Computational neuroscientist bringing machine and neural learning closer together @OxNeuro @OxfordDPAG | #firstgen | @ERC_Research Grantee 🇵🇹🇪🇺🇬🇧🌳

Oxford, UK
Joined December 2013
Can we find shared neural spaces across subjects? Yes! We introduce Multi-encoder-decoder-VAE (MED-VAE), a method for model-guided cross-subject shared spaces. Fantastic work led by @pangeliki #CCN2026 (selected for a talk) 🧵 1/6
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Adding a new subject is cheap: we freeze the shared space and train only a new subject encoder/decoder on their own images. With 20% of the new subject's data, we recover ~98% of the alignment reached with all of it, and 90% of the noise ceiling in self-reconstruction. 5/6
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Rui Ponte Costa retweeted
[Hugging Face release] We’re releasing the dataset behind Reason to Play! I’m incredibly proud of our team for reaching this milestone. @sreejan_kumar Austin Andrews @LHuntNeuro @summerfieldlab Josh Tenenbaum @somnirons @marcelomattar @momchiltomov
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Rui Ponte Costa retweeted
TL;DR: This is one of the most important and exciting opportunities in AI on the planet - please read on. The British Open-ended Learning & Discovery Lab is creating the perfect place for paradigm breaking AI research in the name of open-source and open-science. We have agency, we funding, we have unprecedented amounts of compute*, but WE NEED YOU! ..and we have created the dream job for you: The BOLD Fellow. This job combines a fast-moving, high agency, collaborative environment with full academic freedom and a salary that pays the bills. Apply by noon UK time on the 15th of September for this once in a lifetime opportunity to shape the history of our field and of our planet: my.corehr.com/pls/uoxrecruit… *by academic standards
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Rui Ponte Costa retweeted
Two major announcements for the #DigitalBrainProject: 🥇9 teams were selected in the first round 🚀we’re scaling the funding to $15M More details below 👇: digitalbrainproject.org
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This recent paper posted by @MillerLabMIT got my attention. It shows how abstraction could emerge in PFC. If you are in CS/AI, it's a fun one. The paper is hard, but the main idea can be reduced to a low-d space, so I made a little animation. Here is my toy model and notes:🧵
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Our latest story is finally in preprint! 🧠✨ How does the brain solve the (hierarchical) credit assignment problem efficiently? Cell-type-specific cortical feedback holds the key, closely approximating backprop. Paper: doi.org/10.64898/2026.06.16.…
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9/9 Note: This work brings together the best of the multiplexing ideas by @NeuroNaud @tyrell_turing w/ our interneurons-for-backprop work (Sacramento 2018) to achieve: scalable (multiplexing) and highly plausible (interneurons + many other features) deep cortical learning.
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