DurstewitzLab
@durstewitzlab.bsky.social
Scientific AI/ machine learning, dynamical systems (reconstruction), generative surrogate models of brains & behavior, applications in neuroscience & mental health
We congratulate @matthijspals.bsky.social on his successful PhD defense! During his time in the Mackelab, he used RNNs to link neural activity with underlying mechanisms. Now he moved on to a Postdoc position in @durstewitzlab.bsky.social at the ZI Mannheim and the University of Heidelberg.
In a #ICML2026 position paper we argue a dynamical systems perspective is needed to drive time series models forward: arxiv.org/abs/2602.16864 For TS, we need to move away from transformers that do not respect a system’s dynamical structure, esp. if out-of-domain generalization & insight is sought.
Neural ODEs are great as continuous-time dynamical systems, but slow and tedious to train. In a new #ICML2026 paper we intro a novel solver for continuous-time RNNs that does not rely on numerical integration. It's not only way faster & robust, but enables explicit analysis: arxiv.org/abs/2602.15649
i need the “llms are might be conscious” folx to read this
Jason Koebler@jasonkoebler.bsky.social · 5mo agoI met with AI data labelers in Kenya who are organizing their colleagues to fight the brutal working conditions and horrible pay given to the workers at the "bottom of the AI supply chain." They believe the NDAs they've signed are unenforceable so are speaking out: www.404media.co/ai-is-africa...
From the top of my head, here some recent ones: "Two views on the cognitive brain" by @johnwkrakauer.bsky.social, @dlbarack.bsky.social "Reconstructing computational system dynamics from neural data with recurrent neural networks" by @durstewitzlab.bsky.social et al 1/3
Nikolas Karalis@nikolaskaralis.bsky.social · 5mo agoI’m building a foundational reading list for our lab (systems & circuit neuroscience, compneuro, modeling, neuromodulators, population coding etc.). I’d like to crowdsource recommendations. Which review(s) would you consider mandatory reading for the next generation of researchers?
In a new #ICLR2026 paper we provide an algorithm for semi-analytically constructing un-/stable manifolds of fixed points and cycles of ReLU-based RNNs: openreview.net/pdf?id=EAwLA... These manifolds provide a skeleton for the system’s dynamics, dissecting the state space into basins of attraction.
We had a go at a blog about our recent dynamical systems foundation model published at NeurIPS (with strong support from the Structures outreach team!) … let us know your thoughts!
STRUCTURES Cluster of Excellence Heidelberg@structures-hd.bsky.social · 6mo agoWith DynaMix, researchers bring ideas from physics into machine learning to uncover the dynamics underlying complex data. Learn all about this on the new post by Daniel Durstewitz @durstewitzlab.bsky.social and Christoph Hemmer: structures.uni-heidelberg.de/blog/posts/2... 2/2 #Dynamics #AI
Fully-funded International Neuroscience Doctoral Programme🧠 Champalimaud Foundation, Lisbon, Portugal 🇵🇹 Deadline: Jan 31, 2026 fchampalimaud.org/champalimaud... Research program spans systems/computational/theoretical/clinical/sensory/motor neuroscience, neuroethology, intelligence, and more!!
Tomorrow Christoph will present DynaMix, the first foundation model for dynamical systems reconstruction, at #NeurIPS2025 Exhibit Hall C,D,E #2303
Unlike current AI systems, animals can quickly and flexibly adapt to changing environments. This is the topic of our new perspective in Nature MI (rdcu.be/eSeif), where we relate dynamical and plasticity mechanisms in the brain to in-context and continual learning in AI. #NeuroAI
What neuroscience can tell AI about learning in continuously changing environments
Nature Machine Intelligence - Durstewitz et al. explore what artificial intelligence can learn from the brain’s ability to adjust quickly to changing environments. By linking neuroscience...
rdcu.be
Revised version of our #NeurIPS2025 paper with full code base in Julia & Python now online, see arxiv.org/abs/2505.13192
True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics
Complex, temporally evolving phenomena, from climate to brain activity, are governed by dynamical systems (DS). DS reconstruction (DSR) seeks to infer generative surrogate models of these from observe...
arxiv.org
DurstewitzLab@durstewitzlab.bsky.social · 11mo agoOur #AI #DynamicalSystems #FoundationModel DynaMix was accepted to #NeurIPS2025 with outstanding reviews (6555) – first model which can *zero-shot*, w/o any fine-tuning, forecast the *long-term statistics* of time series provided a context. Test it on #HuggingFace: huggingface.co/spaces/Durst...
