We are excited to announce up to 3 PhD positions in a new project on using ML for "Extracting Probabilistic Representations in Exponential Quantum Spaces" (EXPRESSO) with @philipphennig.bsky.social , @mariokrenn.bsky.social, Igor Lesanovsky, @gmartius.bsky.social in the @ml4science.bsky.social.

Mackelab is at @icmlconf.bsky.social in Seoul 🇰🇷 Two papers on simulation-based and model-based scientific discovery this year. Come say hi!

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.

New paper: We recast automated scientific model discovery with LLMs as Bayesian inference! LLMs write code and carry domain knowledge, great for proposing models. The key idea: discovery is inference, not just generation. What distribution of models explains the data? 🧵 arxiv.org/abs/2602.18266

Come and work with us! We have a PostDoc position at the intersection of ML and Biogeoscience within the TERRA excellence cluster @terra-cluster.org, w/ Senckenberg. Be part of a great ML and Geo community and use ML to investigate fire and its impact on global vegetation🔥 🌱🌳 www.mackelab.org/jobs/

@mackelab.bsky.social is at @cosynemeeting.bsky.social #cosyne2026 in Lisbon with two posters presented by PhD students from the lab. Thread below on the projects 👇

The PhD graduation streak continues with Dr. Janne Lappalainen (@lappalainenjk.bsky.social) and Dr. Auguste Schulz (@auschulz.bsky.social) successfully defending in January and February. Congratulations!

Happy 2026 everyone! Two freshly minted PhDs 🧑‍🎓emerged from our lab at the end of last year. We congratulate Dr Julius Vetter (@vetterj.bsky.social) and Dr Guy Moss (@gmoss13.bsky.social)! Here seen celebrating with the lab 🎳. 1/3

Our group is at NeurIPS and EurIPS this year with four papers and one workshop poster. If you are either curious about SBI with autoML, with foundation models, or on function spaces or about differentiable simulators with Jaxley, have a look below 👇 1/11

I’m super excited to present our new work in #Eurips2025 and #Neurips2025! We developed FNOPE: a new simulation-based inference (SBI) method which excels at inferring function-valued parameters! Paper: openreview.net/forum?id=yB5... Code: github.com/mackelab/fnope (1/9)

We are looking for a Research Engineer (E13 TV-L) to work at the intersection of #ML and #compneuro! 🤖🧠 Help us build large-scale bio-inspired neural networks, write high-quality research code, and contribute to open-source tools like jaxley, sbi, and flyvis 🪰. More info: www.mackelab.org/jobs/

MackeLab has grown! 🎉 Warm welcome to 5(!) brilliant and fun new PhD students / research scientists who joined our lab in the past year — we can’t wait to do great science and already have good times together! 🤖🧠 Meet them in the thread 👇 1/7

Simulation-based inference (SBI) has transformed parameter inference across a wide range of domains. To help practitioners get started and make the most of these methods, we joined forces with researchers from many institutions and wrote a practical guide to SBI. 📄 Paper: arxiv.org/abs/2508.12939

Simulation-Based Inference: A Practical Guide

A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers a principled framewo...

arxiv.org

Our work on training biophysical models with Jaxley is now out in @natmethods.nature.com. Led by @deismic.bsky.social, with @philipp.hertie.ai, @ppjgoncalves.bsky.social & @jakhmack.bsky.social et al. Paper: www.nature.com/articles/s41...

Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics - Nature Methods

Jaxley is a versatile platform for biophysical modeling in neuroscience. It allows efficiently simulating large-scale biophysical models on CPUs, GPUs and TPUs. Model parameters can be optimized with ...

nature.com

Congrats to Dr Michael Deistler @deismic.bsky.social, who defended his PhD! Michael worked on "Machine Learning for Inference in Biophysical Neuroscience Simulations", focusing on simulation-based inference and differentiable simulation. We wish him all the best for the next chapter! 👏🎓

The Macke lab is well-represented at the @bernsteinneuro.bsky.social conference in Frankfurt this year! We have lots of exciting new work to present with 7 posters (details👇) 1/9

From hackathon to release: sbi v0.25 is here! 🎉 What happens when dozens of SBI researchers and practitioners collaborate for a week? New inference methods, new documentation, lots of new embedding networks, a bridge to pyro and a bridge between flow matching and score-based methods 🤯 1/7 🧵

New preprint: SBI with foundation models! Tired of training or tuning your inference network, or waiting for your simulations to finish? Our method NPE-PF can help: It provides training-free simulation-based inference, achieving competitive performance with orders of magnitude fewer simulations! ⚡️

The neurons that encode sequential information into working memory do not fire in that same order during recall, a finding that is at odds with a long-standing theory. Read more in this month’s Null and Noteworthy. By @ldattaro.bsky.social #neuroskyence www.thetransmitter.org/null-and-not...

