Our new paper in @natureportfolio.nature.com. Psychedelics don’t simply disorder the brain. We found hidden, context-sensitive organisation beneath the apparent chaos -- strongest in people reporting more positive, profound experiences and a reduced sense of separation from the world. 🧵👇
@arthurpr4t.bsky.social has a new preprint with important results on a famous psychophysical law (Weber's law). It isn't, in fact, a law, because it can be broken. A more fundamental principle (efficient coding) shows when and why Weber's law holds true. www.biorxiv.org/content/10.6...
Efficient coding makes and breaks Weber's law
Weber's law is a rare quantitative regularity in psychology, yet its origins remain debated. Here we provide causal evidence that it arises from the more fundamental principle of efficient coding. Thi...
biorxiv.org
📢 Calling all senior neuro postdocs: SYNAPSES brings postdocs from around the world to Yale to share their work during an in-person symposium on October 22, 2026. Apply today! Deadline Sept 3. medicine.yale.edu/neuroscience...
I think AI has tremendous potential to help us *help each other* This year I built an AI copilot to help support and scale mentorship. I plan to write a couple of blog posts on what I learned from this. Here's a link to the first one! infinitecare.substack.com/p/what-i-lea...
Do RL agents really need to replan from scratch for every new goal? Why do rodents "preplay" goals they haven't pursued? We think these questions share a candidate answer.
New preprint: "Linear equivalence of nonlinear recurrent neural networks." For large nonlinear (potentially chaotic) RNNs with random connectivity, the full N×N covariance matrix takes the same form as that of a ~linear~ network with the same couplings, driven by independent noise.
Linear equivalence of nonlinear recurrent neural networks
Large nonlinear recurrent neural networks with random couplings generate high-dimensional, potentially chaotic activity whose structure is of interest in neuroscience, machine learning, ecology, and o...
arxiv.org
I am totally pumped about this new work . "Task-trained RNNs" are a powerful and influential framework in neuroscience, but have lacked a firm theoretical footing. This work provides one, and makes direct contact with the classical theory of random RNNs: www.biorxiv.org/content/10.6...
With some trepidation, I'm putting this out into the world: gershmanlab.com/textbook.html It's a textbook called Computational Foundations of Cognitive Neuroscience, which I wrote for my class. My hope is that this will be a living document, continuously improved as I get feedback.
My talk at the terrific MAIN (Montreal AI and Neuroscience) meeting on cognition, neuroscience and AI linking Bayesian causal learning and empowerment in reinforcement learning. youtube.com/watch?v=qrwB...
(Keynote #4) Alison Gopnik - "Empowerment and Causal Learning in Humans and Machines"
YouTube video by MAIN Conference
youtube.com
How do LLMs process syntax? Do different syntactic phenomena recruit the same model units, or do they recruit distinct model components? And do different languages rely on similar units to process the same syntactic phenomenon? Check out our new preprint (to appear at ACL 2026)! shorturl.at/QWU81
Different types of syntactic agreement recruit the same units within large language models
Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the models remains an open question. We investigate ...
arxiv.org
📣 Calling experimental, computational, or theoretical researchers! WTI's Postdoc Fellowships application is now open, offering a competitive salary, structured mentorship, world-class facilities + more: wti.yale.edu/initiatives/... Apply by November 10: apply.interfolio.com/174525 #KnowTogether
Want the freedom of a fancy fellowship, but not the year-long wait or arduous application? Come join my lab! Work on neuroscience and AI, explore your creativity, be independent or work closely with me, collaborate widely, and have a lot of fun! my.corehr.com/pls/uoxrecru...
I did a QA with Quanta about interpretability and training dynamics! I got to talk about a bunch of research hobby horses and how I got into them.
Ben Brubaker@benbenbrubaker.bsky.social · 11mo agoI really enjoyed talking to @nsaphra.bsky.social about her thoughts on what much language model interpretability research misses. My latest in @quantamagazine.bsky.social:
UC Davis is hiring! A tenure-track assistant professor of psychology, in human cognition or cognitive neuroscience #psychjobs recruit.ucdavis.edu/JPF07300
Assistant Professor of Psychology - Human Cognition or Cognitive Neuroscience
University of California, Davis is hiring. Apply now!
recruit.ucdavis.edu
If you work on artificial or natural intelligence and are finishing your PhD, consider applying for a Kempner research fellowship at Harvard: kempnerinstitute.harvard.edu/kempner-inst...
