
DurstewitzLab
@DurstewitzLab
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Scientific machine learning, AI & data analysis, dynamical systems theory, applications in (computat.) neuroscience & psychiatry. @durstewitzlab.bsky.social
Mannheim+Heidelberg
Joined May 2019
We wrote a little #NeuroAI piece about in-context learning & neural dynamics vs. continual learning & plasticity, both mechanisms to flexibly adapt to changing environments:.We relate this to non-stationary rule learning w rapid jumps. Feedback welcome!.
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RT @russo_eleon: Into population dynamics? Coming to #CNS2025 but not quite ready to head home?. Come join us! at the Symposium on "Neural….
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We dive a bit into the reasons why current time series FMs not trained for DS reconstruction fail, and conclude that a DS perspective on time series forecasting & models may help to advance the #TimeSeriesAnalysis field. (6/6)
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And no, it’s neither based on Transformers nor Mamba – it’s a new type of mixture-of-experts architecture based on the recently introduced AL-RNN (, specifically trained for DS reconstruction. #AI.(4/6)
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Unlike TS FMs, DynaMix exhibits #ZeroShotLearning of long-term stats of unseen DS, incl. attrac. geom. & PS, w/o *any* re-training, just from a context signal. It does so with only 0.1% of the parameters of Chronos & 10x faster inference times than the closest competitor. (2/6)
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Can time series #FoundationModels like Chronos zero-shot generalize to unseen #DynamicalSystems (DS)?. No, they cannot. But *DynaMix* can, the first FM based on principles of DS reconstruction, capturing the long-term evolution of out-of-domain DS: (1/6)
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Our revised #ICLR2025 paper & code for a foundation model architecture for dynamical systems is now online: incl. add. examples of how this may be used for identifying drivers (control par.) of non-stationary processes. And please switch platform!.
Interested in interpretable #AI foundation models for #DynamicalSystems reconstruction?.In a new paper we move into this direction, training common latent DSR models with system-specific features on data from multiple different dynamic regimes and DS:.1/4
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Transfer & few-shot learning for dynamical systems . our paper just got accepted for #ICLR2025 @iclr_conf !.Thread below; strongly updated version will be available soon . and don't forget to move to bsky!.
Interested in interpretable #AI foundation models for #DynamicalSystems reconstruction?.In a new paper we move into this direction, training common latent DSR models with system-specific features on data from multiple different dynamic regimes and DS:.1/4
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2) A scalable generative model for dynamical system reconstruction from neuroimaging data. @GeorgiaKoppe.
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Don't miss out on our 2 #NeurIPS2024 papers on dynamical systems reconstruction today & tomorrow:. 1) Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction .
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RT @russo_eleon: 📢PhD position @BristolUni (with Ross Purple and @seanfw, UK) and joint supervision @SantAnnaPisa (with @russo_eleon, IT) o….
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Happy our team is among this years’ recipients of the Samsung Global Research Outreach Awards!. We will take #DynamicalSystems reconstruction to the next level, large-scale – looking forward to the collaboration with the Samsung team!.
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