Our #AI #DynamicalSystems #FoundationModel DynaMix was accepted to #NeurIPS2025 with outstanding reviews (6555) – first model which can *zero-shot*, w/o any fine-tuning, forecast the *long-term statistics* of time series provided a context. Test it on #HuggingFace: huggingface.co/spaces/Durst...
DynaMix - a Hugging Face Space by DurstewitzLab
Upload your time series data in CSV or NPY format and generate future forecasts. Configure the forecast length and settings, then download the results as CSV or NPY.
huggingface.co
DurstewitzLab@durstewitzlab.bsky.social · last yr.Can time series (TS) #FoundationModels (FM) like Chronos zero-shot generalize to unseen #DynamicalSystems (DS)? No, they cannot! But *DynaMix* can, the first TS/DS FM based on principles of DS reconstruction, capturing the long-term evolution of out-of-domain DS: arxiv.org/pdf/2505.131... (1/6)
We have openings for several fully-funded positions (PhD & PostDoc) at the intersection of AI/ML, dynamical systems, and neuroscience within a BMFTR-funded Neuro-AI consortium, at Heidelberg University & Central Institute of Mental Health: www.einzigartigwir.de/en/job-offer... More info below ...
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.
From Spikes To Rates
YouTube video by Gerstner Lab
youtu.be
Today I joined >1900 members of US National Academies of Science, Engineering & Medicine signing this open letter (views our own). Leadership of science by US has been paramount for >70yrs & Admin is now acting to throw it all away! docs.google.com/document/d/1... www.nytimes.com/2025/03/31/s...
Public Statement on Supporting Science for the Benefit of All Citizens
TO THE AMERICAN PEOPLE We all rely on science. Science gave us the smartphones in our pockets, the navigation systems in our cars, and life-saving medical care. We count on engineers when we drive acr...
docs.google.com
What a fantastic accomplishment -- and what a fantastic story! www.quantamagazine.org/at-17-hannah...
At 17, Hannah Cairo Solved a Major Math Mystery | Quanta Magazine
After finding the homeschooling life confining, the teen petitioned her way into a graduate class at Berkeley, where she ended up disproving a 40-year-old conjecture.
quantamagazine.org
Got prov. approval for 2 major grants in Neuro-AI & Dynamical Systems Reconstruction, on learning & inference in non-stationary environments, out-of-domain generalization, and DS foundation models. To all AI/math/DS enthusiasts: Expect job announcements (PhD/PostDoc) soon! Feel free to get in touch.
We wrote a little #NeuroAI piece about in-context learning & neural dynamics vs. continual learning & plasticity, both mechanisms to flexibly adapt to changing environments: arxiv.org/abs/2507.02103 We relate this to non-stationary rule learning tasks with rapid performance jumps. Feedback welcome!
What Neuroscience Can Teach AI About Learning in Continuously Changing Environments
Modern AI models, such as large language models, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task, and then deployed with fixed parameters. Their training ...
arxiv.org
Happy to discuss our work on parsimonious & math. tractable RNNs for dynamical systems reconstruction next week at cns2025florence.sched.com/event/1z9Mt/...
CNS*2025 Florence: NeuroXAI: Explainable AI for Understandi...
View more about this event at CNS*2025 Florence
cns2025florence.sched.com
How do animals learn new rules? By systematically testing diff. behavioral strategies, guided by selective attn. to rule-relevant cues: rdcu.be/etlRV Akin to in-context learning in AI, strategy selection depends on the animals' "training set" (prior experience), with similar repr. in rats & humans.