Kempner Research Fellowship - Kempner Institute
The Kempner brings leading, early-stage postdoctoral scientists to Harvard to work on projects that advance the fundamental understanding of intelligence.
kempnerinstitute.harvard.edu
The beta release of SPM-Python is out now! Amazing work by Johan Medrano @johmedr.bsky.social , Yael Balbastre, Yulia Bezsudnova @ybezs.bsky.social and other members of their team. A new era for SPM! #OHBM2025
Department of Imaging Neuroscience@imagingneuroucl.bsky.social · last yr.Drumroll... The SPM team will announce that SPM is now fully accessible from Python! 🐍 Learn more about SPM-Python at the SPM roundtable event (Friday, 1pm) and poster number 1841 at #OHBM2025. Try the beta for yourself at github.com/spm/spm-python [2/7]
Job announcement 📢 @shawnrhoadsphd.bsky.social and I are looking for a joint postdoc interested in computational models of social interaction! Interested? If you’ll be at #rlc2025 (or I missed you at #cogsci2025) feel free to reach out with any questions! apply.interfolio.com/165809
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apply.interfolio.com
Shawn Rhoads@shawnrhoadsphd.com · last yr.📢 @markkho.bsky.social & I are recruiting a joint postdoc interested in computational models of social interaction & mental health 💻 Ideal candidates have experience w/ multi-player web-based experiments & computational modeling 📅 Apps are reviewed on a rolling basis 🔗 apply.interfolio.com/165809
How does the structure of a neural circuit shape its function? @neuralreckoning.bsky.social & I explore this in our new preprint: doi.org/10.1101/2025... 🤖🧠🧪 🧵1/9
Excited to announce the first workshop on CogInterp: Interpreting Cognition in Deep Learning Models @ NeurIPS 2025! 📣 How can we interpret the algorithms and representations underlying complex behavior in deep learning models? 🌐 coginterp.github.io/neurips2025/ 1/4
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First Workshop on Interpreting Cognition in Deep Learning Models (NeurIPS 2025)
coginterp.github.io
When neurons change, but behavior doesn’t: Excitability changes driving representational drift New preprint of work with Christian Machens: www.biorxiv.org/content/10.1...
Representational drift without synaptic plasticity
Neural computations support stable behavior despite relying on many dynamically changing biological processes. One such process is representational drift (RD), in which neurons' responses change over ...
biorxiv.org
You know how RL is that whole big thing nowadays? Present your work at the first-ever New York Reinforcement Learning Workshop (NYRL), co-organized by Amazon, Columbia Business School & NYU Tandon School of Engineering. ny-rl.com!
Super excited to have the #InfoCog workshop this year at #CogSci2025! Join us in SF for an exciting lineup of speakers and panelists, and check out the workshop's website for more info and detailed scheduled sites.google.com/view/infocog...
Cognitive Science Society@cogscisociety.bsky.social · last yr.#Workshop at #CogSci2025 Information Theory and Cognitive Science 🗓️ Wednesday, July 30 📍 Pacifica C - 8:30-10:00 🗣️ Noga Zaslavsky, Thomas A Langlois, Nathaniel Imel, Clara Meister, Eleonora Gualdoni, and Daniel Polani 🧑💻 underline.io/events/489/s...
📣 I'm looking for a postdoc to join my lab at NYU! Come work with me on a principled, theory-driven approach to studying language, learning, and reasoning, in humans and AI agents. Apply here: apply.interfolio.com/170656 And come chat with me at #CogSci2025 if interested!
Ireland launches global talent fund! www.researchireland.ie/funding/glob... If you're a neuroscience professor (assistant/associate/full) and would consider relocating to the vibrant and booming city of Dublin, please get in touch!
Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...
Discovering cognitive strategies with tiny recurrent neural networks - Nature
Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...
doi.org
🚀Join our team @tuda.bsky.social ! 🚀 I'm looking for 3 PhDs & 1 Postdoc for my @erc.europa.eu project “C4: Compositional Compression in Cognition and Culture” to study learning across individuals, teams, and cultural timescales 👉 PhD: hmc-lab.com/ERC_PhDs.html 👉 Postdoc: hmc-lab.com/ERC_Postdoc....
We just pushed “Memory by a 1000 rules” onto bioRxiv, where we use clever #ML to find #plasticity quadruplets (EE, EI, IE, II) that learn basic stability in spiking nets. Why is it cool? We find 1000s!! of solutions, and they don’t just stabilise. They #memorise! www.biorxiv.org/content/10.1...
Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety & degeneracy at the heart of learning
Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of str...
biorxiv.org
What are the organizing dimensions of language processing? We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals
