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Jesse Geerts

@jessegeerts.bsky.social
228 followers 167 following 51 posts

Cognitive neuroscientist and AI researcher

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Reposted by Jesse Geerts
Neil Burgess @neilburgess10.bsky.social · 01/07/2026
Ever wondered how the hippocampal cognitive map is read-out? @changmin-yu.bsky.social @zilong-ji.bsky.social with Jake & John, show that, during navigation, theta sweeps indicate remembered goal-directions (cf. current/next movements or perceptual targets) 1/2 www.nature.com/articles/s41...
Decoded locations from place cell firing sweep towards a remembered goal location during each theta cycle irrespective of the current movement or head direction
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Jesse Geerts @jessegeerts.bsky.social · 14/05/2026
Thank you Neil!
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Jesse Geerts @jessegeerts.bsky.social · 14/05/2026
#ThinkingAboutThinking #ThATFellowship @thoughtchannel.bsky.social
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Jesse Geerts @jessegeerts.bsky.social · 14/05/2026
Looking forward to connecting with people thinking carefully and ambitiously about what comes next.
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Jesse Geerts @jessegeerts.bsky.social · 14/05/2026
As a Fellow my role will be to lead conversations around intelligence, contributing to discussions, seminars, research initiatives and community events across the year.
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Jesse Geerts @jessegeerts.bsky.social · 14/05/2026
@thoughtchannel.bsky.social is a non-profit organisation bringing together people across disciplines who want to contribute to the conversations shaping ideas about intelligence and the future of AI.
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Jesse Geerts @jessegeerts.bsky.social · 14/05/2026
Over the coming year, I'll be part of a global network working across AI, neuroscience, mathematics and scientific discovery. The programme includes online discussions, private seminars, in-person events and ongoing collaboration through the ThAT Fellowship Hub.
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Jesse Geerts @jessegeerts.bsky.social · 14/05/2026
I am honoured to share that I have been selected as a Thinking About Thinking Fellow for 2026.
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Botos Csabi @botoscsabi.bsky.social · 11/05/2026
Jeee 🐦‍⬛ I am very proud of our joint effort with @sreejan.bsky.social on the project "Reason to Play" LRMs show human-like rule discovery, and their hidden states predict human brain activity during gameplay 10x better than previous methods Interactive demo + paper: botcs.github.io/reason-to-pl...
botcs.github.io
Reason to Play - Behavioral and Brain Alignment
32 fMRI-scanned humans and 8 frontier open weight LLMs play ARC-AGI like games with no rules given. The reasoning models match the human learning trajectories and their hidden states predict human bra...
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The Transmitter @thetransmitter.bsky.social · 25/03/2026
On the latest episode, @braininspired.bsky.social with @juangallego.bsky.social about the wealth of evidence that supports the view that neural manifolds are real and useful, even if they may not completely solve the age-old mind-body problem. #neuroskyence www.thetransmitter.org/brain-inspir...
thetransmitter.org
Juan Gallego on manifolds and populations of neurons
A wealth of evidence supports the view that neural manifolds are real and useful, even if they may not solve the age-old mind-body problem.
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Institute of Psychiatry, Psychology & Neuroscience @kingsioppn.bsky.social · 19/03/2026
How do we know that things we do will have the outcomes we expect? We chat to @tobywise.bsky.social about his research into the relationship between compulsivity and uncertainty. Go give it a listen! #BrainAwarenessWeek open.spotify.com/episode/2hyj...
open.spotify.com
How is compulsivity related to uncertainty?
Inside Neuroscience · Episode
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Ida Momennejad @neuroai.bsky.social · 19/03/2026
This looks great! Congrats team!
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Jesse Geerts @jessegeerts.bsky.social · 19/03/2026
Thanks Ida, and thanks to you for the great task design of course! We're still working on more neural results, so can hopefully share more soon!
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Sam Gershman @gershbrain.bsky.social · 15/03/2026
Wow, awesome to see this work!
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Kim Stachenfeld, PhD @neurokim.bsky.social · 15/03/2026
Wonderful work by @weijiazh.bsky.social at #Cosyne2026, who beautifully navigated a busy poster session yesterday!
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Mark Histed @markhisted.org · 14/03/2026
My travel to Cosyne was barred by the Trump admin, so I'm here on my personal dime. I care about the COSYNE community, I committed to co-chairing. And I always learn here. But the worst part about this travel ban is my lab colleagues— students and fellows—couldn't come. /1
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Jesse Geerts @jessegeerts.bsky.social · 14/03/2026
Finally, I’ll be giving a talk on “Neuro-inspired evals for modern AI systems”, in Monday’s workshop on biologically inspired AI, at 11:35. I’ll talk about Weijia’s project and our recent preprint on relational reasoning in transformers. arxiv.org/abs/2506.04289
arxiv.org
Relational reasoning and inductive bias in transformers and large language models
Transformer-based models have demonstrated remarkable reasoning abilities, but the mechanisms underlying relational reasoning remain poorly understood. We investigate how transformers perform \textit{...
