Mick Bonner

Do neuroscientists need deep learning to explain brain representations? Our new paper shows that architectural manipulations alone—without pretraining—go surprisingly far in explaining visual cortex representations. Led by a fantastic former master’s student @AtlasKazemian

We are excited to open the re:vision initiative, a community-driven initiative for replicating and generalizing findings in visual neuroscience, based on the LAION-fMRI dataset. re-vision-initiative.org Why this initiative and why would you want to participate? 🧵

Are you attending #VSS2026? Come check out my talk on cross-species alignment for finding shared and distinct representational geometries in primate IT. Saturday, May 16, 2026, 3.30pm, Talk Room 1

Very excited about #VSS2026 starting today! We hope you can make it to our lab’s presentations! Come and visit us at the talks & posters, and make sure you come to our workshop about the community replication initiative re:vision. Low bar for entry! Details will follow!

Excited for this! I’ll be there, and will be giving a talk.

Congratulations Cliona, and the rest of the FOUNDCOG team, I am so excited to see these results published!

1/7 Does the infant brain have representational structure? 👶🧠In the FOUNDCOG project, we scanned 134 awake infants using fMRI. Published in Nature Neuroscience, our research reveals 2-month-old infants already possess complex visual representations in VVC that align with DNNs.

New paper from our lab on the behavioral significance of high-dimensional neural representations!

Great profile on @minyoung_huh, @thisismyhat, @ssnl_tz & @phillip_isola. Pairs well our work with @ev_fedorenko on Brain/ANN convergence in language & vision (tinyurl.com/4d3p53hb) & @ziruichen44 & @michaelfbonner’s work on visual reps (tinyurl.com/4d3p53hb)

@UriCohen42 It is an open question whether there is a way of characterizing the data with some lower-dimensional nonlinear manifold. My hunch is that the there is no such simple manifold for visual cortex.

@michaelfbonner Hey Mick, interesting! It means that there is no structure in the resulting representations. Perhaps you used very large variance in the images? This surely does not reproduce with samples from low variance manifolds.

Dimensionality reduction may be the wrong approach to understanding neural representations. Our new paper shows that across human visual cortex, dimensionality is unbounded and scales with dataset size—we show this across nearly four orders of magnitude. journals.plos.org/ploscompbiol/a…

Our work demonstrates that fully understanding human brain representations requires a high-dimensional statistical approach—otherwise, we're just seeing the tip of the iceberg!

Why did so many previous studies report low dimensionality? 1. High-quality neural datasets are finally large enough to probe representations beyond just tens of dimensions! 2. Standard methods in cognitive neuroscience are insensitive to low-variance—but meaningful—dimensions.

New preprint w/ Malin Styrnal & @martin_hebart Have you ever computed noise ceilings to understand how well a model performs? We wrote a clarifying note on a subtle and common misapplication that can make models appear quite a lot better than they are. osf.io/preprints/psya…

Ever wished you had a bigger fMRI dataset beyond the (amazing) NSD? Introducing MOSAIC! Incredible effort led expertly by @Blahner with help from @mayukh091 in collaboration with the amazing @AudeOliva.

What if you could predict brain function from structure in normally developing individuals? In her new work, Fernanda Ribeiro (not on X) provides a toolbox that accurately predicts retinotopy from T1 scans alone! tinyurl.com/deepretinotopy This is exciting for several reasons:

🤖🧠I'll be considering applications for PhD students & postdocs to start at Yale in Fall 2026! If you are interested in the intersection of linguistics, cognitive science, & AI, I encourage you to apply! PhD link: rtmccoy.com/prospective_st… Postdoc link: rtmccoy.com/prospective_po…