[Neuroscientists James DiCarlo and Daniel Yamins] are part of a coterie of neuroscientists using deep neural networks to make sense of the brain’s architecture. In particular, scientists have struggled to understand the reasons behind the specializations within the brain for various tasks. They have wondered not just why different parts of the brain do different things, but also why the differences can be so specific: Why, for example, does the brain have an area for recognizing objects in general but also for faces in particular? Deep neural networks are showing that such specializations may be the most efficient way to solve problems.
Similarly, researchers have demonstrated that the deep networks most proficient at classifying speech, music and simulated scents have architectures that seem to parallel the brain’s auditory and olfactory systems… All these results hint that the structures of living neural systems embody certain optimal solutions to the tasks they have taken on.
These successes are all the more unexpected given that neuroscientists have long been skeptical of comparisons between brains and deep neural networks, whose workings can be inscrutable. “Honestly, nobody in my lab was doing anything with deep nets [until recently],” said the MIT neuroscientist Nancy Kanwisher. “Now, most of them are training them routinely.”