Both brains and RNNs can re-use components of computation across similar tasks or contexts. But what exactly are those “shared components”? How can they be used to solve several tasks? We address these questions in a new preprint with @avm.bsky.social! Link: www.biorxiv.org/content/10.6...

How can the brain learn the hidden hierarchical structure from high dimensional data? In our latest work, we use synthetic datasets to analyze two classes of bio-plausible learning rules: variants of Direct Feedback Alignment, and local self-supervised learning. We find only the latter succeeds.

📂 We're sharing code of a classic from the lab! A biologically inspired RL model from Frémaux, Sprekeler @sprekeler.bsky.social & Gerstner (2013, PLOS CB): a continuous-time actor–critic using spiking neurons that learn to navigate a water maze

Accepted at ICML 2026. I am extremely proud of this work. Local learning is starting to work really well! The theoretical perspective of local learning minimizing an emergent global objective has been eye opening Congrats @zihan-wu.bsky.social and Ariane Delrocq! Great team

How far are biologically-plausible local learning rules from backpropagation (BP)? In our new ICML paper we look at local self-supervised learning and find how to better align its gradients to BP. We achieve competitive performance on various datasets!

Ever heard of the lottery ticket hypothesis? Our new paper shows that lottery tickets are not a useful metaphor to explain the success of overparameterized neural networks - and suggests an alternative metaphor: escape dimensions

Incredibly grateful to @cmiehl.bsky.social and @gjorjulijana.bsky.social for their thoughtful and spot-on Preview "Novelty beyond counting" of our recent work in Neuron with @modirshanechi.bsky.social and @gerstnerlab.bsky.social! 🧠✨ It's a real honor 🙏 www.cell.com/neuron/fullt...

Unbelievably honoured to read Tatiana Engel's (@engeltatiana.bsky.social) wonderfully written Preview on our work "Linking neural manifolds to circtuit structure in recurrent networks" (with @lpezon.bsky.social & @gerstnerlab.bsky.social) in this issue of Neuron www.cell.com/neuron/fullt... 🙏

Episode #39 in #TheoreticalNeurosciencePodcast: On modeling neural population activity with mean-field models – with Tilo Schwalger theoreticalneuroscience.no/thn39 How can mean‑field models be systematically derived from the underlying microscopic dynamics of individual neurons?

Novelty is not just about whats new, but also what feels new given past experience. New study from Sophia Becker in Wulfram Gerstner’s @epfl-brainmind.bsky.social lab posits a model showing how similarity between familiar and novel stimuli shapes exploration and learning - doi.org/10.1016/j.ne...

Presenting a poster tomorrow at Cosyne 26: [3-033] Compositional computation via shared latent dynamics in low-rank RNNs. With @avm.bsky.social, we explore how RNNs can re-use the same dynamics across different tasks, and what it implies for their connectivity and neural activity.

Excited to share our new paper to be published in Neuron! With Valentin Schmutz @bio-emergent.bsky.social and Wulfram Gerstner @gerstnerlab.bsky.social, we explore how circuit structure in RNNs shapes network computation and single-neuron responses. www.sciencedirect.com/science/arti...

🧵Excited to present our latest work at #Neurips25! Together with @avm.bsky.social, we discover 𝐜𝐡𝐚𝐧𝐧𝐞𝐥𝐬 𝐭𝐨 𝐢𝐧𝐟𝐢𝐧𝐢𝐭𝐲: regions in neural networks loss landscapes where parameters diverge to infinity (in regression settings!) We find that MLPs in these channels can take derivatives and compute GLUs 🤯

This was a lot of fun! From my side, it started with a technical Q: what's the relation between two-side cavity and path integrals? Turns out it's a fluctuation correction - and amazingly, this also enable the "O(N) rank" theory by @david-g-clark.bsky.social and @omarschall.bsky.social. 🤯

