Both brains and RNNs can re-use components of computation across similar tasks or contexts. But what exactly are those “shared components”? How can they be used to solve several tasks? We address these questions in a new preprint with @avm.bsky.social! Link: www.biorxiv.org/content/10.6...

Lovely to see the full range of excellent commentaries on our BBS article with @matthiasmichel.bsky.social, together with our response, now out: Target article: www.cambridge.org/core/journal... Commentaries: www.cambridge.org/core/journal... Our response: www.cambridge.org/core/journal...

We are hosting a special issue on "Is There More to Consciousness Than Computation?" in @nconsc.bsky.social. Full information here: academic.oup.com/nc/pages/cal... With @krysdolega.bsky.social and Yair Pinto.

NEW PAPER. Why do larger networks train better? "Because they contain more candidate *sub*networks that can learn the task" → lottery tickets This popular explanation uses an appealing but misleading metaphor🧵 We propose an intuitive alternative grounded in theory: escape dimensions

1/n New preprint: What biology can, and cannot, tell us about conscious AI arxiv.org/abs/2606.02121 With @ulysse-klatzmann.bsky.social

🚨New dataset just dropped🚨 Introducing Places in the Wild: 67,000 RAW-format photographs (45 mpix) densely sampled from 810 places (260 basic-level categories). This is 11x the number of pixels in ImageNet! Preprint is here: arxiv.org/abs/2606.02481 1/

Looks like geons are alive and well: this paper was rated among the top 1.8% of all papers at CVPR: bardofcodes.github.io/superfit/

A short highlight of the beautiful recent work by @varunswadia.bsky.social, Doris Tsao et al., providing single-neuron evidence of the overlap between imagery and perception! www.nature.com/articles/s41...

Can neural representations from different people be translated into a shared space without paired data? In our preprint, "Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry", with Rishi Jha and @fuentemilla.bsky.social, we test this using fMRI 🧵👇

I'm proud to say we are releasing LAION-fMRI, a densely sampled 7T fMRI dataset of natural images, with very broad stimulus sampling for testing countless hypotheses and for deeply exploring brain representations. The dataset is now available at laion-fmri.hebartlab.com What does LAION-fMRI offer? 🧵

Finally, today is the day: Josefine Zerbe will present and release our new multi-echo 7T fMRI dataset LAION-fMRI during #VSS2026, with >30 fMRI session per subject and unprecedented stimulus diversity. Come to Talk Room 1 (Scene perception) today at 5:15. Details will follow in a separate thread!

Researching visual imagery? Consider submitting to this new cross-journal special issue on visual imagery at Nature Communications, Communications Psychology, and Scientific Reports! We welcome a broad range of topics and methods - more info below 👇 www.nature.com/collections/...

Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning process actually look like? New preprint! arxiv.org/abs/2605.14990

Excited about our new preprint: “The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream” arxiv.org/abs/2604.12825 Work with Sushrut Thorat, Anna Mitola, Paolo Papale, Peter König & Tim Kietzmann 🧵 thread below

We wrote a book! www.cambridge.org/core/books/s... Consciousness science is a fascinating but bewildering field: many competing theories, little consensus, and big open questions. If you are looking for an accessible guide through this complex landscape, this book is for you.

Yay 😃 well done @adriendoerig.bsky.social Aaron & Michael !

Most excellent conference. See you there?

🧠Introducing OmniMouse: One of the largest datasets in neuroscience along with a systematic study of scaling properties of brain models Co-led by 🤩 K. Willeke, @pollytur.bsky.social and @alexrgil14.bsky.social Models trained on up to 3M neurons, >150B tokens

CfA: Workshop “Philosophical Issues in Neural Computation” (@ruhr-uni-bochum.de) Jun 2–3 2026. Deadline: March 31. Further Speakers: @adriendoerig.bsky.social, @francesegan.bsky.social, @olivia.science, @gualtiero.bsky.social, @seelikat.bsky.social, Oron Shagrir.

Turns out that individual differences in accuracy, confidence, and RT among ANNs that only differ in their random initialization mimic the individual differences in humans. It may be time for NeuroAI to take individual differences even more seriously. Check out Herrick's thread 👇

NSD-synthetic, the out-of-distribution companion dataset of NSD consisting of 7T fMRI responses to 284 artificial images, is now published. #NeuroAI #CompNeuro #neuroscience #AI doi.org/10.1038/s414...

New paper out at PNAS: www.pnas.org/doi/10.1073/... Revisiting the high-dimensional geometry of population responses in the visual cortex with @jpillowtime.bsky.social. The review took forever because a reviewer was doubtful our new estimator can infer eigenvalues beyond the rank of the data! (1/6)

This paper had a pretty shocking headline result (40% of voxels!), so I dug into it, and I think it is wrong. Essentially: they compare two noisy measures and find that about 40% of voxels have different sign between the two. I think this is just noise!

Out today in Journal of Vision: Mapping the visual cortex with Zebra noise and wavelets By Sophie Skriabine and Max Shinn, with Samuel Picard and @kenneth-harris.bsky.social doi.org/10.1167/jov....

