用于神经网络数据分析的分层矩阵查看器

Philipp Harth, Sumit K. Vohra, D. Udvary, M. Oberländer, H. Hege, D. Baum
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引用次数: 2

摘要

对大脑网络的分析是神经生物学研究的核心。在这种情况下,经常出现以下任务:(1)了解重建神经组织体积的细胞组成,以确定大脑网络的节点;(2)统计量化连通性特征;(3)将这些结果与数学模型的预测结果进行比较。我们提出了一个交互式的、可视化支持的框架来完成这些任务。它的核心组件,分层矩阵查看器,允许用户可视化不同聚集水平的神经元的细胞和/或连接特性的分布。我们通过分析大鼠桶状皮层和人类颞叶皮层的神经网络数据的四个案例来证明它的应用。
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A Stratification Matrix Viewer for Analysis of Neural Network Data
The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.
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