To learn how brains compute, we need to experimentally adjudicate among competing computational hypotheses. How do we do this in the age of complex neural network models? New review paper: “Making models disagree to learn how brains compute” with @talgolanneuro.bsky.social @heikoschuett.bsky.social

How can we design experiments that make computational models disagree? One section of our new @natrevneuro.nature.com Review with @kriegeskorte.bsky.social and @heikoschuett.bsky.social examines studies that used stimulus sets designed to elicit distinct predictions from competing models. 1/16

Videos of amazing workshop "Toward AI with Human Level Efficiency" organized by Ilker Yildirim et al. ... cnclgithub.github.io/yale-ai-work... with T. Brooke-Wilson, @nancykanwisher.bsky.social, @lisik.bsky.social, @joshtenenbaum.bsky.social, @toddgureckis.bsky.social, McCoy, Lew, and Kaelbling.

In this podcast interview with speed cuber Sanjay Adireddi, he and I discuss an imaginary research program on how humans solve Rubik's cube... www.youtube.com/watch?v=m1uJ...

The Computational Basis of Vision | Dr. Nikolaus Kriegeskorte

YouTube video by Neurocracy

youtube.com

I find agentic coding a powerful amplifier for teaching. This term, I taught with @simonprinceai.bsky.social 's excellent book, Understanding Deep Learning. The book's interactive figures are brilliant, but some students struggle to decipher surfaces presented top-down as 2D heat maps. 1/5