Introducing CorText: a framework that fuses brain data directly into a large language model, allowing for interactive neural readout using natural language. tl;dr: you can now chat with a brain scan 🧠💬 1/n

Warren McCulloch

Figuring out how the brain uses information from visual neurons may require new tools, writes @neurograce.bsky.social. Hear from 10 experts in the field. #neuroskyence www.thetransmitter.org/the-big-pict...

Wow, peak library experience at Princeton! Looking forward to a week of the “Automated Scientific Discovery of Mind and Brain” workshop - where I will also present my work on CorText and brain-language fusion 🧠

As it's hiring season again I'm resharing the NeuroJobs feed. Add #NeuroJobs to your post if you're recruiting or looking for an RA, PhD, Postdoc, or faculty position in Neuro or an adjacent field. bsky.app/profile/did:...

🔍 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

Our target discussion article out in Cognitive Neuroscience! It will be followed by peer commentary and our responses. If you would like to write a commentary, please reach out to the journal! 1/18 www.tandfonline.com/doi/full/10.... @cibaker.bsky.social @susanwardle.bsky.social

incredible Ada Lovelace quote highlighted in a talk by Steve Furber. She spells out the dream of computational neuroscience, 2 centuries ago. The sheer ambition 🤩

In other news, I am now a ✨ COXI ✨ (cognitive scientist in Osnabrück, Germany) www.uni-osnabrueck.de/en/campus-li... I'll be looking for PhD students & postdocs later this year, so watch this space if combining cognitive modelling, EEG & tFUS to study flexible cognition sounds exciting to you 🚀

Datasets like NSD & THINGS offer rich stimuli but often test a single task. After great conversations at #CCN2025 on multi-task studies & generalization in brains & models, I thought I would repost our perspective for those interested in this topic. We need multiple tasks!👉 doi.org/10.1038/s415...

That's a wrap for CCN2025 -- and so planning for CCN2026 in New York is starting today! Save travels to all participants and remember to fill out the feedback survey sent via email!

Looking for principles of neural organisation: should we ask what for or how come? Great kickoff discussions at the “Modeling the Physical Brain” CCN satellite event! #CCN2025 #topography

OK, time for a CCN runup thread. Let me tell you about all the lab’s projects present at CCN this year. #CCN2025

🚨 Finally out in Nature Machine Intelligence!! "Visual representations in the human brain are aligned with large language models" 🔗 www.nature.com/articles/s42...

🚨New paper🚨 Neural manifolds went from a niche-y word to an ubiquitous term in systems neuro thanks to many interesting findings across fields. But like with any emerging term, people use it very differently. Here, we clarify our take on the term, and review key findings & challenges rdcu.be/ex8hW

The final destination is an ontology not of a singular Universe, but of a pluriverse of viable, interacting maps. Reality, as we can know it, is not the silent, underlying territory. It is the noisy, dynamic, and ever-changing ecosystem composed of all these living, competing, and co-evolving maps.

The perspective that representational Universality and the ‘platonic representation hypothesis’ etc. are not addressing - multimodal / ANN-human alignment happens due to overlapping “Umwelts”, not due to all systems ultimately representing Reality™ (hot take?)

What makes visual processing in the brain so powerful and flexible? Very excited to share our new work where we started from SOTA models that accurately predict dynamic brain activity during hours of video watching, and investigated core computations underlying visual perception

Thank you everyone for your suggestions! This turned out to be a very useful resource. For those interested, you can see a compiled version of the suggestions from this thread here: tinyurl.com/kaz445jh (The wiki page will be updated as we find more resources.) 🧠🤖 , 🧠📈

This summer my lab's journal club somewhat unintentionally ended up reading papers on a theme of "more naturalistic computational neuroscience". I figured I'd share the list of papers here 🧵:

My paper with @stellalourenco.bsky.social ‬is now out in Science Advances! We found that children have robust object recognition abilities that surpass many ANNs. Models only outperformed kids when their training far exceeded what a child could experience in their lifetime doi.org/10.1126/scia...

Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions. Work with @zejinlu.bsky.social @sushrutthorat.bsky.social and Radek Cichy arxiv.org/abs/2507.03168

How is high-level visual cortex organized? In a new preprint with @martinhebart.bsky.social & @kathadobs.bsky.social, we show that category-selective areas encode a rich, multidimensional feature space 🌈 www.biorxiv.org/content/10.1... #neuroskyence 🧵 1/n

What shapes the topography of high-level visual cortex? Excited to share a new pre-print addressing this question with connectivity-constrained interactive topographic networks, titled "Retinotopic scaffolding of high-level vision", w/ Marlene Behrmann & David Plaut. 🧵 ↓ 1/n

In this study, Lu et al. introduce All-Topographic Neural Networks (All-TNN) as a parsimonious model of the human visual cortex.

How can we model the emergence of topographical organisation in the brain and its impact on visual behaviour, using image-computable ‘behaving’ neural networks? 🧠We move beyond convolutions with All-TNNs…Now out in Nature Human Behaviour! #neuroai #neuroskyence

Energy efficiency is all you need.

A little late on the 🦋🚂, but happy to share a new(ish) preprint - the culmination of three years of postdoc with @tyrellturing.bsky.social and @apeyrache.bsky.social, and my first real foray into #NeuroAI as a tool to study the sleeping brain 🧠🟦: biorxiv.org/content/10.1... (1/🧵) c’est parti!

Happy to announce that the Analytical Connectionism summer school is back! This year we'll dive into "bias" across ML, Neuroscience, and Psychology. 📆 Aug 25-Sep 5 🗺️ London, UK 🌐 www.analytical-connectionism.net/school/2025/ Applications are open!

🚨Call for Papers🚨 The Re-Align Workshop is coming back to #ICLR2025 Our CfP is up! Come share your representational alignment work at our interdisciplinary workshop at @iclr-conf.bsky.social Deadline is 11:59 pm AOE on Feb 3rd representational-alignment.github.io

New year, new preprint!💫 I'm excited to share my first(!) postdoc paper working with Jim DiCarlo & ‪@kohitij.bsky.social‬ #NeuroAI #CompNeuro We revisited a long-standing question: How does category feedback (a.k.a. training) reconfigure IT responses? 🧠 Our long story short [1/7]:

1/ Okay, one thing that has been revealed to me from the replies to this is that many people don't know (or refuse to recognize) the following fact: The unts in ANN are actually not a terrible approximation of how real neurons work! A tiny 🧵. 🧠📈 #NeuroAI #MLSky

OK If we are moving to Bluesky I am rescuing my favourite ever twitter thread (Jan 2019). The renamed: Bluesky-sized history of neuroscience (biased by my interests)

My lab has been working on comparing neural representations for the past few years - methods like RSA, CKA, CCA, Procrustes distance We are often asked: What do these things tell us about the system's function? How do they relate to decoding? Our new paper has some answers arxiv.org/abs/2411.08197

Just to offer a different PoV: this would benefit progress in the same direction, but IMO we need new math for life/natural intelligence to get us out of copying ML into neuroAI, e.g. claiming an engineering tool as THE way the brain works etc. Breakthrough Theoretical advances need broader search.

My favorite description of a large language model was accidentally written by Ray Bradbury in 1969, more than half a century ago, and it's eerie how fitting its rendition of an emergent language mind is: vvvvvvv The poem follows in the replies vvvvvv

1/13 Heavily updated preprint! arxiv.org/abs/2209.11737 We show that the contextual information encoded in Large Language Models (LLMs) is beneficial for modelling the complex visual information extracted by the brain from natural scenes. 🧵

The P100 is dead, long live the P100. Early responses do not generalise from static viewing to active vision.