论文标题
如果是答案，那么问题是什么？
If deep learning is the answer, then what is the question?
论文作者
论文摘要
神经科学研究正在进行一场小革命。机器学习和人工智能（AI）研究的最新进展开辟了有关神经计算的新方法。许多研究人员对深层神经网络可能为生物大脑提供感知，认知和行动的理论感到兴奋。这种观点有可能从根本上重塑我们理解神经系统的方法，因为深层网络执行的计算是从经验中学到的，而不是研究人员赋予的。如果是这样，神经科学家如何使用深层网络来建模和了解生物学大脑？试图表征计算或神经代码的神经科学家的前景是什么，或者希望了解感知，注意力，记忆和执行功能？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路线图。我们讨论了人工和生物系统中比较行为，学习动力和神经表现形式的概念和方法论挑战。我们重点介绍了为神经科学而出现的新研究问题，这是机器学习最近进步的直接结果。
Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence (AI) 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 perspective has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, 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 characterise computations or neural codes, or who wish to understand perception, attention, memory, and executive functions? In this Perspective, our goal is to offer a roadmap for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics, and neural representation in artificial and biological systems. We highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning.
