Uri and I discuss his recent perspective that conceives of brains as super-over-parameterized models that try to fit everything as exactly as possible rather than trying to abstract the world into usable models. He was inspired by the way artificial neural networks overfit data when they can, and how evolution works the same way on a much slower timescale.
Show notes:
Kyle and I talk about his work modeling the basal ganglia and its circuitry to control whether we take an action and how we...
Jay's homepage at Stanford.Implementing mathematical reasoning in machines:The video lecture.The paper.Parallel Distributed Processing by Rumelhart and McClelland.Complimentary Learning Systems Theory and Its Recent Update.Episode...
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