Abstract rule learning promotes cognitive flexibility in complex environments across species
Nature Communications - Whether neurocomputational mechanisms that speed up human learning in changing environments also exist in other species remains unclear. Here, the authors show that both...
rdcu.be
What a line up!! With Lorenzo Gaetano Amato, Demian Battaglia, @durstewitzlab.bsky.social, @engeltatiana.bsky.social, @seanfw.bsky.social, Matthieu Gilson, Maurizio Mattia, @leonardopollina.bsky.social, Sara Solla.
Into population dynamics? Coming to #CNS2025 but not quite ready to head home? Come join us! at the Symposium on "Neural Population Dynamics and Latent Representations"! 🧠 📆 July 10th 📍 Scuola Superiore Sant’Anna, Pisa (and online) 👉 Free registration: neurobridge-tne.github.io #compneuro
I’m really looking so much forward to this! In wonderful Pisa!
Eleonora Russo@russoel.bsky.social · last yr.Into population dynamics? Coming to #CNS2025 but not quite ready to head home? Come join us! at the Symposium on "Neural Population Dynamics and Latent Representations"! 🧠 📆 July 10th 📍 Scuola Superiore Sant’Anna, Pisa (and online) 👉 Free registration: neurobridge-tne.github.io #compneuro
Just heading back from a fantastic workshop on neural dynamics at Gatsby/ London, organized by Tatiana Engel, Bruno Averbeck, & Peter Latham. Enjoyed seeing so many old friends, Memming Park, Carlos Brody, Wulfram Gerstner, Nicolas Brunel & many others … Discussed our recent DS foundation models …
Can time series (TS) #FoundationModels (FM) like Chronos zero-shot generalize to unseen #DynamicalSystems (DS)? No, they cannot! But *DynaMix* can, the first TS/DS FM based on principles of DS reconstruction, capturing the long-term evolution of out-of-domain DS: arxiv.org/pdf/2505.131... (1/6)
I'm presenting our lab's work on *learning generative dynamical systems models from multi-modal and multi-subject data* in the world-wide theoretical neurosci seminar Wed 23rd, 11am ET: www.wwtns.online --> incl. recent work on building foundation models for #dynamical-systems reconstruction #AI 🧪
Home | Neuroscience | World Wide Theoretical Neuroscience Seminar
WWTNS is a weekly digital seminar on Zoom targeting the theoretical neuroscience community. Its aim is to be a platform to exchange ideas among theoreticians.
wwtns.online
Nature Communications Uncertainty estimation with prediction-error circuits www.nature.com/articles/s41...
Uncertainty estimation with prediction-error circuits - Nature Communications
How the brain integrates sensory input and predictions to adapt to change is not fully understood. Here authors build a neural network model to show how prediction-error neurons compute uncertainty of...
nature.com
My latest post is now out. I show how Trump's attacks on science and universities are neither random nor new - they fit very precisely into the authoritarian playbook. This means we can guess what might come next and prepare - and we must! christinapagel.substack.com/p/censor-pur...
Censor, purge, defund: how Trump following the authoritarian playbook on science and universities
I have mapped 35 of the Trump administration's attacks on science and universities to the authoritarian playbook - and consider what it means for attacks still to come
christinapagel.substack.com
Our revised #iclr2025 paper and codebase for an architecture for foundation models for dynamical systems reconstruction is now online: openreview.net/pdf?id=Vp2OA... ... includes additional examples of how this may be harvested for identifying drivers (control par.) of non-stationary processes.
openreview.net
DurstewitzLab@durstewitzlab.bsky.social · 2y agoToward interpretable #AI foundation models for #DynamicalSystems reconstruction: Our paper on transfer & few-shot learning for dynamical systems just got accepted for #ICLR2025 ! Previous version: arxiv.org/pdf/2410.04814; strongly updated version will be available soon ... (1/4)
Toward interpretable #AI foundation models for #DynamicalSystems reconstruction: Our paper on transfer & few-shot learning for dynamical systems just got accepted for #ICLR2025 ! Previous version: arxiv.org/pdf/2410.04814; strongly updated version will be available soon ... (1/4)