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:
Thomas and I talk about what happens in the brain’s visual system when you see something versus imagine it. He uses generative encoding and...
Kanaka and I discuss a few different ways she uses recurrent neural networks to understand how brains give rise to behaviors. We talk about...
