  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.
