%0 Journal Article
%T Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms
%A Lorenzo L. Pesce
%A Hyong C. Lee
%A Mark Hereld
%A Sid Visser
%A Rick L. Stevens
%A Albert Wildeman
%A Wim van Drongelen
%J Computational and Mathematical Methods in Medicine
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/182145
%X Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers. 1. Introduction Biological systems are complex and networks of neurons are no exception. Simulating these systems provides a means for testing configurations that would be difficult or impractical to replicate in vitro or in vivo. Taking a computational approach can also enable sweeps in configuration space that would otherwise be intractable, as it often requires extremely large sample sizes to achieve any degree of significance because of the inherently low power of multidimensional exploratory data analysis [1, 2]. One approach to study how the many possible combinations of parameter values measured can affect experimental findings across scales is via modeling and large-scale simulations [3¨C17]. Furthermore, the behavior of macroscopic neural tissues depends on factors determined across a range of physical scales, from microscale (a few to a hundred neurons) to the meso- and macroscales (the emergent behavior produced by the simultaneous interaction of millions of neurons). Science has dramatically improved our ability to study the micro- and macroscales independently, but to date only simulations are capable of trying to infer the emergent macroscopic behavior from microscopic properties, even though more sophisticated tools are being developed [18, 19]. In fact, the
%U http://www.hindawi.com/journals/cmmm/2013/182145/