
Matt Panichello
@mattpanichello
postdoc @StanfordBrain
ID: 1356681784361017345
http://panichem.github.io 02-02-2021 19:12:08
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We're also hiring! We are looking for at least one full-time RA/lab manager to join the newly located Shenhav Lab this summer. Please RT and pass this along to your best candidates!

In 2019 I went to Cambridge to debate Máté Lengyel on the usefulness of Marr's Three Levels of Analysis. We've finally published a pair of "CrossTalk" articles in Journal of Physiology laying out our positions. My piece: onlinelibrary.wiley.com/share/author/Y… #MarrNoMore #DownWithThe3Levels

Our paper, Crows ‘count’ the number of self-generated vocalizations, is out! With this amazing team (Katharina Brecht, @lena_veit, A_Nieder), we uncovered that crows are able to control the number of vocalizations produced. science.org/doi/10.1126/sc…

Happy to see my thesis work out today in nature! nature.com/articles/s4158… Josh Merel Jesse Marshall lhq Yuval Tassa Ugne Klibaite Amanda Gellis Greg Wayne Matt Botvinick @BOlveczky Google DeepMind Video credit to the talented Kristian J Herrera



1/5 Excited to finally share our new paper (led by Laura Driscoll, now a group leader at the Allen!) in Nature Neuroscience on modular computation in neural networks! We've explored how artificial recurrent networks handle multiple tasks, offering insights into flexible computation.



We live with the limitations of our memory, but don’t really know where they come from. Our new paper (doi.org/10.1073/pnas.2…) studies "swap errors", which we argue arise during memory manipulation – see thread for more! Tim Buschman, Matt Panichello, Jeff Johnston



Selective attention guides behavior through target enhancement and distractor suppression. Using human ECoG, we uncovered the complementary neural mechanisms enabling this gating process. pnas.org/doi/10.1073/pn… Work with Sabine Kastner, Bob Knight, Ole Jensen @olejensen.bsky.social, Ian Fiebelkorn


Our new paper with Christopher Langdon is just out in Dr Laurie K Mischley! We show that high-dimensional RNNs use low-dimensional circuit mechanisms for cognitive tasks and identify a latent inhibitory mechanism for context-dependent decisions in PFC data. nature.com/articles/s4159…

