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Panel on "What neural systems can teach us about building better machine learning systems"
Timothy Lillicrap · James J DiCarlo · Christopher Rozell · Viren Jain · Nathan Kutz · William Gray Roncal · Bingni Brunton

Sat Dec 09 11:40 AM -- 12:20 PM (PST) @
in BigNeuro 2017: Analyzing brain data from nano to macroscale »

Author Information

Timothy Lillicrap (Google DeepMind)
James J DiCarlo (Massachusetts Institute of Technology)

Prof. DiCarlo received his Ph.D. in biomedical engineering and his M.D. from Johns Hopkins in 1998, and did his postdoctoral training in primate visual neurophysiology at Baylor College of Medicine. He joined the MIT faculty in 2002. He is a Sloan Fellow, a Pew Scholar, and a McKnight Scholar. His lab’s research goal is a computational understanding of the brain mechanisms that underlie object recognition. They use large-scale neurophysiology, brain imaging, optogenetic methods, and high-throughput computational simulations to understand how the primate ventral visual stream is able to untangle object identity from other latent image variables such as object position, scale, and pose. They have shown that populations of neurons at the highest cortical visual processing stage (IT) rapidly convey explicit representations of object identity, and that this ability is reshaped by natural visual experience. They have also shown how visual recognition tests can be used to discover new, high-performing bio-inspired algorithms. This understanding may inspire new machine vision systems, new neural prosthetics, and a foundation for understanding how high-level visual representation is altered in conditions such as agnosia, autism and dyslexia.

Christopher Rozell (Georgia Institute of Technology)
Viren Jain (Google)
Nathan Kutz (University of Washington)
William Gray Roncal (Johns Hopkins University)
Bingni Brunton (University of Washington)

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