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? 🧵
1/X Our new method, the Inter-Animal Transform Class (IATC), is a principled way to compare neural network models to the brain. It's the first to ensure both accurate brain activity predictions and specific identification of neural mechanisms.
Preprint: arxiv.org/abs/2510.02523
Martin Hebart
6/ The shifts aren’t well-captured by the existing temporal models we tried. A target for future modeling work - directly fitting models on these data (which clearly have a lot of signal.
5/ The dynamics look like they might be driven by a local recurrent circuit. We report evidence of such a circuit and show that its emergence correlates with the shifts.
2/ Representations in each area are better predicted by later DNN layers over time, unifying dynamics via a single complexity-increasing motif.
3/ We document an area-wide phenomenon, not specific to subpopulations of neurons. Most individual electrodes are predicted by deeper DNN layers over the course of the response.
Yeah. I see a lot of "if chatGPT coded all of it, how do you know it's right?" - but for me the more important thing is "if chatGPT coded all of it, how did you learn anything???"
Excited to share that our paper is now out in Neuron @cp-neuron.bsky.social (dlvr.it/TM9zJ8).
Our perception isn't a perfect mirror of the world. It's often biased by our expectations and beliefs. How do these biases unfold over time, and what shapes their trajectory? A summary thread. (1/13)
Josh Wilson
4/ The dynamics correspond to increased functionality for decoding hard images. Images that are harder for a model to classify are decoded later from the neural population, as representations become more complex.
1/ New preprint with @dyamins.bsky.social + team! Ventral visual representations within areas evolve over the course of the response along the same hierarchical complexity axis that distinguishes the visual areas, potentially driven by local recurrence.
Josh Wilson
Josh Wilson
Josh Wilson
Josh Wilson
People exhibit biases when perceiving features of the world, shaped by both external
stimuli and prior decisions. By tracking behavioral, neural, and mechanistic markers
of stimulus- and decision-rela...
A hierarchical computational motif unifies neural dynamics across the ventral visual stream https://www.biorxiv.org/content/10.64898/2026.05.18.726101v1
