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neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome

机译:neuroVIISAS:接近大鼠连接组的多尺度模拟

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neuroVIISAS is a generic platform which allows the integration of neuroontologies, mapping functions for brain atlas development, and connectivity data administration; all of which are required for the analysis of structurally and neurobiologically realistic simulations of networks. What makes neuroVIISAS unique is the ability to integrate neuroontologies, image stacks, mappings, visualizations, analyzes and simulations to use them for modelling and simulations. Based on the analysis of over 2020 tracing studies, atlas terminologies and registered histological stacks of images, neuroVIISAS permits the definition of neurobiologically realistic networks that are transferred to the simulation engine NEST. The analysis on a local and global level, the visualization of connectivity data and the results of simulations offer new possibilities to study structural and functional relationships of neural networks. This paper describes the major components and techniques of how to analyse, visualize and simulate with neuroVIISAS shown on a model network at a coarse CNS level (106 regions, 1566 connections) out of 13681 regions and 134043 connections of the left and right part of the CNS. This network of major components of the left and right hemisphere has small-world properties of the Watts-Strogatz model. Furthermore, synchronized subpopulations, oscillations of rate distributions and a time shift of population activities of the left and right hemisphere were observed in the neurocomputational simulations. In summary, a generic platform has been developed that realizes data-analysis-visualization integration for the exploration of network dynamics on multiple levels.
机译:NeuroVIISAS是一个通用平台,可以集成神经本体论,用于脑图谱开发的映射功能以及连接性数据管理。所有这些都是分析网络的结构和神经生物学现实模拟所必需的。使NeuroVIISAS独树一帜的是能够集成神经本体论,图像堆栈,映射,可视化,分析和仿真以将它们用于建模和仿真的能力。基于对2020年以上追踪研究,地图集术语和已注册图像的组织学堆栈的分析,neuroVIISAS允许定义神经生物学现实网络,并将其转移到模拟引擎NEST。局部和全局级别的分析,连通性数据的可视化以及模拟的结果为研究神经网络的结构和功能关系提供了新的可能性。本文介绍了如何使用模型网络上显示的NeuroVIISAS在粗糙的CNS级别(106个区域,1566个连接)中的13681个区域和134043个左右部分的连接中进行分析,可视化和模拟的主要组件和技术。 CNS。左右半球的主要组成部分网络具有Watts-Strogatz模型的小世界性质。此外,在神经计算模拟中观察到同步的亚群,速率分布的振荡以及左右半球的种群活动的时移。总而言之,已经开发了一种通用平台,该平台实现了数据分析-可视化集成,以在多个级别上探索网络动态。

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