Jim and I discuss his reverse engineering approach to visual intelligence, using deep models optimized to perform object recognition tasks. We talk about the history of his work developing models to match the neural activity in the ventral visual stream, how deep learning connects with those models, and some of his recent work: adding recurrence to the models to account for more difficult object recognition, using unsupervised learning to account for plasticity in the visual stream, and controlling neural activity by creating specific images for subjects to view.
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Pieter and I discuss his ongoing quest to figure out how the brain implements learning that solves the credit assignment problem, like backpropagation does...
Support the show to get full episodes and join the Discord community. Àlex Gómez-Marín heads The Behavior of Organisms Laboratory at the Institute of...
In the 4th installment of our 100th episode celebration, previous guests responded to the question: What ideas, assumptions, or terms do you think is...