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Jesse Geerts @jessegeerts.bsky.social · 14/03/2026
In the same session, also make sure to visit poster [3-135] by @weijiazh.bsky.social , who’s presenting joint work with @neurokim.bsky.social and Josh Jacobs showing that Human Single Neurons and Predictive Models Remap Differently in Reward and Transition Relearning
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Jesse Geerts @jessegeerts.bsky.social · 14/03/2026
Excited to be at #COSYNE2026 ! I’m presenting our recent preprint with Francesca Greenstreet, @juangallego.bsky.social an and @clopathlab.bsky.social in today’s poster session [3-026] www.biorxiv.org/content/10.6...
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Kim Stachenfeld, PhD @neurokim.bsky.social · 11/03/2026
I have just arrived at @cosynemeeting.bsky.social ! During my flight, as I thought about opening remarks (and had a glass of wine), I got to reflect on why I love neuroscience. Maybe other folks are doing the same and would like to read or add, so I thought I'd do something unusual and share!
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UCL NeuroAI @ucl-neuroai.bsky.social · 24/02/2026
We are excited to have Dr. Tim Kietzmann from Osnabrück University for our next seminar! This will be an in-person plus online seminar! 🗓️Wed 11 March 2026 ⏰2-3pm GMT Talk title: NeuroAI - the synergy between machine learning and neuroscience Registration: www.eventbrite.co.uk/e/ucl-neuroa...
eventbrite.co.uk
UCL NeuroAI Talk Series
A series of NeuroAI themed talks organised by the UCL NeuroAI community. Talks will continue on a monthly basis.
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Jaeeon Lee @jel0624.bsky.social · 21/02/2026
🚨🚨New Preprint Alert!🚨🚨 www.biorxiv.org/content/10.6... Animal learning is painfully slow (at least initially). Yet, well trained animals can learn very fast, sometimes displaying few-shot inference. How does this transition occur?
biorxiv.org
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Athena Akrami @athenaakrami.bsky.social · 16/02/2026
Thrilled to finally share this work! 🧠🔊 Using a new reinforcement-free task we show mice (like humans) extract abstract structure from sound (unsupervised) & dCA1 is causally required by building factorised, orthogonal subspaces of abstract rules. Led by Dammy Onih! www.biorxiv.org/content/10.6...
biorxiv.org
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Jesse Geerts @jessegeerts.bsky.social · 09/02/2026
Code for our multi-region motor learning model is now available on GitHub! github.com/jessegeerts/...
github.com
GitHub - jessegeerts/action-embedding: Action embeddings for RL - model of motor adaptation and generalization
Action embeddings for RL - model of motor adaptation and generalization - jessegeerts/action-embedding
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Jesse Geerts @jessegeerts.bsky.social · 09/02/2026
Code to run this model and reproduce figures is now public: github.com/jessegeerts/...
github.com
GitHub - jessegeerts/action-embedding: Action embeddings for RL - model of motor adaptation and generalization
Action embeddings for RL - model of motor adaptation and generalization - jessegeerts/action-embedding
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Reposted by Jesse Geerts
Kim Stachenfeld, PhD @neurokim.bsky.social · 04/02/2026
Updated work from @jessegeerts.bsky.social extending his results on transitive inference in transformers (including LLMs!) updated paper: arxiv.org/abs/2506.04289 bleeprint (what are we calling these?) below ⬇️
arxiv.org
Relational reasoning and inductive bias in transformers and large language models
Transformer-based models have demonstrated remarkable reasoning abilities, but the mechanisms underlying relational reasoning remain poorly understood. We investigate how transformers perform \textit{...
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Jesse Geerts @jessegeerts.bsky.social · 04/02/2026
Updated paper: arxiv.org/abs/2506.04289. Joint work @ndrewliu.bsky.social, @scychan.bsky.social, @clopathlab.bsky.social, and @neurokim.bsky.social
arxiv.org
Relational reasoning and inductive bias in transformers and large language models
Transformer-based models have demonstrated remarkable reasoning abilities, but the mechanisms underlying relational reasoning remain poorly understood. We investigate how transformers perform \textit{...
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Jesse Geerts @jessegeerts.bsky.social · 04/02/2026
This parallels our small transformer findings: when models must reason from context, representational geometry determines success or failure at transitive inference.
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Jesse Geerts @jessegeerts.bsky.social · 04/02/2026
This effect was strongest when models couldn't fall back on stored knowledge (incongruent/permuted items). For congruent items where weight-stored knowledge helps, the geometric scaffold barely mattered.
