Janne Lappalainen

🪰 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

How to build a truly global computational neuroscience community Computational sciences offer an opportunity to increase global access to, and participation in, neuroscience. Neuromatch’s inclusive, scalable model for community building shows how to realize this…

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...

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

🚀 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

How can we use machine learning to find subtle patterns in our data and have them visualized?

Read the thread below to find out how!

Or head to @adjavon.bsky.social's blog post and let Simon the duck walk you through it (like a duck of course): tinyurl.com/yy7csdcd

www.biorxiv.org/content/10.1...

GitHub - TuragaLab/flyvis: A connectome-constrained deep mechanistic network (DMN) model of the fruit fly visual system in PyTorch. A connectome-constrained deep mechanistic network (DMN) model of the fruit fly visual system in PyTorch. - TuragaLab/flyvis

Feel free to reach out if you are interested or encounter any issues, and raise issues directly at github.com/turagalab/fl...

Thanks to everyone who provided feedback and contributed. I diagnosed, revised, adapted, expanded, and documented our code for many hours (too many if you'd ask me 😅) to ensure things can be reproduced nicely within known variations.

Or install the package with `pip install flyvis` to develop custom models and go from here:
t.ly/oj396

Quickstart with running the demos in Google Collab (which are just two clicks away). They explain the main approach and results of our paper:
t.ly/rR3_Z

Connectome-constrained networks predict neural activity across the fly visual system - Nature A study demonstrates how experimental measurements of only the connectivity of a biological neural network can be used to predict neural responses across the fly visual system at single-neuron resolut...

Paper: www.nature.com/articles/s41...

Screenshot of the docs at turagalab.github.io/flyvis

Ever wanted to do deep learning with a neural net that is one-to-one mapped to 65.05% of the fruit fly brain? 😄

Before this year ends, I will quickly advertise our code release of `flyvis.`

Docs: t.ly/YqWzR
Repo: t.ly/pMWpp

Work with @jakhmack.bsky.social, @srinituraga.bsky.social and colleagues

Thinking about this a lot as a (pre-tenure) PI. There just isn't enough time to go deeply into an idea, which is obviously bad for (my) science.

Thanks for the support for this blog post @ml4science.bsky.social, @jakhmack.bsky.social.

Why study the fruit fly brain? Tech giants spend billions on gigantic neural networks, but there’s a fruit fly with only 130_000 neurons that can do so much more and runs on less power than a quartz watch.