  Neural network models can now recognise images, understand text, translate
languages, and play many human games at human or superhuman levels. These
systems are highly abstracted, but are inspired by biological brains and use
only biologically plausible computations. In the coming years, neural networks
are likely to become less reliant on learning from massive labelled datasets,
and more robust and generalisable in their task performance. From their
successes and failures, we can learn about the computational requirements of
the different tasks at which brains excel. Deep learning also provides the
tools for testing cognitive theories. In order to test a theory, we need to
realise the proposed information-processing system at scale, so as to be able
to assess its feasibility and emergent behaviours. Deep learning allows us to
scale up from principles and circuit models to end-to-end trainable models
capable of performing complex tasks. There are many levels at which cognitive
neuroscientists can use deep learning in their work, from inspiring theories to
serving as full computational models. Ongoing advances in deep learning bring
us closer to understanding how cognition and perception may be implemented in
the brain -- the grand challenge at the core of cognitive neuroscience.
