Department of Bioengineering, University of Pennsylvania, United States.
Department of Bioengineering, University of Pennsylvania, United States; Department of Neuroscience, University of Pennsylvania, United States; Canadian Institute for Advanced Research, Canada.
Prog Neurobiol. 2019 Apr;175:126-137. doi: 10.1016/j.pneurobio.2019.01.008. Epub 2019 Feb 7.
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML's contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: (1) creating solutions to engineering problems, (2) identifying predictive variables, (3) setting benchmarks for simple models of the brain, and (4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
在过去的几年中，机器学习（ML）在神经科学中的应用迅速增加。在这里，我们回顾了 ML 在系统神经科学的几个领域中的贡献，包括已经实现的和潜在的贡献。我们描述了 ML 在神经科学中的四个主要作用：（1）为工程问题创造解决方案，（2）识别预测变量，（3）为大脑的简单模型设定基准，以及（4）自身作为大脑的模型。其广泛的适用性和易用性表明，机器学习应该成为大多数系统神经科学家工具包的一部分。
