NEAT (Neuro-AI-Talks) is fully booked! We are happy for the great interest and are looking forward to welcoming you all to Osnabrück in September!

Packed room for the second talk of the MIND (machine intelligence, neuroscience discovery) lecture series in Osnabrück. Excited to hear from @dirkwulff.bsky.social !

Ever wondered why we fixate some locations longer than others? We initially thought that it had to do with recognition complexity, and were quite wrong. In Nature Neuroscience we present an alternative take: the need to memorise governs fixation durations.

Now out in Nature Neuroscience: "Fixation duration on natural scenes is explained by memory encoding not processing demand". www.nature.com/articles/s41... Our eyes don't linger because recognition is hard; they linger to remember. Let me take you on a quick tour. 🧵

Our NeuroAI study made it onto the cover of Nature Machine Intelligence ❤️. In it, we demonstrate that a developmentally-inspired visual diet can drastically improve the robustness of ANN-based vision systems. www.nature.com/articles/s42... open access, open code, open weights, open science.

This paper changed my mind on how visual processing works in the brain. We knew it was dynamic given all the recurrent connections. But I thought it would converge to a fixpoint during a fixation. Not so, it seems ... The dynamic captures different aspects of the stimulus at different times. Nice.

Looking for robust AI vision? Then this NeuroAI paper may be for you 👇 Open access, open code, open weights.

A huge effort and a new take on what constitutes the "feedforward pass" across the visual system. Make sure to check out our new preprint:

A huge effort and a new take on what constitutes the "feedforward pass" across the visual system. Make sure to check out our new preprint:

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.

Highly recommended: I really enjoyed NEAT, every time I attended. Great opportunity to catch up on #NeuroAI, especially for those who can't make it to CCN!

Happy to announce the 3rd iteration of NEAT (Neuro-AI-Talks), which will take place in Osnabrück September 14th-15th 2026. NEAT is a (deliberately small scale) NeuroAI workshop that brings together researchers from neuroscience and AI. www.kietzmannlab.org/neat2026/ More information below 👇

📢 PhD position in the NeuroAI of Language Why can LLMs predict brain activity so well? We're hiring a PhD student to find out -- AI interpretability meets neuroimaging Deadline March 20 Please RT 🙏 👇 mpi.nl/career-education/vacancies/vacancy/fully-funded-4-year-phd-position-neuroai-language

📢 PhD position in Developmental Language Modelling (PLZ RT) What can human language acquisition teach us about training language models? Join us as a PhD! mpi.nl/career-education/vacancies/vacancy/fully-funded-4-year-phd-position-developmental-language @carorowland.bsky.social @mpi-nl.bsky.social

What a milestone 🤯. The institute of cognitive science in Osnabrück, the first of its kind in Germany, is now home to more than 1,000 cognitive science students across our programs. Wild.

What happens if you hook up an energy-efficiency optimising RNN on active vision input? It learns predictive remapping and path integration into allocentric scene coordinates. Now out in patterns: www.cell.com/patterns/ful...

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.

Our work reveals a sharp trade-off between predictive accuracy and model identifiability. Flexible mappings maximize predictivity, but blur the distinction between competing computational hypotheses.

🚨 Out in Patterns! We asked ourselves, if complex neural dynamics like predictive remapping and allocentric coding can emerge from simple physical principles, in this case Energy Efficiency. Turns out they can! More information in the 🧵 below. I am super excited to see this one out in the wild.

We went back to the drawing board to think about what information is available to the visual system upon which it could build scene representations. The outcome: a self-supervised training objective based on active vision that beats the SOTA on NSD representational alignment. 👇

We managed to integrate brain scans into LLMs for interactive brain reading and more.. check out Vicky's post below. Super excited about this one!

We managed to integrate brain scans into LLMs for interactive brain reading and more.. check out Vicky's post below. Super excited about this one!

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...

Hi, we will have three NeuroAI postdoc openings (3 years each, fully funded) to work with Sebastian Musslick (@musslick.bsky.social), Pascal Nieters and myself on task-switching, replay, and visual information routing. Reach out if you are interested in any of the above, I'll be at CCN next week!

