Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains. This approach has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, and not endowed by the researcher. If so, how can neuroscientists use deep networks to model and understand biological brains? What is the outlook for neuroscientists who seek to characterize computations or neural codes, or who wish to understand perception, attention, memory and executive functions? In this Perspective, our goal is to offer a road map for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics and neural representations in artificial and biological systems, and we highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning.

神经科学研究正在经历一场小小的革命。机器学习和人工智能研究的最新进展为神经计算开辟了新的思维方式。深度神经网络可能为生物大脑提供感知，认知和动作理论的可能性使许多研究人员感到兴奋。这种方法有可能从根本上重塑我们理解神经系统的方法，因为深度网络执行的计算是从经验中学到的，而不是研究人员所赋予的。如果是这样，神经科学家如何利用深层网络来建模和理解生物大脑？寻求表征计算或神经代码或希望了解感知，注意力，记忆和执行功能的神经科学家的前景如何？从这个角度来看，我们的目标是为深度学习时代的系统神经科学研究提供一个路线图。我们讨论了在人工和生物系统中比较行为，学习动力学和神经表示形式的概念和方法上的挑战，并重点介绍了由于机器学习最新进展而直接导致的神经科学新研究问题。