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Mick Bonner

@mickbonner.bsky.social
344 followers 143 following 42 posts

Assistant Professor of Cognitive Science at Johns Hopkins. My lab studies human vision using cognitive neuroscience and machine learning. bonnerlab.org

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Reposted by Mick Bonner
Martin Hebart @martinhebart.bsky.social · 25/09/2026
What is the nature of universal representations in AI models, and what determines whether they emerge? Our paper accepted at #neurips2026 led by @florianmahner.bsky.social & @rothj.bsky.social addressed these questions comparing 162 vision models, with intriguing results. arxiv.org/abs/2605.13675 🧵
arxiv.org
Characterizing Universal Object Representations Across Vision Models
Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. However, what remains unknown is which visual prop...
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re-vision-init.bsky.social @re-vision-init.bsky.social · 01/08/2026
(1/6) Do our visual neuroscience findings actually replicate? And do they generalize beyond the datasets they were found in? We've lauched re:vision, a community-driven initiative to answer these questions, and we are looking for scientistis to participate. re-vision-initiative.org
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Mick Bonner @mickbonner.bsky.social · 22/07/2026
Super excited about this!
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Martin Hebart @martinhebart.bsky.social · 18/05/2026
Today at #VSS2026 we are introducing re:vision, a community driven replication/generalization initiative based on our newly released LAION-fMRI dataset. Come to the satellite at 2pm to learn more about it & why you may want to participate! The room Blue Heron is in the upstairs region of the lobby.
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Martin Hebart @martinhebart.bsky.social · 17/05/2026
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? 🧵
laion-fmri.hebartlab.com
LAION-fMRI - a 7T fMRI dataset of human vision
LAION-fMRI (LfMRI / LAION MRI dataset): 5 subjects, 25,052 launch-release natural images, 165 acquired 7T fMRI sessions with single-trial GLMsingle betas, retinotopy, localizers, and diffusion.
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jane-yang.bsky.social @jane-yang.bsky.social · 16/05/2026
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
arxiv.org
Characterizing the visual representation of objects from the child's view
Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning process look like? We analyzed first-person videos ...
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Jorge Morales @jorgemorales.me · 14/05/2026
Vision Science's Christmas is almost here! Our lab will present work —including collaborations with the one and only @meganakpeters.bsky.social — on mental imagery & vividness, LLMs, advanced decoding of reality monitoring signals in fMRI, pupillometry of fake light & visual discomfort! #vss2026
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Chaz Firestone @chazfirestone.bsky.social · 15/05/2026
It's #VSS2026! SO excited about the projects we've brought with us this year, and of course #phiVis to close things out. Come say hi, and don't forget to grab your lab 'merch' 👀 perception.jhu.edu/vss/
A lab 'ad' laying out VSS 2026 presentations from the Perception & Mind Lab. More information can be found at https://perception.jhu.edu/vss/
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Leyla Isik @lisik.bsky.social · 14/05/2026
Heading to #vss2026? Check out the presentations from our lab @vssmtg.bsky.social
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Mick Bonner @mickbonner.bsky.social · 16/05/2026
Excited for the lab's presentations at #VSS2026!
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Martin Hebart @martinhebart.bsky.social · 16/05/2026
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!
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Zejin Lu @zejinlu.bsky.social · 11/05/2026
Now out in Nature Machine Intelligence @NatMachIntell “Adopting a human developmental visual diet yields robust and shape-based AI vision”: doi.org/10.1038/s422.... A wonderful case where brain inspiration improved AI. With @martisamuser.bsky.social, Radek Cichy and @timkietzmann.bsky.social .
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Iris Groen @irisgroen.bsky.social · 12/05/2026
EXCITED to travel to #VSS2026 to contribute 2 talks and 2 posters from our lab! Me and @niklasmuller.bsky.social will talk scene encoding models on Fri & Sat, @sargechris.bsky.social and @annewzonneveld.bsky.social will discuss their cool work on video models on Tue (schedule👇for exact times).
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Chris Baker @cibaker.bsky.social · 24/03/2026
The Laboratory of Brain and Cognition at NIH is hosting a two-day symposium on 'Foundations and Frontiers in Cognitive Neuroscience' in honor of Dr. Alex Martin, to be held at NIH (with online videocast) on April 7th-8th, 2026. Register to attend online or in-person at: bit.ly/4bYlbxw
Picture of Alex Martin, National Institute of Mental Health
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Kamila Maria Jozwik @kamilajozwik.bsky.social · 23/03/2026
postdoc and PhD positions in visual cognitive computational neuroscience in my lab at the University of Cambridge kamilajozwik.com/join_lab.html
kamilajozwik.com
Kamila Jozwik - Join lab
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Andrew Lampinen @lampinen.bsky.social · 17/03/2026
Pleased to share that our paper "Representation Biases: Variance is Not Always a Good Proxy for Importance" is now out as Theory/New Concepts paper in eNeuro! www.eneuro.org/content/13/3... 1/
eneuro.org
Representation Biases: Variance Is Not Always a Good Proxy for Importance
A central approach in neuroscience is to analyze neural representations as a means to understand a system's function, through the use of methods like principal component analysis, regression, and repr...
