Structure of Neocortical Circuits Group, Max Planck Institute of Neurobiology, Munich-Martinsried, Germany.
Nat Methods. 2013 Jun;10(6):501-7. doi: 10.1038/nmeth.2476.
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.
神经网络是高维图，它们以极高的密度打包到三维神经组织中。因此，全面绘制这些网络是一个主要挑战。尽管最近在容积电子显微镜成像方面的发展使得获取包含几百到几千个神经元的电路的数据成为可能，但数据分析却严重滞后。本文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。
