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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:
Support the show to get full episodes and join the Discord community. Henry and I discuss why he thinks neuroscience is in a crisis...
Mentioned in the show: Follow Niko on twitter @KriegeskorteLab. Visit his lab website. The Cognitive Computational Neuroscience Conference. The review papers we base the...
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