Cellular-resolution connectomics: challenges of dense neural circuit reconstruction
Cellular-resolution connectomics: challenges of dense neural circuit reconstruction
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DOI：
10.1038/nmeth.2476
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发表时间：
2013-06-01
期刊：
影响因子：
48
通讯作者：
Helmstaedter, Moritz
中科院分区：
文献类型：
--
作者：
Helmstaedter, Moritz
神经元网络是以极高密度填充到三维神经组织中的高维图。因此，全面绘制这些网络是一项重大挑战。虽然体积电子显微镜成像的最新发展已经使得包括几百到几千个神经元的电路的数据采集变得可行，但数据分析却严重滞后。该观点的目的是总结和量化细胞分辨率连接组学中数据分析的挑战，并描述涉及在线众包和机器学习方法的当前解决方案。
Neuronal networks are high-dimensional graphs that are packed into three-dimensional nervous tissue at extremely high density. Comprehensively mapping these networks is therefore a major challenge. Although recent developments in volume electron microscopy imaging have made data acquisition feasible for circuits comprising a few hundreds to a few thousands of neurons, data analysis is massively lagging behind. The aim of this perspective is to summarize and quantify the challenges for data analysis in cellular-resolution connectomics and describe current solutions involving online crowd-sourcing and machine-learning approaches.