📍 @ml4science.bsky.social, Tübingen, Germany
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! 👏🎓
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 🧵
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! ⚡️
By @ldattaro.bsky.social
#neuroskyence
www.thetransmitter.org/null-and-not...
By @ldattaro.bsky.social
#neuroskyence
www.thetransmitter.org/null-and-not...
www.cambridge.org/core/journal...
www.cambridge.org/core/journal...
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
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
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...
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 🎉)!
nature.com/articles/s41593-025-01893-7
nature.com/articles/s41593-025-01893-7
nature.com/articles/s41593-025-01893-7
Join us in Florence if you like dendrites, biophysics, or optimization!
Join us in Florence if you like dendrites, biophysics, or optimization!
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... 🧵
📍Poster #4006 (East; 11 am PT)
Paper: openreview.net/forum?id=0cg...
Code: github.com/mackelab/sou...
(1/8)
📍Poster #4006 (East; 11 am PT)
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:
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
PhD students: Apply by Nov 15 (tomorrow!), directly to IMPRS-IS or ELLIS
PhD students: Apply by Nov 15 (tomorrow!), directly to IMPRS-IS or ELLIS
For now let’s introduce ourselves with some pictures of our recent group retreat.
For now let’s introduce ourselves with some pictures of our recent group retreat.