Linda Ulmer

Looking forward to presenting our work on connectome-constrained modeling at #cosyne2026

Finally got the job ad—looking for 2 PhD students to start spring next year:

www.gao-unit.com/join-us/

If comp neuro, ML, and AI4Neuro is your thing, or you just nerd out over brain recordings, apply!

I'm at neurips. DM me here / on the conference app or email if you want to meet 🏖️🌮

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/

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 ...

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...

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

a man wearing a white shirt and tie smiles in front of a window ALT: a man wearing a white shirt and tie smiles in front of a window

I've been waiting some years to make this joke and now it’s real:

I conned somebody into giving me a faculty job!

I’m starting as a W1 Tenure-Track Professor at Goethe University Frankfurt in a week (lol), in the Faculty of CS and Math

and I'm recruiting PhD students 🤗

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 🧵

A wide shot of approximately 30 individuals standing in a line, posing for a group photograph outdoors. The background shows a clear blue sky, trees, and a distant cityscape or hills.

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

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... 🧵

Screenshot of the docs at turagalab.github.io/flyvis

Ever wanted to do deep learning with a neural net that is one-to-one mapped to 65.05% of the fruit fly brain? 😄

Before this year ends, I will quickly advertise our code release of `flyvis.`

Docs: t.ly/YqWzR
Repo: t.ly/pMWpp

Work with @jakhmack.bsky.social, @srinituraga.bsky.social and colleagues

How a tiny animal helps us improve brain simulations with AI Can we build neural networks whose structure and computational abilities match a real brain? We are not quite there yet, but our new paper shows a strategy for getting closer to this goal.

Can we build neural networks whose structure and computational abilities match a real brain? We are not quite there yet, but recent work by @lappalainenjk.bsky.social et al. shows a strategy for getting closer to this goal. Read more on our blog: www.machinelearningforscience.de/en/improving...

sbi reloaded: a toolkit for simulation-based inference workflows Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challeng...

The sbi package is growing into a community project 🌍 To reflect this and the many algorithms, neural nets, and diagnostics that have been added since its initial release, we have written a new software paper 📝 Check it out, and reach out if you want to get involved: arxiv.org/abs/2411.17337