Grateful to many. @ml4science.bsky.social built an amazing environment for this research. Taking some of these open challenges with me now as I start my next chapter at the Allen Institute and UW in Seattle! ⛰️👨💻🐋🔬
Computational sciences can offer a unique opportunity for democratized access and participation, but we are currently falling far short of that goal, writes @neuromatch.bsky.social's @meganakpeters.bsky.social and Bradley Roberts.
#neuroskyence
www.thetransmitter.org/education/ho...
Janne Lappalainen
The Transmitter
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
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...
Friday, 13:15 (Poster 2-048) at @cosynemeeting.bsky.social #cosyne2026: @allierced.bsky.social and @lappalainenjk.bsky.social present how they made GNNs learn interpretable circuit models from neural activity. Code: saalfeldlab.github.io/flyvis-gnn/
🚀 Applications are OPEN for the CAJAL NeuroAI course!
Learn how AI & deep learning help us model brain activity & behavior. Work with experts, get hands-on training & join a global network!
📅 Apply by March 7
🔗 loom.ly/xg_uRKE
#NeuroAI #DeepLearning #Neuroscience
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 ...
My dissertation is just out today:
hdl.handle.net/10900/176447
Timely for the current discussion. It covers predicting neural function underlying behavior with connectomes & ML, why single models aren't enough, and remaining challenges for whole-brain simulation (e.g. Chapter 4.1.1).
🪰 A new ‘Eyemap’ developed by a team led by Arthur Zhao & @michaelreiser.bsky.social reveals how visual information detected by the fly’s eye shows up in neurons deep in the brain. Remarkably, the eye’s shape determines how flies see motion.👁️
🔗 hhmi.news/4kSZjou
