposition methods ,Context (language use) ,010103 numerical & computational mathematics ,01 natural sciences ,Action (physics) ,Hodgkin–Huxley model ,03 medical and health sciences ,Computational Mathematics ,0302 clinical medicine ,Simultaneous equations ,Reaction–diffusion system ,0101 mathematics ,Algorithm ,030217 neurology & neurosurgery ,Mathematics - Abstract
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.
- Published
- 2016