Center for Theoretical Neuroscience, Columbia University, New York, NY, USA.
Center for Neural Science, New York University, New York, NY, USA.
Neuron. 2020 Sep 23;107(6):1048-1070. doi: 10.1016/j.neuron.2020.09.005.
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity, and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics.
人工神经网络 (ANNs) 是机器学习中的重要工具,在神经科学领域受到越来越多的关注。除了提供强大的数据分析技术外,ANNs 还为神经科学家提供了一种新的方法来构建复杂行为、异质神经活动和电路连接的模型,以及探索神经网络的优化,而传统模型则不适合用于这些方面。在这个教学入门中,我们介绍了人工神经网络,并展示了它们如何被成功地用于研究神经科学问题。我们首先讨论了人工神经网络的基本概念和方法。然后,我们重点介绍了如何使这个数学框架更接近神经生物学,详细说明了如何定制分析、结构和学习人工神经网络,以更好地解决大脑研究中的广泛挑战。为了帮助读者获得实践经验,本入门教程还提供了 PyTorch 和 Jupyter Notebook 中的教程风格代码,涵盖了主要主题。