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Manasi Malik @manasimalik.bsky.social · 26/02/2026
Excited to share new work on how the brain makes social inferences from visual input! 🧠👯‍♂️ (With @lisik.bsky.social , @shariliu.bsky.social, @tianminshu.bsky.social , and Minjae Kim!) www.biorxiv.org/content/10.6...
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tyler bonnen @tylerbonnen.bsky.social · 26/02/2026
excited to share some recent work! neural networks trained on multi-view sensory data are the first to match human-level 3D shape perception we predict human accuracy, error patterns, and reaction time—all zero-shot, no training on experimental data arxiv.org/abs/2602.17650 1/🧠
arxiv.org
Human-level 3D shape perception emerges from multi-view learning
Humans can infer the three-dimensional structure of objects from two-dimensional visual inputs. Modeling this ability has been a longstanding goal for the science and engineering of visual intelligenc...
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
I don't disagree with that point, but at the same time, you can think of this from another perspective: Isn't it crazy that despite the many complex nonlinear transformations implemented by seemingly different models, they nonetheless arrive at something that is similar up to a linear transform?
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
More to come. We are working on a paper now that characterizes these issues in more depth.
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
And Fig. 7 in this paper. journals.plos.org/ploscompbiol...
journals.plos.org
Universal scale-free representations in human visual cortex
Author summary The human cerebral cortex is thought to encode sensory information in population activity patterns, but the statistical structure of these population codes has yet to be characterized. ...
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
This number is based on what we have seen in analyses in my lab. Some examples are Fig. 5 of in this paper... www.science.org/doi/10.1126/...
science.org
Universal dimensions of visual representation
Probing neural representations reveals universal aspects of vision in artificial and biological networks.
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
Second, if the only thing that differentiates two alternative models is a simple linear reweighing, it raises a question of how important their differences really are. It may be more informative in the end to focus on understanding what the models have in common than how they differ.
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
3. We have been thinking about this. The answer is not straightforward. First, RSA is effectively insensitive to anything beyond the first 5-10 PCs in brain and network representations, and I happen to think there is much more to the story than just a handful of dimensions. bsky.app/profile/mick...
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
1. Yes, trained networks are much better when using RSA. We show this in a supplementary analysis. 2. We have never computed this exact quantify. But we did show that if you do PCA on wide untrained networks, you can drastically reduce their dimensionality while still retaining their performance.
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
Although pre-trained networks can be super useful for comp neuro, the surprising success of untrained networks suggests that there may be still be much to learn by focusing on simpler approaches. We shouldn't be focusing all our attention on the latest DNN models coming out of the ML world.
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
These architectural manipulations were things that you wouldn’t typically think to try if your primary focus was on trained networks. We wrote about this in our discussion.
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
Importantly, one of the things we learned in that work was that the field hasn’t been giving untrained networks the best chance possible. We found that fairly simple architectural manipulations could dramatically improve their performance.
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Mick Bonner @mickbonner.bsky.social · 10/02/2026
That's true. But untrained networks can do surprisingly well. In a recent paper, we found that untrained networks can rival trained networks in a key monkey dataset. In the human data we examined, there was still a gap relative to pre-trained models, as you point out. www.nature.com/articles/s42...
nature.com
Convolutional architectures are cortex-aligned de novo - Nature Machine Intelligence
Kazemian et al. report that untrained convolutional networks with wide layers predict primate visual cortex responses nearly as well as task-optimized networks, revealing how architectural constraints...
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Vlad Ayzenberg @vayzenb.bsky.social · 28/01/2026
This paper was an awesome collaborative effort of a @fitngin.bsky.social working group. It provides a detailed review of how DNNs can be used to support dev neuro research @lauriebayet.bsky.social and I wrote the network modeling section about how DNNs can be used to test developmental theories 🧵
sciencedirect.com
Deep learning in fetal, infant, and toddler neuroimaging research
Artificial intelligence (AI) is increasingly being integrated into everyday tasks and work environments. However, its adoption in medical image analys…
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Rhodri Cusack @rhodricusack.bsky.social · 02/02/2026
Infants organise their visual world into categories at two-months-old! So happy to see these results published - congratulations Cliona and the rest of the FOUNDCOG team.
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Mick Bonner @mickbonner.bsky.social · 30/01/2026
New paper from our lab on the behavioral significance of high-dimensional neural representations!
