Matthijs Pals
@matthijspals.bsky.social
📤 924
📥 615
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Using deep learning to study neural dynamics
@mackelab.bsky.social
pinned post!
Seems like it's a good time to jump ship! I am working in
@mackelab.bsky.social
, on using deep learning (usually recurrent neural networks; RNNs) to study neural dynamics. A cool example is recent work where we show how to generate long sequences of realistic neural data:
arxiv.org/abs/2406.16749
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Inferring stochastic low-rank recurrent neural networks from neural data
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpre...
https://arxiv.org/abs/2406.16749
about 1 year ago
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Matthijs Pals
Guillaume Bellec
20 days ago
Our paper on data constrained RNN that generalize to optogenetic perturbations now citable on eLife:
doi.org/10.7554/eLif...
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Richard Gao
about 1 month ago
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 🏖️🌮
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Machine Learning in Science
about 1 month ago
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/
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Jobs - mackelab
The MackeLab is a research group at the Excellence Cluster Machine Learning at Tübingen University!
https://www.mackelab.org/jobs/
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Matthijs Pals
Machine Learning in Science
about 1 month ago
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
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Michael Deistler
about 2 months ago
I am super happy to share that our project on training biophysical models with Jaxley is now published in Nature Methods:
www.nature.com/articles/s41...
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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 ...
https://www.nature.com/articles/s41592-025-02895-w
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Matthijs Pals
Machine Learning in Science
about 2 months ago
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...
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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 ...
https://www.nature.com/articles/s41592-025-02895-w
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Matthijs Pals
Machine Learning in Science
3 months ago
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
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Richard Gao
4 months ago
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 🤗
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Matthijs Pals
DurstewitzLab
4 months ago
Our
#AI
#DynamicalSystems
#FoundationModel
DynaMix was accepted to
#NeurIPS2025
with outstanding reviews (6555) – first model which can *zero-shot*, w/o any fine-tuning, forecast the *long-term statistics* of time series provided a context. Test it on
#HuggingFace
:
huggingface.co/spaces/Durst...
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Matthijs Pals
sbi - Simulation-based inference
4 months ago
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 🧵
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Matthijs Pals
DurstewitzLab
6 months ago
Got prov. approval for 2 major grants in Neuro-AI & Dynamical Systems Reconstruction, on learning & inference in non-stationary environments, out-of-domain generalization, and DS foundation models. To all AI/math/DS enthusiasts: Expect job announcements (PhD/PostDoc) soon! Feel free to get in touch.
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Elkan Akyurek
6 months ago
Jelmer Borst and I are looking for a PhD candidate to build an EEG-based model of human working memory! This is a really cool project that I've wanted to kick off for a while, and I can't wait to see it happen. Please share and I'm happy to answer any Qs about the project!
www.rug.nl/about-ug/wor...
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Vacatures bij de RUG
https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-02S000BEAP&cat=wp
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Matthijs Pals
The Transmitter
6 months ago
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...
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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.
https://www.thetransmitter.org/null-and-noteworthy/null-and-noteworthy-neurons-tracking-sequences-dont-fire-in-order/?utm_source=bluesky&utm_medium=org-social&utm_campaign=20250630-news-null-noteworthy-neurons-tracking-sequences
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Matthijs Pals
DurstewitzLab
6 months ago
How do animals learn new rules? By systematically testing diff. behavioral strategies, guided by selective attn. to rule-relevant cues:
rdcu.be/etlRV
Akin to in-context learning in AI, strategy selection depends on the animals' "training set" (prior experience), with similar repr. in rats & humans.
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Abstract rule learning promotes cognitive flexibility in complex environments across species
Nature Communications - Whether neurocomputational mechanisms that speed up human learning in changing environments also exist in other species remains unclear. Here, the authors show that both...
https://rdcu.be/etlRV
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Matthijs Pals
Tatiana Engel
7 months ago
Out today in
@nature.com
: we show that individual neurons have diverse tuning to a decision variable computed by the entire population, revealing a unifying geometric principle for the encoding of sensory and dynamic cognitive variables.
www.nature.com/articles/s41...
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Valentin Schmutz
7 months ago
Our new preprint 👀
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Tim Vogels
7 months ago
We just pushed “Memory by a 1000 rules” onto bioRxiv, where we use clever
#ML
to find
#plasticity
quadruplets (EE, EI, IE, II) that learn basic stability in spiking nets. Why is it cool? We find 1000s!! of solutions, and they don’t just stabilise. They
#memorise
!
www.biorxiv.org/content/10.1...
