Recent progress in AI has been remarkable. Artificial systems now outperform expert humans at Atari video games, the ancient board game Go, and high-stakes matches of heads-up poker. They can also produce handwriting and speech indistinguishable from those of humans, translate between multiple languages and even reformat your holiday snaps in the style of Van Gogh masterpieces.

These advances are attributed to several factors, including the application of new statistical approaches and the increased processing power of computers. But in a recent Perspective in the journal Neuron, we argue that one often overlooked contribution is the use of ideas from experimental and theoretical neuroscience.

Psychology and neuroscience have played a key role in the history of AI. Founding figures such as Donald Hebb, Warren McCulloch, Marvin Minsky and Geoff Hinton were all originally motivated by a desire to understand how the brain works. In fact, throughout the late 20th Century, much of the key work developing neural networks took place not in mathematics or physics labs, but in psychology and neurophysiology departments.