Null and Noteworthy: Neurons tracking sequences don’t fire in order

Instead, neurons encode the position of sequential items in working memory based on when they fire during ongoing brain wave oscillations—a finding that challenges a long-standing theory.

thetransmitter.org

Many people in our lab use Scholar Inbox regularly -- highly recommended!

ML for Science@ml4science.bsky.social · last yr.

On our blog: Science is moving fast. How do we keep up? #ScholarInbox, developed by the Autonomous Vision Group led by @andreasgeiger.bsky.social, helps researchers stay ahead - by making the discovery of #openaccess papers smarter and more personal: www.machinelearningforscience.de/en/scholar-i...

Thrilled to share that our paper on using simulation-based inference for inferring ice accumulation and melting rates for Antarctic ice shelves is now published in Journal of Glaciology! www.cambridge.org/core/journal...

Great news! Our March SBI hackathon in Tübingen was a huge success, with 40+ participants (30 onsite!). Expect significant updates soon: awesome new features & a revamped documentation you'll love! Huge thanks to our amazing SBI community! Release details coming soon. 🥁 🎉

🎓Hiring now! 🧠 Join us at the exciting intersection of ML and Neuroscience! #AI4science We’re looking for PhDs, Postdocs and Scientific Programmers that want to use deep learning to build, optimize and study mechanistic models of neural computations. Full details: www.mackelab.org/jobs/ 1/5

Excited to present our work on compositional SBI for time series at #ICLR2025 tomorrow! If you're interested in simulation-based inference for time series, come chat with Manuel Gloeckler or Shoji Toyota at Poster #420, Saturday 10:00–12:00 in Hall 3. 📰: arxiv.org/abs/2411.02728

Compositional simulation-based inference for time series

Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likelihoods, it often requir...

arxiv.org

Thanks so much for the shout-out, and congrats on your exciting work!! 🎉 🙂 Also, a good reminder to share that our work is now out in Cell Reports 🙏🎊 ⬇️ www.cell.com/cell-reports...

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

Schulz et al. demonstrate how neural encoding and decoding can be cast as computing conditional distributions and how to modify variational autoencoders (VAEs) to calculate such distributions. The pro...

cell.com

Cole Hurwitz@colehurwitz.bsky.social · last yr.

Current neurofoundation modeling approaches focus on either predicting neuronal responses (Wang et al. 2025) or behavior (POYO, POYO+, NDT2). Inspired by Schulz et al. 2025, we unify these two tasks using a multimodal masked modeling approach we call "Neural Encoding and Decoding at Scale (NEDS)"

The @mackelab.bsky.social is represented at @cosynemeeting.bsky.social #cosyne2025 in Montreal with 3 posters, 2 workshop talks, and a main conference contributed talk (for the very first time in Mackelab history 🎉)!

Exciting new paper out of a Tübingen-Bonn collaboration, with three researchers from our cluster involved: first author @stefanieliebe.bsky.social, @matthijspals.bsky.social & @mackelab.bsky.social. Congrats to the team!

C3N@c3neuro.bsky.social · last yr.

Science Alert 🚨: Our paper is now out in @natureneuro.bsky.social - We show that the firing phase of neurons in human MTL doesn’t reflect the order of events, challenging a long-standing theory of human memory. nature.com/articles/s41593-025-01893-7

Science Alert 🚨: Our paper is now out in @natureneuro.bsky.social - We show that the firing phase of neurons in human MTL doesn’t reflect the order of events, challenging a long-standing theory of human memory. nature.com/articles/s41593-025-01893-7

Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks - Nature Neuroscience

The temporal order of events in working memory is thought to be reflected by ordered neuronal firing at different phases. Here the authors show that this is not the case and that phase order is linked...

nature.com

Together with @dendritesgr.bsky.social, we’ll be hosting a tutorial on constructing and optimizing biophysical models (via Jaxley & DendroTweaks) 🚀 Join us in Florence if you like dendrites, biophysics, or optimization!

Roman Makarov@roman-makarov.bsky.social · last yr.

We are thrilled to host a tutorial on biophysical modeling with Jaxley & DendroTweaks at #CNS2025! 📍 Florence, July 5 — looking forward to seeing you there! www.cnsorg.org/cns-2025 @cnsorg.bsky.social

1) Some exciting science in turbulent times: How do mice distinguish self-generated vs. object-generated looming stimuli? Our new study combines VR and neural recordings from superior colliculus (SC) 🧠🐭 to explore this question. Check out our preprint doi.org/10.1101/2024... 🧵