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Iris Groen @irisgroen.bsky.social · 16/01/2026
I have a PhD opening for my #VIDI BrainShorts project 📽️🧠🤖! Are you or do you know an ambitious, recent (or almost) MSc graduate with a background in NeuroAI and interest in large-scale data collection and video perception? Check out our vacancy! (deadline Feb 15). werkenbij.uva.nl/en/vacancies...
werkenbij.uva.nl
Vacancy — PhD Position in NeuroAI for Video Perception in the Human Brain
<p><span>Are you interested in using AI to unravel the mysteries of the brain? Do you want to perform cutting-edge NeuroAI research and leverage deep learning to understand human vision? Then check out the vacancy below and apply for a PhD position in this exciting research direction.</span></p>
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Dirk Bernhardt-Walther @dirkbwalther.bsky.social · 09/01/2026
Wonderful article about our recent paper in @pnasnexus.org! Thanks, @sachapfeiffer.bsky.social and @mickbonner.bsky.social! @yikai-tang.bsky.social @uoftpsychology.bsky.social @artsci.utoronto.ca @utoronto.ca
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Russell Epstein @russellepstein.bsky.social · 07/01/2026
Our new paper in @sfnjournals.bsky.social shows different neural systems for integrating views into places--PPA integrates views *of* a location (e.g., views of a landmark), while RSC integrates views *from* a location (e.g., views of a panorama). Work by the bluesky-less Linfeng Tony Han.
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Andrew Lampinen @lampinen.bsky.social · 16/12/2025
Why isn’t modern AI built around principles from cognitive science or neuroscience? Starting a substack (infinitefaculty.substack.com/p/why-isnt-m...) by writing down my thoughts on that question: as part of a first series of posts giving my current thoughts on the relation between these fields. 1/3
infinitefaculty.substack.com
Why isn’t modern AI built around principles from cognitive science?
First post in a series on cognitive science and AI
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Grace Lindsay @neurograce.bsky.social · 08/12/2025
Spread the word: I'm looking to hire a postdoc to explore the concept of attention (as studied in psych/neuro, not the transformer mechanism) in large Vision-Language Models. More details here: lindsay-lab.github.io/2025/12/08/p... #MLSky #neurojobs #compneuro
lindsay-lab.github.io
Lindsay Lab - Postdoc Position
Artificial neural networks applied to psychology, neuroscience, and climate change
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
As for what other inductive biases will prove to be important, this is still TBD. I think that wiring costs (e.g., topography) may be one.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
But neuroscientists and AI engineers have different goals! Neuroscientists should be seeking parsimonious theories, not high-performing models.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
Importantly, to get this to work, NeuroAI researchers have to back to the drawing board and search for simpler approaches. I think that currently, we are relying too much on the tools and models coming out of AI. It makes it seem like the only feasible approach is whatever currently works in AI.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
The simple-local-learning goal is certainly non-trivial! But recent findings (especially universality of network representations) suggest that it has potential.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
What might such a theory look like? My bet is that it will be one that combines strong architectural inductive biases with fully unsupervised learning algorithms that operate without the need for backpropagation. This is a very different direction than where AI and NeuroAI are currently headed.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
Although the deep learning revolution in vision science started with task-based optimization, there are intriguing signs that a far more parsimonious computational theory of the visual hierarchy is attainable.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
These universal representations are not restricted to early network layers. We see them across the full depth of the networks that we examined. Their strong universality and independence of task demands calls out for a parsimonious explanation that has yet to discovered.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
A second paper from my lab adds another element to this story: after training, many diverse DNNs converge to universal features that are independent of the tasks they were trained on. It is these universal features that are most strongly shared with visual cortex. www.science.org/doi/10.1126/...
science.org
Universal dimensions of visual representation
Probing neural representations reveals universal aspects of vision in artificial and biological networks.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
What does this mean? It suggests that architectural inductive biases alone can get us surprisingly far in explaining the image representations of the ventral stream. See a great commentary by @binxuwang.bsky.social wang.bsky.social and Carlos Ponce. www.nature.com/articles/s42...
nature.com
Structure as an inductive bias for brain–model alignment - Nature Machine Intelligence
Even before training, convolutional neural networks may reflect the brain’s visual processing principles. A study now shows how structure alone can help to explain the alignment between brains and mod...
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
Second, similar manipulations in other architectures were relatively ineffective—the effects were specific to convolutional architectures and relied critically on the use of spatially local filters.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
These results could not simply be explained by high-dimensional regression. First, we could drastically reduce the dimensionality of wide layers through PCA while still retaining strong performance.
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
We found that architectural manipulations alone (most importantly, making deeper layers wider) yielded large performance gains in untrained convolutional models of the ventral stream. In fact, these untrained networks even rivaled ImageNet-trained AlexNet in predicting monkey IT representations!
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Mick Bonner @mickbonner.bsky.social · 15/12/2025
Our recent paper adds to this story by showing the remarkable effectiveness of untrained convolutional networks in predicting ventral stream representations. www.nature.com/articles/s42...
nature.com
Convolutional architectures are cortex-aligned de novo - Nature Machine Intelligence
Kazemian et al. report that untrained convolutional networks with wide layers predict primate visual cortex responses nearly as well as task-optimized networks, revealing how architectural constraints...
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