Lab members are at the Bernstein conference @bernsteinneuro.bsky.social with 9 posters! Here’s the list: TUESDAY 16:30 – 18:00 P1 62 “Measuring and controlling solution degeneracy across task-trained recurrent neural networks” by @flavioh.bsky.social

New in @pnas.org: doi.org/10.1073/pnas... We study how humans explore a 61-state environment with a stochastic region that mimics a “noisy-TV.” Results: Participants keep exploring the stochastic part even when it’s unhelpful, and novelty-seeking best explains this behavior. #cogsci #neuroskyence

🎉 "High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model" will be presented as an oral at #NeurIPS2025 🎉 Feeling very grateful that reviewers and chairs appreciated concise mathematical explanations, in this age of big models. www.biorxiv.org/content/10.1... 1/2

🧠 “You never forget how to ride a bike”, but how is that possible? Our study proposes a bio-plausible meta-plasticity rule that shapes synapses over time, enabling selective recall based on context

So happy to see this work out! 🥳 Huge thanks to our two amazing reviewers who pushed us to make the paper much stronger. A truly joyful collaboration with @lucasgruaz.bsky.social, @sobeckerneuro.bsky.social, and Johanni Brea! 🥰 Tweeprint on an earlier version: bsky.app/profile/modi... 🧠🧪👩‍🔬

Attending #CCN2025? Come by our poster in the afternoon (4th floor, Poster 72) to talk about the sense of control, empowerment, and agency. 🧠🤖 We propose a unifying formulation of the sense of control and use it to empirically characterize the human subjective sense of control. 🧑‍🔬🧪🔬

Is it possible to go from spikes to rates without averaging? We show how to exactly map recurrent spiking networks into recurrent rate networks, with the same number of neurons. No temporal or spatial averaging needed! Presented at Gatsby Neural Dynamics Workshop, London.

Excited to present at the PIMBAA workshop at #RLDM2025 tomorrow! We study curiosity using intrinsically motivated RL agents and developed an algorithm to generate diverse, targeted environments for comparing curiosity drives. Preprint (accepted but not yet published): osf.io/preprints/ps...

Stoked to be at RLDM! Curious how novelty and exploration are impacted by generalization across similar stimuli? Then don't miss my flash talk in the PIMBAA workshop (tmr at 10:30, E McNabb Theatre) or stop by my poster tmr (#74)! Looking forward to chat 🤩 www.biorxiv.org/content/10.1...

Our new preprint 👀

Interested in high-dim chaotic networks? Ever wondered about the structure of their state space? @jakobstubenrauch.bsky.social has answers - from a separation of fixed points and dynamics onto distinct shells to a shared lower-dim manifold and linear prediction of dynamics.

Episode #22 in #TheoreticalNeurosciencePodcast: On 50 years with the Hopfield network model - with Wulfram Gerstner theoreticalneuroscience.no/thn22 John Hopfield received the 2024 Physics Nobel prize for his model published in 1982. What is the model all about? @icepfl.bsky.social

A cool EPFL News article was written about our recent neurotheory paper on spikes vs rates! Super engaging text by science communicater Nik Papageorgiou. actu.epfl.ch/news/brain-m... Definitely more accessible than the original physics-style, 4.5-page letter 🤓 journals.aps.org/prl/abstract...

Super excited to see my PhD thesis featured by EPFL! 🎓 actu.epfl.ch/news/learnin... P.S.: There's even a French version of the article! It feels so fancy! 😎 👨‍🎨 🇫🇷 actu.epfl.ch/news/apprend...

New round of spike vs rate? The concentration of measure phenomenon can explain the emergence of rate-based dynamics in networks of spiking neurons, even when no two neurons are the same. This is what's shown in the last paper of my PhD, out today in Physical Review Letters 🎉 tinyurl.com/4rprwrw5

Pre-print 🧠🧪 Is mechanism modeling dead in the AI era? ML models trained to predict neural activity fail to generalize to unseen opto perturbations. But mechanism modeling can solve that. We say "perturbation testing" is the right way to evaluate mechanisms in data-constrained models 1/8