Lausanne, Switzerland
Joined December 2009
- Tweets92
- Following252
- Followers107
- Likes309
Alexis Arnaudon retweeted
Pleased to share a new preprint of our collaborative effort with Sean Hill’s lab and @BlueBrainPjt led by Dr. Polina Litvak.
biorxiv.org/content/10.1101/…
Alexis Arnaudon retweeted
New paper from our team @imperialcollege @IBMEurope @TheAnnaFischer @r1cc4rd0 @KEMoselund We made a laser with a network topology that is highly sensitive to pump illumination to enable applications in machine vision and signal processing. More here: doi.org/10.1002/lpor.2024006…
New paper out on lasing networks led by @saxedh ! doi.org/10.1002/lpor.2024006…
Alexis Arnaudon retweeted
Saturday morning June 29, Blue Brain scientist @arnaudon is presenting their poster at #FENS2024 - Controlling morpho-electrophysiological variability of neurons with detailed biophysical models - Come for a chat!
More info: fensforum.org/
Alexis Arnaudon retweeted
Dysco: a unified and ultrafast dynamical functional connectivity spectral analysis framework (shorturl.at/UoSGO)
Led by @GAlteriis and @Olitob123 with @Joana_Cabral__ @leechbrain and the one and onlyFet Turkheimer
The toolboxes (Matlab & Python): github.com/Mimbero/DySCo
Alexis Arnaudon retweeted
Finally out in Chaos, congrats @MarcoNurisso !
doi.org/10.1063/5.0169388
📢New preprint:
A unified framework for Simplicial Kuramoto models
Led by @MarcoNurisso - come for the figures, stay for the science.
Here, oscillators are defined on triangles and so on, not just on nodes. That's different from models I'm used to, but they can be related ➡️
All you ever wanted to know about the Kuramoto model ! pubs.aip.org/aip/cha/article…
Just accepted paper on ACM TOMS on a little code for multiscale clustering! dl.acm.org/doi/10.1145/36512… (the code is here: github.com/barahona-research…)
I am co-editing this special issue on Higher-order brain networks, submission of all kinds are very welcome! iopscience.iop.org/collectio… please pm and RT if interested!
Alexis Arnaudon retweeted
📢New Publication Alert! Our paper "Cortical cell assemblies and their underlying connectivity: An in silico study" is out in @PLOSCompBiol.
We plunge into the world of cortical connectivity to understand the formation of functional cell assemblies.
👉doi.org/10.1371/journal.pcbi…
Alexis Arnaudon retweeted
I've also compiled all open computational neuroscience resources I can find on the web: github.com/asoplata/open-com… Contributions welcome!
Alexis Arnaudon retweeted
We are #hiring for ML researcher/computational neuroscientist at BSc/MSc level at Medical University of Vienna. Message me if you're interested in joining our team. We are attending COSYNE 2024 if you would like to meet! #cosyne2024 #cosyne24
New code released! github.com/BlueBrain/BluePyP… for anyone needing a generalisation of Pandas `.apply` to run massively in parallel on large HPC systems (or just on a small laptop ofc).
Alexis Arnaudon retweeted
Our cover in Cell Patterns @Patterns_CP today pretty much says it all about our recent work: we've been busy building and testing detailed single cell models for various neuronal types (1/2)
Alexis Arnaudon retweeted
📢 Embracing variability: Nature's safeguard against chaos! Our latest study pioneers detailed #biophysical #neuron models, unraveling the intricate interplay between shape and function via #MCMC parameter sampling.
#simulation #neuroscience
👉iScience - doi.org/10.1016/j.isci.2023.…
The paper on MCMC sampling of electrical models of cortical neurons is out in iScience! cell.com/iscience/fulltext/S…
Alexis Arnaudon retweeted
Just out! Groundbreaking universal workflow to build robust #biophysical models of any neuronal type. SSCx neuronal models demonstrated. Based on #opensource tools & all steps freely available.
#neuroscience #electrophysiology
Read the paper in Patterns - doi.org/10.1016/j.patter.202…
Electrical model paper is out in Cell Pattern! (led by @maria__reva!) cell.com/patterns/fulltext/S…
Alexis Arnaudon retweeted
Our *new preprint* shows how to define Gaussian processes for data over unknown manifolds. You will find our method useful if you study neural manifolds but only have sparse single-cell data or with low-res neuroimaging data (eg EEG), but you want high-quality vector fields.
You need to model vector fields over a manifold but you don’t really know said underlying manifold ? Well, fear not for we’ve got your back with our latest paper led by the fearless @AGosztolai arxiv.org/abs/2309.16746
