Support the Podcast
Andrew and I discuss his work exploring how various facets of deep networks contribute to their function, i.e. deep network theory. We talk about what he’s learned by studying linear deep networks and asking how depth and initial weights affect learning dynamics, when replay is appropriate (and when it’s not), how semantics develop, and what it all might tell us about deep learning in brains.
Show notes:
A few recommended texts to dive deeper:
Mentioned in the show: Dan’s Stanford Neuroscience and Artificial Intelligence Laboratory: The 2 papers we discuss Performance-optimized hierarchical models predict neural responses in higher...
Support the show to get full episodes and join the Discord community. Patryk and I discuss his wide-ranging background working in both the neuroscience...
K, Josh, and I were postdocs together in Jeff Schall’s and Geoff Woodman’s labs. K and Josh had backgrounds in psychology and were getting...