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Memory by a thousand rules: Automated discovery of functional multi-type plasticity rules reveals variety & degeneracy at the heart of learning
Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of str...
https://www.biorxiv.org/content/10.1101/2025.05.28.656584v1
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sbi - Simulation-based inference
8 months ago
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. 🥁 🎉
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Joao Barbosa
8 months ago
Please RT🙏 Reach out if you want to help understand cognition by modelling, analyzing and/or collect large scale intracortical data from 👩🐒🐁 We're a friendly, diverse group (n>25) w/ this terrace 😎 in the center of Paris! See👇 for + info about the lab We have funding to support your application!
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Matthijs Pals
Machine Learning in Science
8 months ago
🎓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
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Jobs - mackelab
The MackeLab is a research group at the Excellence Cluster Machine Learning at Tübingen University!
https://www.mackelab.org/jobs/
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Matthijs Pals
Shervin Safavi
11 months ago
Re-posting is appreciated: We have a fully funded PhD position in CMC lab
@cmc-lab.bsky.social
(at @tudresden_de). You can use
forms.gle/qiAv5NZ871kv...
to send your application and find more information. Deadline is April 30. Find more about CMC lab:
cmclab.org
and email me if you have questions.
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https://forms.gle/qiAv5NZ871kvQ1u96
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Matthijs Pals
Machine Learning in Science
9 months ago
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
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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...
https://arxiv.org/abs/2411.02728
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Amin Nejatbakhsh
9 months ago
Excited to announce that our paper on "Comparing noisy neural population dynamics using optimal transport distances" has been selected for an oral presentation in
#ICLR2025
(1.8% top papers). Check the thread for paper details (0/n). Presentation info:
iclr.cc/virtual/2025...
.
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ICLR 2025 Comparing noisy neural population dynamics using optimal transport distances OralICLR 2025
https://iclr.cc/virtual/2025/oral/31814
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Happening tomorrow morning :).
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9 months ago
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Matthijs Pals
Machine Learning in Science
10 months ago
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 🎉)!
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Matthijs Pals
Jakob Macke
10 months ago
Out now 'in print'--- a true labor of love, in more ways than one. See the paper and press-release below! Also, go and see
@matthijspals.bsky.social
's talk at
#Cosyne
, where he will talk about related/follow up work!
uni-tuebingen.de/en/research/...
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Our study on sequence working memory using human spiking data and RNNs, is finally published :). Check it out! 👇
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10 months ago
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T. Anderson Keller
10 months ago
In the physical world, almost all information is transmitted through traveling waves -- why should it be any different in your neural network? Super excited to share recent work with the brilliant
@mozesjacobs.bsky.social
: "Traveling Waves Integrate Spatial Information Through Time" 1/14
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Michael Deistler
10 months ago
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!
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Auguste Schulz
11 months ago
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...
🧵
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Melanie Tschiersch
12 months ago
🚨Excited to share my first
@biorxivpreprint.bsky.social
🚨 with the amazing Smith lab,
@jbarbosa.org
and Albert Compte who made this work possible. We show that 🐒prefrontal hemispheres combine redundancy (for precision) & weak connections (for capacity) for supporting spatial working memory (WM). 1/🧵
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Guillaume Bellec
12 months ago
Pre-print 🧠🧪 Is mechanism modeling dead in the AI era? ML models trained to predict neural activity fail to generalize to unseen opto perturbations. But mechanism modeling can solve that. We say "perturbation testing" is the right way to evaluate mechanisms in data-constrained models 1/8
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Matthijs Pals
Machine Learning in Science
about 1 year ago
Talk to
@vetterj.bsky.social
and
@gmoss13.bsky.social
about sourcerer at
#Neurips2024
today! 📍Poster #4006 (East; 11 am PT)
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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
about 1 year ago
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Matthijs Pals
sbi - Simulation-based inference
about 1 year ago
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
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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...
http://arxiv.org/abs/2411.17337
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A Erdem Sagtekin
about 1 year ago
This list likely reflects mainly my interests and circle, and I’m sure I’ve missed many people, but I gave it a try: (I’ll be slowly editing it until it reaches 150/150)
go.bsky.app/7VFUkdn
(also, I tried but couldn't remove my profile...)
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Seems like it's a good time to jump ship! I am working in
@mackelab.bsky.social
, on using deep learning (usually recurrent neural networks; RNNs) to study neural dynamics. A cool example is recent work where we show how to generate long sequences of realistic neural data:
arxiv.org/abs/2406.16749
loading . . .
Inferring stochastic low-rank recurrent neural networks from neural data
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpre...
https://arxiv.org/abs/2406.16749
about 1 year ago
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