Published @cp-trendscognsci.bsky.social with @drewlinsley.bsky.social & @tonyfeng.bsky.social: As vision models scale to human/superhuman accuracy, they’re becoming worse models of primate vision—benchmark engineering isn’t neuroscience. @carneyinstitute.bsky.social @browncopsy.bsky.social

Legit super excited about this work coming out. My amazing doctoral student @ben.graphics has been working on an idea to use physically based differentiable rendering (PBDR) to probe visual understanding. Here, we generate physically-grounded metamers for vision models. 1/4 arxiv.org/abs/2512.12307

Happy to see this preprinted! We show temporally fine-grained auditory representations in EEG during silent reading. Could be a candidate neural substrate for the inner voice many people report having while reading! Congrats @jiaweili.bsky.social on all the hard work.

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...

New preprint w/ Malin Styrnal & @martinhebart.bsky.social 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/ps...

What does it mean to understand language? We argue that the brain’s core language system is limited, and that *deeply* understanding language requires EXPORTING info to other brain regions. w/ @neuranna.bsky.social @evfedorenko.bsky.social @nancykanwisher.bsky.social arxiv.org/abs/2511.19757 1/n🧵👇

Is the “standard workflow” holding back fMRI analysis? Mass-univariate analysis is still the bread-and-butter: intuitive, fast… and chronically overfitted. Add harsh multiple-comparison penalties, and we patch the workflow with statistical band-aids. No wonder the stringency debates never die.

Excited to share my first paper: Model–Behavior Alignment under Flexible Evaluation: When the Best-Fitting Model Isn’t the Right One (NeurIPS 2025). link below.

Very cool work, go check it out! Rich, brain-like scene representations built from acrive vision

This seems very cool, @singerjohannes.bsky.social decidedly did loads of cool stuff before I got lucky and he joined our group

Ask a brain anything! This is incredible work led by Victoria Bosch @initself.bsky.social at the Kietzmann Lab @timkietzmann.bsky.social, developing interactive and flexible brain decoding. I'm excited to see where this goes next.

New preprint! "Non-commitment in mental imagery is distinct from perceptual inattention, and supports hierarchical scene construction" (by Li, Hammond, & me) link: doi.org/10.31234/osf... -- the title's a bit of a mouthful, but the nice thing is that it's a pretty decent summary

Over the past year, my lab has been working on fleshing out theory + applications of the Platonic Representation Hypothesis. Today I want to share two new works on this topic: Eliciting higher alignment: arxiv.org/abs/2510.02425 Unpaired learning of unified reps: arxiv.org/abs/2510.08492 1/9

🧠 New preprint: we show that model-guided microstimulation can steer monkey visual behavior. Paper: arxiv.org/abs/2510.03684 🧵

Awesome work by @jorge-morales.bsky.social and team, using LLMs to suggest that propositional reasoning might be enough to solve classic imagery tasks! ✨

So excited to see this preprint released from the lab into the wild. Charlotte has developed a theory for how learning curriculum influences learning generalization. Our theory makes straightforward neural predictions that can be tested in future experiments. (1/4) 🧠🤖 🧠📈 #MLSky

I wanted to add some thoughts to this excellent blog post, not detailed, maybe wrong, maybe useful: 1. Unique variance is easy to interpret as a lower bound of what a variable explains (the upper bound being either what the variable explains alone or what the other variables cannot explain uniquely)

Variance partitioning is used to quantify the overlap of two models. Over the years, I have found that this can be a very confusing and misleading concept. So we finally we decided to write a short blog to explain why. @martinhebart.bsky.social @gallantlab.org diedrichsenlab.org/BrainDataSci...

🧠 New preprint: Why do deep neural networks predict brain responses so well? We find a striking dissociation: it’s not shared object recognition. Alignment is driven by sensitivity to texture-like local statistics. 📊 Study: n=57, 624k trials, 5 models doi.org/10.1101/2025...

What determines where specialisation for sensory information occurs in the cortex? We observe a spatial competition between primary sensory areas and the Default Mode Network, using our new Spatial Component Decomposition method. Work by @ulysse-klatzmann.bsky.social , with Bazin & Daniel Margulies

Preprint alert! 🚨 1/ How does deep sleep reshape our memories? Our new study shows that slow-wave sleep (SWS) reorganises episodic memory networks, shifting recall from the parietal cortex to the anterior temporal lobe (ATL). With Polina Perzich and @bstaresina.bsky.social . A thread below👇

A transformation from vision to imagery in the human brain. Intriguing new preprint by Roy & Naselaris et al for anyone interested in mental imagery! www.biorxiv.org/content/10.1...

🧠 “You never forget how to ride a bike”, but how is that possible? Our study proposes a bio-plausible meta-plasticity rule that shapes synapses over time, enabling selective recall based on context

New paper with @ManuKirberg 💭 Is “unconscious mental imagery” real? The evidence is weaker than it seems. We explain why—and how to move the debate forward. 🔗 www.sciencedirect.com/science/arti...

🔍 Large language models, similar to those behind ChatGPT, can predict how the human brain responds to visual stimuli New study by @adriendoerig.bsky.social @freieuniversitaet.bsky.social with colleagues from Osnabrück, Minnesota and @umontreal-en.bsky.social Read the whole story 👉 bit.ly/3JXlYmO

What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results. www.nature.com/articles/s42...