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.
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Mentioned in the show The two papers we discuss: The Roles of Supervised Machine Learning in Systems Neuroscience Machine learning for neural decoding Kording...
