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.
Highlights
The structure of neurons and networks in the brain is known to change continuously over time
2013) that has been used in several detailed studies of structure-function interplay (Xylouris et al, 2007; Hansen et al, 2008; Nägel et al, 2008, 2009; Wittmann et al, 2009; Grillo et al, 2010; Muha et al, 2011). To study this anatomy-high-performance framework we present a study of synapse loss vs. signal synchronicity and the influence on somatic calcium signals as well as simulations of large and detailed network simulations (10,000 neurons, each neuron containing 574–586 degree of freedom) of a neocortical column synthetically generated with NeuGen
In this paper we presented studies of electrical and biochemical signals in single cells and networks to investigate the interplay between synapse loss and signaling synchrony
Summary
The structure of neurons and networks in the brain is known to change continuously over time. Experimental research draws from microscopy techniques that can make morphology and spatio-temporal signals visible (Spacek and Harris, 1997; Arellano et al, 2007; Chen et al, 2008), theoretical work in Computational Neuroscience has brought forth an abundant spread of cellular and network models, many of them rely on a spatial representation of neurons and networks (Bower and Beeman, 1997; Hines and Carnevale, 1997; Balls et al, 2004; Gewaltig and Diesmann, 2007; Andrews et al, 2010). More than 30,000 cell reconstructions are freely available on this platform
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