Neuron ( IF 16.2 ) Pub Date : 2020-09-23 , DOI: 10.1016/j.neuron.2020.09.005 Guangyu Robert Yang , Xiao-Jing Wang
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.
中文翻译:
神经科学家的人工神经网络:入门
人工神经网络(ANN)是机器学习中必不可少的工具,在神经科学领域引起了越来越多的关注。除了提供强大的数据分析技术外,人工神经网络还为神经科学家提供了一种新方法,可以为复杂行为,异构神经活动和电路连通性建立模型,并以传统模型未设计的方式探索神经系统的优化。在此教学入门中,我们介绍了人工神经网络,并演示了如何将其有效地用于研究神经科学问题。我们首先讨论人工神经网络的基本概念和方法。然后,以使该数学框架更接近神经生物学为重点,我们详细介绍了如何自定义ANN的分析,结构和学习,以更好地应对大脑研究中的各种挑战。