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These papers were what inspired me to look into random feature models of ANNs

The mechanics of correlated variability in segregated cortical excitatory subnetworks | PNAS pnas.org/doi/10.1073/pn… Happy to see the work come out! Congrats to Gregory Handy @algomage and Matt Getz!

This article is now open access. This was delayed due to some technicalities at Nature.

The video recordings from many of these talks are now available! These could be great resources for anyone interested in theoretical and computational neuroscience. Thanks again to the organizers for putting together such a great event! indico.ictp.it/event/10481/ot…

Dear friends and colleagues -- My lab has a postdoc opening for an exciting new project. We are looking for someone with top software engineering skills. I'd appreciate it if you could RT and distribute widely. Jib posting below: dropbox.com/scl/fi/r9m2544…

Our work on individual differences in monkeys and RNNs is out on Nat Comms! nature.com/articles/s4146… A huge thanks to Aldo Battista Fabio Stefanini Satoshi Tsujimoto aldo genovesio and Stefano Fusi for the massive work! A brief recap 👇🧵

I'll be leaving ICTP in September to start as Assistant Professor at Donders Institute, Radboud University in Nijmegen. Students interested in pursuing a PhD at the border of Machine Learning, Neuroscience and Statistical Mechanics, don't hesitate to contact me.

Hi folks! If you're joining #CCN2024 in Boston, stop by Jianbo's poster (Aug 9, 11:15am-1:15pm, C85) on why even the 'cleverest' humans struggle with XOR rule learning🤔❓. It's a cute and elegant model🐱. Interested? Talk to Jianbo!

Recruiting postdocs! Please get in touch if you wanna discuss projects on biologically plausible learning through TUM's global postdoctoral fellowship tum.de/en/about-tum/c…

1/ Excited to share new work with online, learning guy, Alex van Meegen, and Ashok Litwin-Kumar! "Connectivity Structure and Dynamics of Nonlinear Recurrent Neural Networks" analyzes how global, spectral structure of connectivity in real-world networks affects collective dynamics.

It’s finally out🔥 Our review formalizes hours of conversations w/ Richard Gao over the past 2 years, starting from our CosyneMeeting workshop, into a new perspective about neural timescales. Thanks to Anna Levina, Jakob Macke and all our wonderful speakers and attendees!

Our new paper in nature shows that entorhinal grid cells adapt their representations to a new environment in one-shot and we derive a model that fuses landmarks & motion to *predict* the detailed grid rep *before* the mouse enters the new environment! nature.com/articles/s4158…

New papers stress neural similarity scores may not always tell us what we think. This does MORE, by giving the community new pipeline & tools to see what current or future metrics focus on- & whether it's relevant for your neuro research: does it reflect the task, activity PCs...

I highly recommend that one! Beautiful modeling work combined with excellent, large-scale data. It answers questions about when , how and why the brain can leverage expectations about future perturbations for motor control.

Fully neural mechanisms for structured generalization and rapid learning identified using a meta-learning (“learning-to-learn”) approach to model relational learning (e.g. if A > B and B > C, then A > C) Thomas Miconi and Kenneth Kay nature.com/articles/s4159…

📢📢Announcing this year's conference on the Mathematics of Neuroscience & AI (Split, Croatia, 27-30th May): neuromonster.org Abstract submissions and early bird registration are open. The first 100 tickets can get an extra 20% off using discount code SUPEREARLYNM25.

🚀 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

In one sentence: We studied how multiple neural regions with random connectivity interact through low-dimensional communication subspaces. I wanted to thank the reviewers as well for their thoughtful comments. Please reach out if you have any thoughts on the work!

Really happy to see this paper out, led by Nishil Patel in collaboration with Stefano Sarao Mannelli and Andrew Saxe: we apply the statistical physics toolbox to analyse a simple model of reinforcement learning, and find some cool effects, like a speed-accuracy trade-off for generalisation 🚀