A Shared Vision for Machine Learning in Neuroscience
A Shared Vision for Machine Learning in Neuroscience
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DOI：
10.1523/jneurosci.0508-17.2018
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发表时间：
2018-02-14
影响因子：
5.3
通讯作者：
Dzirasa, Kafui
中科院分区：
文献类型：
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作者：
Vu, Mai-Anh T.;Adali, Tulay;Dzirasa, Kafui
随着技术的不断进步，神经科学家能够以更高的分辨率收集更多的数据。因此，理解大脑如何工作的瓶颈正在从我们可以收集的数据的数量和类型转向我们实际使用数据的方式。人们越来越有兴趣利用跨分析、测量技术和实验范式水平的大量数据来更深入地了解大脑功能。随着大数据神经科学计划的出现，例如BRAIN计划（Bargmann等人，2014年），人类大脑项目，人类连接组项目和国家精神卫生研究所的研究领域标准倡议。在这些大规模项目中，人们对跨组数据共享进行了大量思考（Poldrack和Gorgolewski，2014; Sejnowski等人，2014年）;然而，即使有这样的数据共享举措、供资机制和基础设施，仍然存在如何连贯地整合所有数据的挑战。在神经科学研究的多个阶段和层次，机器学习作为发现大脑如何工作的分析工具库的补充，具有很大的希望。
With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.