Machine Learning in Science
@mackelab.bsky.social
We build probabilistic #MachineLearning and #AI Tools for scientific discovery, especially in Neuroscience. Probably not posted by @jakhmack.bsky.social.
📍 @ml4science.bsky.social, Tübingen, Germany
📍 @ml4science.bsky.social, Tübingen, Germany
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
December 1, 2025 at 4:16 PM
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
Reposted by Machine Learning in Science
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)
Paper: openreview.net/forum?id=yB5...
Code: github.com/mackelab/fnope
(1/9)
FNOPE: Simulation-based inference on function spaces with Fourier...
Simulation-based inference (SBI) is an established approach for performing Bayesian inference on scientific simulators. SBI so far works best on low-dimensional parametric models. However, it is...
openreview.net
December 1, 2025 at 8:34 AM
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)
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/
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/
Jobs - mackelab
The MackeLab is a research group at the Excellence Cluster Machine Learning at Tübingen University!
www.mackelab.org
November 28, 2025 at 1:54 PM
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/
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
November 28, 2025 at 10:26 AM
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
📄 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
November 21, 2025 at 3:08 PM
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
📄 Paper: arxiv.org/abs/2508.12939
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...
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 ...
www.nature.com
November 13, 2025 at 1:08 PM
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...
Paper: www.nature.com/articles/s41...
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! 👏🎓
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! 👏🎓
October 2, 2025 at 11:28 AM
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! 👏🎓
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
September 30, 2025 at 2:06 PM
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
Reposted by Machine Learning in Science
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 🧵
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 🧵
September 9, 2025 at 3:00 PM
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 🧵
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! ⚡️
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! ⚡️
July 23, 2025 at 2:28 PM
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! ⚡️
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! ⚡️
Reposted by Machine Learning in Science
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...
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.
www.thetransmitter.org
June 30, 2025 at 4:08 PM
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...
By @ldattaro.bsky.social
#neuroskyence
www.thetransmitter.org/null-and-not...
Many people in our lab use Scholar Inbox regularly -- highly recommended!
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...
Scholar Inbox: Daily Research Recommendations just for You
Science is moving fast. How can we keep up? Scholar Inbox helps researchers stay ahead by making the discovery of open access papers more personal.
www.machinelearningforscience.de
July 2, 2025 at 7:05 AM
Many people in our lab use Scholar Inbox regularly -- highly recommended!
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...
www.cambridge.org/core/journal...
Simulation-based inference of surface accumulation and basal melt rates of an Antarctic ice shelf from isochronal layers | Journal of Glaciology | Cambridge Core
Simulation-based inference of surface accumulation and basal melt rates of an Antarctic ice shelf from isochronal layers - Volume 71
www.cambridge.org
June 11, 2025 at 11:47 AM
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...
www.cambridge.org/core/journal...
Reposted by Machine Learning in Science
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. 🥁 🎉
May 12, 2025 at 2:29 PM
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
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
Jobs - mackelab
The MackeLab is a research group at the Excellence Cluster Machine Learning at Tübingen University!
www.mackelab.org
April 30, 2025 at 1:43 PM
🎓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
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
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
April 25, 2025 at 8:53 AM
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
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
Reposted by Machine Learning in Science
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...
Also, a good reminder to share that our work is now out in Cell Reports 🙏🎊
⬇️
www.cell.com/cell-reports...
April 17, 2025 at 8:50 PM
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...
Also, a good reminder to share that our work is now out in Cell Reports 🙏🎊
⬇️
www.cell.com/cell-reports...
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 🎉)!
with 3 posters, 2 workshop talks, and a main conference contributed talk (for the very first time in Mackelab history 🎉)!
March 27, 2025 at 2:03 PM
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 🎉)!
with 3 posters, 2 workshop talks, and a main conference contributed talk (for the very first time in Mackelab history 🎉)!
Reposted by Machine Learning in Science
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!
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
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...
www.nature.com
March 24, 2025 at 1:53 PM
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!
Reposted by Machine Learning in Science
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
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...
www.nature.com
March 24, 2025 at 12:55 PM
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
nature.com/articles/s41593-025-01893-7
Reposted by Machine Learning in Science
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!
Join us in Florence if you like dendrites, biophysics, or optimization!
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
February 28, 2025 at 8:08 AM
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!
Join us in Florence if you like dendrites, biophysics, or optimization!
Reposted by Machine Learning in Science
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... 🧵
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... 🧵
February 3, 2025 at 7:19 PM
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... 🧵
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... 🧵
Talk to @vetterj.bsky.social and @gmoss13.bsky.social about sourcerer at #Neurips2024 today!
📍Poster #4006 (East; 11 am PT)
📍Poster #4006 (East; 11 am PT)
@vetterj.bsky.social and I are excited to present our work at #NeurIPS2024! We present Sourcerer: a maximum-entropy, sample-based solution to source distribution estimation.
Paper: openreview.net/forum?id=0cg...
Code: github.com/mackelab/sou...
(1/8)
Paper: openreview.net/forum?id=0cg...
Code: github.com/mackelab/sou...
(1/8)
Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation
Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations - an inference task also known as source distribution estimation....
openreview.net
December 13, 2024 at 4:49 PM
Talk to @vetterj.bsky.social and @gmoss13.bsky.social about sourcerer at #Neurips2024 today!
📍Poster #4006 (East; 11 am PT)
📍Poster #4006 (East; 11 am PT)
Reposted by Machine Learning in Science
1) With our @neuripsconf.bsky.social poster happening tomorrow, it's about time to introduce our Spotlight paper 🔦, co-lead with @jkapoor.bsky.social:
Latent Diffusion for Neural Spiking data (LDNS), a latent variable model (LVM) which addresses 3 goals simultaneously:
Latent Diffusion for Neural Spiking data (LDNS), a latent variable model (LVM) which addresses 3 goals simultaneously:
December 11, 2024 at 7:43 AM
1) With our @neuripsconf.bsky.social poster happening tomorrow, it's about time to introduce our Spotlight paper 🔦, co-lead with @jkapoor.bsky.social:
Latent Diffusion for Neural Spiking data (LDNS), a latent variable model (LVM) which addresses 3 goals simultaneously:
Latent Diffusion for Neural Spiking data (LDNS), a latent variable model (LVM) which addresses 3 goals simultaneously:
Reposted by Machine Learning in Science
How to find all fixed points in piece-wise linear recurrent neural networks (RNNs)?
A short thread 🧵
In RNNs with N units with ReLU(x-b) activations the phase space is partioned in 2^N regions by hyperplanes at x=b 1/7
A short thread 🧵
In RNNs with N units with ReLU(x-b) activations the phase space is partioned in 2^N regions by hyperplanes at x=b 1/7
December 11, 2024 at 1:32 AM
How to find all fixed points in piece-wise linear recurrent neural networks (RNNs)?
A short thread 🧵
In RNNs with N units with ReLU(x-b) activations the phase space is partioned in 2^N regions by hyperplanes at x=b 1/7
A short thread 🧵
In RNNs with N units with ReLU(x-b) activations the phase space is partioned in 2^N regions by hyperplanes at x=b 1/7