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This 33-minute talk by Dan Yamins from the Wu Tsai Neurosciences Institute at Stanford explores the frontier of creating digital models of the human brain using AI. Discover how researchers are applying foundation model approaches similar to those powering ChatGPT to create "digital twins" of brain regions, as demonstrated by Stanford researcher Andreas Tolias who successfully modeled a mouse visual cortex that can predict neuronal responses to new visual inputs. Explore the profound implications of extending these techniques to human brain simulations, including what such models might reveal about the fundamental algorithms of perception and cognition. Learn about the convergence of neuroscience and artificial intelligence, how neural networks help us understand human brain function, and the latest research in areas like auditory behavior replication, grid-like representations in artificial agents, and how the brain encodes abstract state-space representations.
