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Front Comput Neurosci. 2016 Sep 12;10:97. doi: 10.3389/fncom.2016.00097. eCollection 2016.

Efficient Integration of Coupled Electrical-Chemical Systems in Multiscale Neuronal Simulations.

Author information

1
Science for Life Laboratory, Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of TechnologyStockholm, Sweden; National Centre for Biological SciencesBangalore, India; Manipal UniversityManipal, India.
2
National Centre for Biological Sciences Bangalore, India.
3
PDC Center for High-Performance Computing, KTH Royal Institute of TechnologyStockholm, Sweden; International Neuroinformatics Coordinating Facility, Karolinska InstituteStockholm, Sweden.
4
Science for Life Laboratory, Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of TechnologyStockholm, Sweden; Science for Life Laboratory, Department of Numerical Analysis and Computer Science, Stockholm UniversityStockholm, Sweden; Department of Neuroscience, Karolinska InstituteStockholm, Sweden.
5
Department of Mathematics, School of Engineering Sciences, KTH Royal Institute of Technology Stockholm, Sweden.

Abstract

Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required. The latter may become a challenging task for several reasons. First, the components of a multiscale system usually span different spatial and temporal scales, such that rigorous analysis of possible coupling solutions is required. Then, the components can be defined by different mathematical formalisms. For certain classes of problems a number of coupling mechanisms have been proposed and successfully used. However, a strict mathematical theory is missing in many cases. Recent work in the field has not so far investigated artifacts that may arise during coupled integration of different approximation methods. Moreover, in neuroscience, the coupling of widely used numerical fixed step size solvers may lead to unexpected inefficiency. In this paper we address the question of possible numerical artifacts that can arise during the integration of a coupled system. We develop an efficient strategy to couple the components comprising a multiscale test problem in neuroscience. We introduce an efficient coupling method based on the second-order backward differentiation formula (BDF2) numerical approximation. The method uses an adaptive step size integration with an error estimation proposed by Skelboe (2000). The method shows a significant advantage over conventional fixed step size solvers used in neuroscience for similar problems. We explore different coupling strategies that define the organization of computations between system components. We study the importance of an appropriate approximation of exchanged variables during the simulation. The analysis shows a substantial impact of these aspects on the solution accuracy in the application to our multiscale neuroscientific test problem. We believe that the ideas presented in the paper may essentially contribute to the development of a robust and efficient framework for multiscale brain modeling and simulations in neuroscience.

KEYWORDS:

adaptive time step integration; backward differentiation formula; co-simulation; coupled integration; coupled system; multiscale modeling; multiscale simulation; parallel numerical integration

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