Stanford Radio

Neuroscientist Kalanit Grill-Spector studies the physiology of human vision and says that the ways computers and people see are in some ways similar, but in other ways quite different. In fact, she says, rapid advances in computational modeling, such as deep neural networks, applied to brain data and new imaging technologies, like quantitative MRI and diffusion MRI, are revolutionizing our understanding of how the human brain sees. We’re unraveling how the brain “computes” visual information, as Grill-Spector tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.