@ppjgoncalves
Computational Neuroscience and Machine Learning. Group Leader at VIB.AI (vib.ai/en) and Associate Professor at KU Leuven (www.kuleuven.be/english/kuleuven). Affiliated with VIB-KUL CN (vibneuroscienceleuven.be). More details at goncalveslab.sites.vib.be
🚀 Proudly introducing the VIB-KU Leuven Center For Neuroscience, a merger of the two former VIB research centers VIB-KU Leuven Center for Brain & Disease Research and Neuro-Electronics Research Flanders (NERF)! Our new motto: Bold Science, Real Impact.
www.youtube.com/watch?v=uhaq...
This is the happy face of four researchers embarking on a cool scientific collaboration backed by 7-years of structural financing!
Congrats @steinaerts.bsky.social, @joanampereira.bsky.social, @ppjgoncalves.bsky.social, and Maarten De Vos on your Methusalem grant.
https://tinyurl.com/nvcardzy
The work this article is about has recently been published in @natmethods.nature.com ! It was led by @deismic.bsky.social and was a collaboration between the labs of @jakhmack.bsky.social / @mackelab.bsky.social, @philipp.hertie.ai and @ppjgoncalves.bsky.social.
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
New work out today by a collaborative team including @ppjgoncalves.bsky.social! 🙌
Introducing #JAXLEY, a differentiable simulator that brings biophysically detailed neural models into the realm of large‑scale, machine‑learning‑powered optimization.
https://doi.org/10.1038/s41592-025-02895-w
Super excited about this work led by the one and only @deismic.bsky.social!
“Mapping ion channel function” doi.org/10.7554/eLif... isn’t exactly a citation slayer, but it’s still one of my favourites (& my first independent project). Today we push pt 2, where we trace code origin & unite almost all channel models in a common expression. Boom! www.biorxiv.org/content/10.1...
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 🧵
Super exciting work led by @bconfavreux.bsky.social and @tpvogels.bsky.social!
The @ppjgoncalves.bsky.social lab is recruiting a PhD student via the VIB.AI International PhD Call 2025!
Join to develop machine learning tools for neuroscience, focusing on simulation-based inference & neural circuit modeling.
📍 Leuven
🗓️ Apply by June 22 → https://tinyurl.com/53nc9vnd
Fantastic PhD opportunities at the intersection of machine learning and biology. Come join us at @vibai.bsky.social! More details at: vib.ai/en/vibai-int...
Fantastic opportunity to establish your lab in ML for biology! Come join us!
Exciting project in Parkinson's disease! Patrik Verstreken's lab (verstrekenlab.sites.vib.be) and my lab are looking for a postdoc to develop and apply ML methods to the analysis of exciting brain-on-chip data. Please spread the word!
More details at: jobs.vib.be/j/110908/pos...
Hayden, Karthik & Nastya from the @ppjgoncalves.bsky.social lab joined the #SBI Hackathon in Tübingen in March, contributing to the open-source SBI package. Nastya gave a lightning talk on multifidelity inference for computationally expensive simulators. A great week of science & collaboration!
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...
We apply our masked VAE approach to drosophila 🪰walking behavior, a macaque 🐒reach task & a synthetic dataset with access to the ground truth.
We propose calibration checks to evaluate the models’ uncertainty estimates, to avoid making confidently wrong predictions.
Check out the paper for more 🙂
It's been a blast, thanks to @sbi-devs.bsky.social ! This week's hackathon was phenomenal! 🙏 😍 The sbi hackathon welcomed about 25 people in Tübingen with contributions spanning the globe , e.g. 🇺🇸🇯🇵🇧🇪🇩🇪. Wanna see, what we did? Check out the PRs👇
github.com/sbi-dev/sbi/...
New work co-led by @ppjgoncalves.bsky.social introduces a powerful new approach to model conditional distributions of neural and behavioral data using masked variational autoencoders.
Congratulations to @auschulz.bsky.social et al.
https://www.cell.com/cell-reports/fulltext/S2211-1247(25)00109-3
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... 🧵
🎉 Just in time for the end of the year, we’ve released a new version of sbi!
📦 v0.23.3 comes packed with exciting features, bug fixes, and docs updates to make sbi smoother and more robust. Check it out! 👇
🔗 Full changelog: github.com/sbi-dev/sbi/...
Cutting it a bit fine, but here’s my review of the year in neuroscience for 2024
The eighth of these, would you believe? We’ve got dark neurons, tiny monkeys, the most complete brain wiring diagram ever constructed, and much more…
Published on The Spike
Enjoy!
medium.com/the-spike/20...
1/ Excuse me, can I interest you in eliciting your beliefs as flexible probability distributions? No worries, we only need pairwise comparisons or rankings, no personal details.
Led by **Petrus Mikkola** and joint with **Arto Klami**, to be presented soon at @neuripsconf.bsky.social #NeurIPS2024
We are launching an SBI Discord Server! 🎉
We want to use this server to further build a community around SBI, i.e., for sharing insights, questions and events around simulation-based inference in general and the sbi package in particular.
You are all invited to join! 🤗 github.com/sbi-dev/sbi/...
Very privileged to be part of such an inspiring and fun community (effort)!
Thrilled to contribute to this work led by David Frazier providing theory for NPE/NLE in simulation-based inference. These methods are known to match the accuracy of ABC and BSL with fewer simulations, this paper rigorously shows why this can be achieved.
arxiv.org/abs/2411.12068
Hello, world! We are a community-developed toolkit that performs Bayesian inference for simulators. We support a broad range of methods (NPE, NLE, NRE, amortized and sequential), neural network architectures (flows, diffusion models), samplers, and diagnostics. Join us!
First post!
New preprint led by @josepemanzano.bsky.social on amortised simulation-based inference #SBI to map orientation #uncertainty and perform brain #tractography. Great collaboration with @jakhmack.bsky.social, @ppjgoncalves.bsky.social, @deismic.bsky.social.
