DurstewitzLab

In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems” (preprint: arxiv.org/abs/2607.14937) we reduce a DS FM to ingredients minimally necessary to reproduce long-term stat. and geom. properties of DS, even outperforming many TS & DS FM.

In our #NeurIPS2026 paper “Topological Out-of-Domain Generalization in Dynamical Systems (DS) Reconstruction” (arxiv.org/abs/2606.22969) we aim to infer the DS generating observed TS jointly with control parameters, in order to extrapolate to different dynamical regimes, e.g. across tipping points.

In our #NeurIPS2026 spotlight “Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems (DS) Reconstruction” (preprint: arxiv.org/abs/2605.12683) we speed up training of nonlinear RNNs on time series from chaotic DS by >100x by combining DEER with generalized teacher forcing.

Next week I will talk about *foundation models for dynamical systems reconstruction* at ECML in Naples, ml4its.github.io/ml4its2026/. I will cover some of the latest from the group, like efficient parallel-in-time training for DSR and minimal mechanisms for zero-shot DSR.

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

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

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!

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

Revised version of our #NeurIPS2025 paper with full code base in Julia & Python now online, see arxiv.org/abs/2505.13192

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.

What a fantastic accomplishment -- and what a fantastic story! www.quantamagazine.org/at-17-hannah...

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!

Happy to discuss our work on parsimonious & math. tractable RNNs for dynamical systems reconstruction next week at cns2025florence.sched.com/event/1z9Mt/...

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!

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 …

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 🧪

Nature Communications Uncertainty estimation with prediction-error circuits www.nature.com/articles/s41...

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...

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.

Great to see this group on Bluesky. Durstewitz and Seamans 2008 is one of my all time favorite papers on the function of dopamine in the PFC www.researchgate.net/profile/Dani...