Department of Experimental Psychology, University of Oxford, Oxford, UK.
Nat Rev Neurosci. 2021 Jan;22(1):55-67. doi: 10.1038/s41583-020-00395-8. Epub 2020 Nov 16.
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革命。机器学习和人工智能研究的最新进展为思考神经计算开辟了新的途径。许多研究人员对深度神经网络可能为生物大脑提供感知、认知和行为理论的可能性感到兴奋。这种方法有可能从根本上改变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习的，而不是由研究人员赋予的。如果是这样，神经科学家如何使用深度网络来模拟和理解生物大脑？那些试图描述计算或神经编码的神经科学家，或者那些希望理解感知、注意力、记忆和执行功能的神经科学家，前景如何？在这篇观点文章中，我们的目标是为深度学习时代的系统神经科学研究提供路线图。我们讨论了在人工和生物系统中比较行为、学习动态和神经表示的概念和方法学挑战，并强调了由于机器学习的最新进展而直接出现的新的神经科学研究问题。
