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.
Notes:
Support the show to get full episodes and join the Discord community. Sri and Mei join me to discuss how including principles of neuromodulation...
Support the show to get full episodes and join the Discord community. Johannes (Yogi) is a freelance philosopher, researcher & educator. We discuss many...
Support the show to get full episodes and join the Discord community. David runs his lab at UCLA where he's also a distinguished professor. ...
