Lichtman Jeff W, Pfister Hanspeter, Shavit Nir
1] Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA. [2] Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA.
1] Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA. [2] School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA.
Nat Neurosci. 2014 Nov;17(11):1448-54. doi: 10.1038/nn.3837. Epub 2014 Oct 28.
The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes. Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s with a the study of the small nervous system of a worm and has recently garnered general interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains. However, modern connectomics produces 'big data', unprecedented quantities of digital information at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions. Here we describe some of the key difficulties that may arise and provide suggestions for managing them.
神经系统的结构极其复杂,因为单个神经元与数百甚至数千个其他细胞相互连接,形成的网络可延伸至很大范围。在突触连接层面绘制此类网络,即一个名为连接组学的领域,始于20世纪70年代对一种蠕虫的小型神经系统的研究,并且由于技术和计算方面的进步,最近引起了广泛关注,这些进步使电子显微镜数据的收集自动化,并提供了绘制甚至大型哺乳动物大脑图谱的可能性。然而,现代连接组学产生了“大数据”,以前所未有的速度产生了前所未有的大量数字信息,并且如同当时的基因组学一样,将需要突破性的算法和计算解决方案。在这里,我们描述了可能出现的一些关键困难,并提供了应对这些困难的建议。