General Assembly wrap-up!🚀 Brainstormed exciting ideas for implementing our experience sampling software tool in the clinic. Ready for the journey ahead! #HealthTech #Innovation Support our IMMERSE project here: vbhcprize.com/nominees-vbhc-…

We won the 2024 VBHC award for collaboration! Dr. Weermeijer and Drs. Bonnier received it on behalf of the entire consortium. Huge thanks to our IMMERSE colleagues, the Value Based HealthCare Centre, and all our participants, clinicians, and advisory board members. 🏆 #VBHC

Are you a mental health practitioner curious about how our 'Experience Sampling' tool can enhance your practice? Watch the info video below to learn more! youtu.be/YbAJH-NJR1Y

Just wanted to stop by & say: We have 2 new accepted #NeurIPS2024 papers: 1) Manuel Brenner , Hemmer, Zahra Monfared, DD: Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction --> *this takes DSR to a new level!*, details to follow

2) Volkmann, Brändle, DD, Georgia Koppe: A scalable generative model for dynamical system reconstruction from neuroimaging data --> efficiently scales up fMRI analysis with generative DSR models! Again, details to follow ...

Creating digital twins of social interaction behavior with #AI! Our study shows how generative models can predict interactions from limited data, revealing hidden dynamics. Together with Manuel Brenner DurstewitzLab. Explore: osf.io/preprints/psya… #DigitalTwin #SocialBehavior

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: arxiv.org/pdf/2410.04814 1/4

We show applications like transfer & few-shot learning, but most interestingly perhaps, subject/system-specific features were often linearly related to control parameters of the underlying dynamical system trained on … 2/4

This gives rise to an interpretable latent feature space, where datasets with similar dynamics cluster. Intriguingly, this clustering according to *dynamical systems features* led to much better separation of groups than could be achieved by more trad. time series features. 3/4

Fantastic work by the incomparable Manuel Brenner and Elias Weber, together with Georgia Koppe! 4/4

📢New work on hippocampal assemblies & theta! nature.com/articles/s4146… It was a pleasure to work with Aleks Domanski, Nadine, Tim, Kipp Freud, DurstewitzLab, and Matt Jones (TheSleepDetectives). SantAnnaPisa Bristol Neuroscience Zentralinstitut für Seelische Gesundheit Bernstein Network Computational Neuroscience 🧵[1/11]

Can we use data-driven tools to guide the selection of ecological momentary interventions (EMI) to improve mental health? In this new study, we borrow concepts from control theory to personalize and optimize the selection of EMI. onlinelibrary.wiley.com/doi/pdf/10.100… 1/2

We demonstrate that data-driven strategies can be effective in identifying behavioral contingencies and enhancing the personalized selection of digital interventions. Together with DurstewitzLab Uli Reininghaus Hamidreza Jamalabadi 2/2

🚨Keynote speakers for the Computational Psychiatry Conference (14-16th July, Tubingen) announced 📢 Very excited to have excellent keynotes across theory-driven CP, machine learning, and clinical psychiatry: Andreas Heinz Georgia Koppe Chandra Sripada Sophie Valk tor wager

Our revised #ICLR2025 paper & code for a foundation model architecture for dynamical systems is now online: openreview.net/pdf?id=Vp2OAxM… ... incl. add. examples of how this may be used for identifying drivers (control par.) of non-stationary processes. And please switch platform!

📢 Early registration prices end tomorrow 15 April! 🚨 The 3rd Computational Psychiatry Conference is 14-16 July in Tübingen, Germany. cpconf.org Speakers inc. @PhilCorlett1 CharFraza Andreas Heinz Georgia Koppe Jill O'Reilly Chandra Sripada Sophie Valk tor wager

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 w rapid jumps. Feedback welcome!