DiCarlo and Yamins, who now runs his own lab at Stanford University, 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.
Neuroscientists are finding that deep-learning networks, often criticized as “black boxes,” can be good models for the organization of living brains.
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