title: Anatomically Detailed and Large-Scale Simulations Studying Synapse Loss and Synchrony Using NeuroBox.
abstract: The morphology of neurons and networks plays an important role in processing electrical and biochemical signals. Based on neuronal reconstructions, which are becoming abundantly available through databases such as NeuroMorpho.org, numerical simulations of Hodgkin-Huxley-type equations, coupled to biochemical models, can be performed in order to systematically investigate the influence of cellular morphology and the connectivity pattern in networks on the underlying function. Development in the area of synthetic neural network generation and morphology reconstruction from microscopy data has brought forth the software tool NeuGen. Coupling this morphology data (either from databases, synthetic, or reconstruction) to the simulation platform UG 4 (which harbors a neuroscientific portfolio) and VRL-Studio, has brought forth the extendible toolbox NeuroBox. NeuroBox allows users to perform numerical simulations on hybrid-dimensional morphology representations. The code basis is designed in a modular way, such that e.g., new channel or synapse types can be added to the library. Workflows can be specified through scripts or through the VRL-Studio graphical workflow representation. Third-party tools, such as ImageJ, can be added to NeuroBox workflows. In this paper, NeuroBox is used to study the electrical and biochemical effects of synapse loss vs. synchrony in neurons, to investigate large morphology data sets within detailed biophysical simulations, and used to demonstrate the capability of utilizing high-performance computing infrastructure for large scale network simulations. Using new synapse distribution methods and Finite Volume based numerical solvers for compartment-type models, our results demonstrate how an increase in synaptic synchronization can compensate synapse loss at the electrical and calcium level, and how detailed neuronal morphology can be integrated in large-scale network simulations.
authors.affiliation: Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe University Frankfurt am Main, Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Breit
authors.affiliation: Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe University Frankfurt am Main, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Stepniewski
authors.affiliation: Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe UniversityFrankfurt am Main, Germany; Department of Mathematics, Temple UniversityPhiladelphia, PA, USA.
authors.forename: Stephan
authors.initials: S
authors.lastname: Grein
authors.affiliation: Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe University Frankfurt am Main, Germany.
authors.forename: Pascal
authors.initials: P
authors.lastname: Gottmann
authors.affiliation: Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe University Frankfurt am Main, Germany.
authors.forename: Lukas
authors.initials: L
authors.lastname: Reinhardt
authors.affiliation: Computational Neuroscience, Department for Computer Science and Mathematics, Goethe Center for Scientific Computing, Goethe UniversityFrankfurt am Main, Germany; Department of Mathematics, Temple UniversityPhiladelphia, PA, USA.
authors.forename: Gillian
authors.initials: G
authors.lastname: Queisser
keywords: HPC; anatomy; cable equation; electrical scale; large-scale neuronal networks; reconstruction; simulation; synaptic plasticity