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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

1mo

DurstewitzLab

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!

Tomorrow Christoph will present DynaMix, the first foundation model for dynamical systems reconstruction, at #NeurIPS2025 Exhibit Hall C,D,E #2303

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.

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

3mo

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!!

6mo

3mo

6mo

7mo

5mo

DurstewitzLab

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

True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics

DurstewitzLab

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

i need the “llms are might be conscious” folx to read this

3mo

Memming Park

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...

8mo

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

Joao Barbosa

Dr Abeba Birhane

May 20, 2025

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)

3mo

I’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?