Machine Learning in Science

Friday, 13:15 (Poster 2-116): Maren Eberle presents “Objective functions and task complexity in connectome-constrained Spiking Neural Networks” (work in the NYU Neuroinformatics lab)

Friday, 13:15 (Poster 2-040): @lulmer.bsky.social presents “Neural activity constraints improve task-optimized connectome-constrained models” (joint work with @srinituraga.bsky.social).

@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 👇

Auguste developed ML methods for linking neural activity and behavior, ranging from classic discriminative models to deep generative models such as VAEs and DDPMs e.g. doi.org/10.52202/079... or doi.org/10.1016/j.ce....

Janne developed deep mechanistic networks to study when and how detailed measurements of brain wiring (connectomes) can enable accurate, neuron-level predictions of neural dynamics across the brain (www.nature.com/articles/s41...).

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!

Guy Moss (@gmoss13.bsky.social) developed and applied simulation-based inference methods to solve inference problems in glaciology, in collaboration with @geophys-tuebingen.bsky.social. E.g., openreview.net/forum?id=yB5... 3/3

Julius Vetter (@vetterj.bsky.social) worked on deep generative modeling and simulation-based Bayesian inference, with applications to (physiological) time series data. E.g., openreview.net/forum?id=kN0... 2/3

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

AutoSBI Poster: Tuesday 2 Dec 10:30am at the Amortized ProbML workshop, Copenhagen 11/11

Fifth, we bring AutoML to SBI pipelines with a practical performance metric that does not require ground-truth posteriors, improving inference quality on the SBI benchmark! By @swagatam.bsky.social, @gmoss13.bsky.social, @keggensperger.bsky.social, @jakhmack.bsky.social 10/11

Kalman filtering meets Jaxley poster #2015: Thu 4 Dec 11:00am at San Diego ➡️ openreview.net/forum?id=1si... 9/11

Fourth, in collaboration with Ian C Tanoh and Scott Linderman, we used the Jaxley toolbox and extended Kalman filters to estimate the marginal log-likelihood of a biophysical neuron model. We showed that this enables identifying biophysical parameters given extracellular recordings. 8/11

Retina model with Jaxley poster #2015: Friday 5 Dec 11:00am at San Diego ➡️ openreview.net/forum?id=ayj... 7/11

Third, in collaboration with @kyrakadhim.bsky.social, @philipp.hertie.ai, and others, we built a task- and data-constrained biophysical network of the outer plexiform layer of the mouse retina. To optimize this model, we built it on top of our Jaxley toolbox for differentiable simulation. 6/11

NPE-PFN poster #509: Thursday 4 Dec 11:00am at San Diego and Thursday 4 Dec 10:30am at Copenhagen ➡️ openreview.net/forum?id=kN0... 5/11

Second, come by to check out NPE-PFN: We leverage the power of tabular foundation models for training-free and simulation-efficient SBI. SBI has never been so effortless! By @vetterj.bsky.social, Manuel Gloeckler, @danielged.bsky.social, @jakhmack.bsky.social 4/11

FNOPE poster #601: Friday 5 Dec 4:30pm at San Diego and Thursday 4 Dec 10:30am at Copenhagen ➡️ openreview.net/forum?id=yB5... 3/11

First, we introduce FNOPE, a new simulation-based inference approach for efficiently and flexibly inferring function-valued parameters. By @gmoss13.bsky.social, @leahsmuhle.bsky.social, Reinhard Drews, @jakhmack.bsky.social and @coschroeder.bsky.social 2/11

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/

Check out our website for the whole team: www.mackelab.org/people/ 7/7

Maren joined the lab as PhD student in October to work on connectome-constrained models of neural activity and behavior in the fruit fly. She holds an MSc Computational Neuroscience from the BCCN Berlin. 6/7

Byoungsoo (@byoungsookim.bsky.social) joined as a research scientist in October, upon finishing his Master's in Computational Neuroscience in the lab. He is working on modeling optomotor response circuits with a 3D compound eye model of the fruit fly. 5/7

Isaac joined as a master’s thesis student working on representation learning for connectome-constrained models. Now, as a PhD student since July, he’s applying this to models of the fruitfly. He previously did an MSc at AIMS South Africa. 4/7

Nicolas previously worked on computational neuroscience and NLP projects at EPFL. He joined the lab in June as a PhD student and is interested in building foundational models for neurophysiology data and applying LLMs for scientific discovery. 3/7

Stefan (@stewah.bsky.social) joined the lab as a PhD student in June. He completed his Bachelor’s and Master’s degrees in Physics at Heidelberg University. He works on using LLMs to discover scientific models. 2/7

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

Nicolas has previously worked on computational neuroscience and NLP projects at EPFL. He joined the lab in June as a PhD student and is currently interested in building foundational models for neurological data and applying LLMs for scientific discovery. 3/7