Matthijs Pals @matthijs_pals
Using deep learning to elucidate neural representations and dynamics @MackeLab Tübingen, Germany Joined November 2021-
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🎉Finally published @PLOSCompBiol ! Why do neurons use low-frequency oscillations for encoding? Why not use higher frequencies for better sampling resolution? We identify a speed-precision trade-off driven by noise, showing that theta (3–8 Hz) maximizes bits/s! Check it out 👇
The sbi package is growing into a community project 🌎 To reflect this and the algorithms, neural nets, and diagnostics that have been added since its initial release, we have written a new software paper. Reach out if you want to get involved: arxiv.org/abs/2411.17337
Want to stay up-to-date with exciting #ml4science #ai4science research happening in our lab? Come have a look at @mackelab.bsky.social :).
Want a tool that uses ML to generate REALLY good fake brain recordings? You're getting one. Julius' paper on diffusion models for brain data is published! Works with all kinds of densely sampled, multichannel continuous signals (LFP, EEG, etc.) cell.com/patterns/fullt…
A propos: Inspired to do a PhD or Postdoc in #ML4Science/#AI4Science? We have multiple openings to work ML and AI tools for scientific discovery, in neuroscience and beyond! Full details: mackelab.org/media/Ad_Macke… Students: Apply by Nov 15, directly to IMPRS-IS or @ELLISforEurope
A propos: Inspired to do a PhD or Postdoc in #ML4Science/#AI4Science? We have multiple openings to work ML and AI tools for scientific discovery, in neuroscience and beyond! Full details: mackelab.org/media/Ad_Macke… Students: Apply by Nov 15, directly to IMPRS-IS or @ELLISforEurope
At Poster III-69 @matthijs_pals will explain how to fit RNNs to neural data - and use them as generative models. Want to understand the fit models? We show how to obtain all fixed points in low-rank piecewise-linear RNNs.
We’re at Bernstein Conference next week with lots of new work to share: 10 posters, 1 workshop talk, and don’t miss @jakhmack’s invited talk on Wednesday! If you’re excited about machine learning for (neuro)science, come chat with us—we’re hiring PhD students & postdocs!
We’re stoked to share: “A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science”. Now out in TMLR: openreview.net/forum?id=isEFz… This was an incredibly special project for us, as it involved the **entire** lab getting together!
Back in 2022, @roxana_zeraati & I organized a Cosyne workshop on neural timescales, and after working on it for the last 2 years together, it's now a review paper! arxiv.org/abs/2409.02684 w/ @SelfOrgAnna & @jakhmack (2nd blogpost to turn into a real review paper this year lol)
📜🎉 We have a new preprint: an overview & comparison of measures of 'aperiodic' neural activity! This project explores different ideas & many methods used to study non-oscillatory features of intra- & extra-cranial electrophysiological recordings! biorxiv.org/content/10.110…
Want to train neuroscience models consisting of single cells, recurrent neural networks (RNNs), or huge feedforward networks - all with detailed biophysics? @deismic_'s Jaxley has your back! 👇
Want to train neuroscience models consisting of single cells, recurrent neural networks (RNNs), or huge feedforward networks - all with detailed biophysics? @deismic_'s Jaxley has your back! 👇
Biggest joy and honour leading this project at the intersection of visual neuroscience and ML to a successful finish! Paper: nature.com/articles/s4158…
My #AI4Neuro magnum opus: Discovery of spiking network model parameters constrained by neural recordings, using simulation-based inference & generative “AI”. (aka the answer to “how the f did you end up in Tübingen?”) Here's what we have in store: biorxiv.org/content/10.110…
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