Mazviita and I discuss the growing divide between prediction and understanding as neuroscience models and deep learning networks become bigger and more complex. She describes her non-factive account of understanding, which among other things suggests that the best predictive models may deliver less understanding. We also discuss the brain as a computer metaphor, and whether it's really possible to ignore all the traditionally "non-computational" parts of the brain like metabolism and other life processes.
Show notes:
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...
David and John discuss some of the concepts from their recent paper Two Views on the Cognitive Brain, in which they argue the recent...
Show notes: Tim’s Neuroscience homepage: Follow Tim on Twitter: @behrenstim. Edward Tolman’s cognitive maps work: Cognitive maps in rats and men. Place Cells and...