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

#AI "Ultimately, this is a step forward in understanding how the human brain understands meaning from the visual world." #LLMs @mila-quebec.bsky.social @adriendoerig.bsky.social @timkietzmann.bsky.social @natmachintell.nature.com nouvelles.umontreal.ca/en/article/2...

A long time coming, now out in @natmachintell.nature.com: Visual representations in the human brain are aligned with large language models. Check it out (and come chat with us about it at CCN).

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

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

Nice paper by @zejinlu.bsky.social the group of @timkietzmann.bsky.social appearing in Nat Human Behav www.nature.com/articles/s41... showing the properties of a CNN for which you release the weight sharing constraint.. #neuroAI

Introducing All-TNNs: Topographic deep neural networks that exhibit ventral-stream-like feature tuning and a better match to human behaviour than the gold standard. Now out in Nature Human Behaviour. 👇

Introducing All-TNNs: Topographic deep neural networks that exhibit ventral-stream-like feature tuning and a better match to human behaviour than the gold standard. Now out in Nature Human Behaviour. 👇

Can seemingly complex multi-area computations in the brain emerge from the need for energy efficient computation? In our new preprint on predictive remapping in active vision, we report on such a case. Let us take you for a spin. 1/6 www.biorxiv.org/content/10.1...

Can seemingly complex multi-area computations in the brain emerge from the need for energy efficient computation? In our new preprint on predictive remapping in active vision, we report on such a case. Let us take you for a spin. 1/6 www.biorxiv.org/content/10.1...

I'd put these on the NeuroAI vision board: @tyrellturing.bsky.social's Deep learning framework www.nature.com/articles/s41... @tonyzador.bsky.social's Next-gen AI through neuroAI www.nature.com/articles/s41... @adriendoerig.bsky.social's Neuroconnectionist framework www.nature.com/articles/s41...

🚨 New preprint alert! Our latest study, led by @DrewLinsley, examines how deep neural networks (DNNs) optimized for image categorization align with primate vision, using neural and behavioral benchmarks.

Check out our new paper at #ICLR2025, where we show that multi-task neural decoding is both possible and beneficial. As well, the latents of a model trained only on neural activity capture information about brain regions and cell-types. Step-by-step, we're gonna scale up folks! 🧠📈 🧪 #NeuroAI

#CCN2025 abstract acceptances were sent out this morning. I'll post a summary of each of our projects closer to the conference. Looking forward to seeing you all in Amsterdam!

Neat idea and great insight into the inner workings of MLLMs. MLLMs are good at high level vision, but fail at low- and mid-level visual tasks.

Top-down feedback is ubiquitous in the brain and computationally distinct, but rarely modeled in deep neural networks. What happens when a DNN has biologically-inspired top-down feedback? 🧠📈 Our new paper explores this: elifesciences.org/reviewed-pre...

Three year postdoctoral position available in my lab @psychologyuea.bsky.social to work on neuroimaging and deep learning studies of multimodal material perception! with @timkietzmann.bsky.social @stephanierossit.bsky.social - funded by @leverhulme.ac.uk pls repost #compneurosky #neuroskyence

Love the buzzing of the lab before the CCN deadline. Such great focus, and literally just such a great feeling to see what awesome projects they are all working on.

Thanks for sharing! But note that there have been many cases showing this previously. For example we found very clear evidence that training to predict LLM embeddings is better than a variety of other objectives arxiv.org/abs/2209.11737.

Interesting fact I just unearthed about reference letters. Doesn't surprise me.

Glad you got something out of the course, was great fun teaching it,... and thanks for the meme 😎

Hello! I'm looking to hire a post-doc, to start this Summer or Fall. It'd be great if you could share this widely with people you think might be interested. More details on the position & how to apply: bit.ly/cocodev_post... Official posting here: academicpositions.harvard.edu/postings/14723

This year we ran another meme contest as part of my ML4CCN lecture series. Student submissions were fantastic. Guess the paper...

Ouch. Computer vision is nowhere near where we'd want it to be.

Exciting opportunity to work with Fraser (Stephanie and me) on material perception from vision and touch. Please apply and/or spread the word.✌️

My 15' talk "the modern EEG regression toolkit: rERPs, TRFs, mixed models, and more" is now on youtube youtu.be/vnlbVDarRUc?... thanks @cuttingeeg.bsky.social for an awesome #EEG conference!