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Jesse Geerts @jessegeerts.bsky.social · 04/02/2026
Across Gemini, Gemma, and GPT models, linear consistently led to higher accuracy on transitive inference prompts.
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Jesse Geerts @jessegeerts.bsky.social · 04/02/2026
We then prompted LLMs with different geometric scaffolds: "imagine these items on a number line" (linear) vs "on a circle" (circular). Circular orderings violate transitivity because relationships can wrap around (A>B>C>A).
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Jesse Geerts @jessegeerts.bsky.social · 04/02/2026
We used the ReCogLab dataset (github.com/google-deepm...) to test transitive inference with items that are congruent with world knowledge (whale > dolphin > goldfish), incongruent (goldfish > dolphin > whale), or random. This lets us tease apart reasoning from context vs relying on stored knowledge.
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Jesse Geerts @jessegeerts.bsky.social · 04/02/2026
Quick recap: how a transformer is pre-trained determines whether it can do transitive inference (A>B, B>C → A>C). In-weights learning → yes. ICL trained on copying → no. ICL pre-trained on linear regression → yes. But these are small-scale toy models. What about in LLMs?
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Jesse Geerts @jessegeerts.bsky.social · 04/02/2026
Update on this work! We've extended our transitive inference study to large language models 🧵
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Masahiro Nakano @masahironakano.bsky.social · 16/01/2026
I’m excited to share my first PhD preprint!🎉 We studied how interactions between medial entorhinal cortex (MEC) and hippocampus shape theta sequences during navigation, and asked whether some “planning-like” patterns in hippocampus could arise from upstream MEC dynamics. (1/8)
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Sam Gershman @gershbrain.bsky.social · 09/01/2026
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.
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Jesse Geerts @jessegeerts.bsky.social · 08/01/2026
Just to add one thing to this discussion: in our paper, the "supervised" network predicts the action, which is internally generated by the actor, which is why we assume the agent has access to it. We toyed with calling this self-supervised but didn't want to cause confusion with other SS work
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Jesse Geerts @jessegeerts.bsky.social · 07/01/2026
Thanks for sharing that paper! I was unaware of this but it's a cool result
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Juan Gallego @juangallego.bsky.social · 06/01/2026
New paper led by wonder postdocs Francesca Greenstreet and @jessegeerts.bsky.social and @clopathlab.bsky.social trying to understand why –in the "what for" sense– there are multiple motor learning systems –supervised and RL-based– in the brain. Check out Jesse's 🧵 www.biorxiv.org/content/10.6...
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Claudia Clopath Lab @clopathlab.bsky.social · 05/01/2026
Check out our new work on motor learning across multiple brain regions!
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
Thank you! Feel free to get in touch with comments or questions
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
Many thanks to first author Francesca Greenstreet (equal contribution), and to @juangallego.bsky.social and @clopathlab.bsky.social!
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
The key insight: supervised learning in ctx/cerebellum doesn't just predict actions - it builds a structured space that makes RL in basal ganglia faster and enables generalization between similar movements. We make several predictions in the paper: www.biorxiv.org/content/10.6... (8/9)
biorxiv.org
Why motor learning involves multiple systems: an algorithmic perspective
The initial stage of learning motor skills involves exploring vast action spaces, making it impractical to learn the value of every possible action independently. This poses a challenge for standard r...
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
3. Limits on dual adaptation (shown by Woolley et al 2007) also emerge: learning opposite rotations for nearby targets fails because their policies overlap in embedding space. Distant targets adapt independently. (7/9)
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
2. The model also captures classic behavioural findings such as fast visuomotor adaptation (e.g. Krakauer et al. 2000). In our model, this emerges from retraining only the linear decoder. The characteristic generalization profile falls out without additional assumptions. (6/9)
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
1. Recent work shows similar striatal activity for similar reaches (Park et al. 2025), while classic work shows distinct activity for distinct choices. Our model captures both: if basal ganglia learn policies in a structured embedding, policy similarity scales with action similarity. (5/9)
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
Our model combines two learning systems: a supervised encoder-decoder (ctx/cerebellum) learns embeddings where similar movements cluster together, by predicting actions via a bottleneck. An actor-critic network (basal ganglia) learns policies directly in this low-dimensional embedding space. (4/9)
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
We were inspired by recent machine learning approaches which learn structured action representations for tasks like robotics, where action spaces are vast 🤖 (3/9)
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
The problem: learning motor skills means exploring vast action spaces (think: every muscle combination). Standard RL models treat each action independently, which is slow and scales badly with the size of the action space (2/9)
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Jesse Geerts @jessegeerts.bsky.social · 05/01/2026
🧠 New year, new preprint! Why does motor learning involve multiple brain regions? We propose that the cortico-cerebellar system learns a "map" of actions where similar movements are nearby, while basal ganglia do RL in this simplified space. www.biorxiv.org/content/10.6...
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