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Is a universal brain decoder possible? Can we train a decoding system that easily transfers to new individuals/tasks? Check out our #NeurIPS2023 paper where we show that it’s possible to transfer from a large pretrained model to achieve SOTA 🧠! Link: poyo-brain.github.io 🧵

Super excited to share new work to appear at #NeurIPS2023 led by the incredible Mehdi Azabou! In collaboration with an amazing team: Vinam Arora, 🤖, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake Richards, Matt Perich, Guillaume Lajoie Patrick Mineault Machine Learning at Georgia Tech 🧠🤖

Check out this new paper: Led by Mehdi Azabou and Eva Dyer, we show that it is possible to get SOTA brain decoding with transfer across individuals and tasks! The key is a clever way to tokenize spiking data for transformers. #brain #neurotech #NeurIPS2023

The key to building a universal encoder for spikes? Spike embeddings! Mehdi and co. build a single model that can train on any neural data that mapping spikes to a continuous space, similar to word embeddings. It works 🔥

Neural spiking data and Transformers are a tricky match. Temporal segmentation and tokenization are the crux. Together with an all-star team, we figured out a scalable sol. The results are exciting: training and transferring on multi-sessions, multi-subjects neural decoding tasks

This is incredibly important. We will all be using such methods soon.

How can we extract insights from behavior modulated by multiple complex factors? 🐭🪰🤖🏃 Check out our #NeurIPS2023 Spotlight Paper where we present a SSL method for learning multiscale representations of behavior! Machine Learning at Georgia Tech Google DeepMind Link: multiscale-behavior.github.io 🧵

"Why the simplest explanation isn’t always the best" - commentary with Eva Dyer highlighting how dimensionality reduction does not usually give us what we want. pnas.org/doi/10.1073/pn… "Major Achievement: Dino scatterplot in paper" unlocked.

Have you ever done a dense grid search over neural network hyperparameters? Like a *really dense* grid search? It looks like this (!!). Blueish colors correspond to hyperparameters for which training converges, redish colors to hyperparameters for which training diverges.

Today, I am very proud share what we have been working on for the last 14 months. ✨ Introducing Aya -- a new state-of-art for massively multilingual models. 🔥🎉

Cosyne Workshop Alert! On Tuesday March 5th, Chethan Pandarinath and I are proud to bring you: Understanding Neural Computation using Task-trained and Data-trained Networks. youtu.be/bJ0stLORdgQ

Be a content creator for the Neuromatch NeuroAI course! We're looking for people to write tutorials on transfer learning and RL in PyTorch over the next 3 weeks. If you want to help build this amazing course, DM me for details or fill out this app: airtable.com/app32npl2ZlbJv…

Meet the Georgia Tech experts who are helping unlock the future of #AI. These experts will share their latest research findings in machine learning on the world stage at ICML Conference (July 21-27). Tech experts are part of 40 teams with new research, and the institute is the lead

Nancy Kanwisher @[email protected] KanwisherLab Alex Koulakov Guillaume Lajoie Mackenzie Mathis, PhD Alva Noe Carsen Stringer @[email protected] Andreas Tolias Lab @ Stanford University Daniel Yamins Alex Wiltschko Eva Dyer SueYeon Chung Giacomo Indiveri | @[email protected] Max Bennett Matthias Bethge Eva Dyer Robert Yang deadline extended -- please retweet

Check this out! So excited to share our recent work in building foundation models for graphs!

This might be the secret to breaking through the next plateau in deeper reasoning, planning and retrieval capabilities for AI agents 🤔 LGGMs (large generative graph models) are on the rise! While Adobe and Intel were first at it with LGGMs, researchers at Georgia Tech have

What will a foundation model for the brain look like? We argue that it must be able to solve a diverse set of tasks across multiple brain regions and animals. Check out our preprint where we introduce a multi-region, multi-animal, multi-task model (MtM): arxiv.org/abs/2407.14668

Looking forward to a visit from Eva Dyer on Thursday! Eva is working at the forefront of the intersection between machine learning, neuroscience, and neuroAI 🧠 Come check out her talk and learn more about her work here: dyerlab.gatech.edu

First paper from the lab biorxiv.org/content/10.110…! Long story short: Emine Zeynep Ulutas, Amartya Pradhan, Dorothy Koveal and I developed a method using injectable nanoparticles to turn mice into…constellations in motion.

How can we make progress in developing a general model of neural computation rather than a series of disjointed models tied to specific experimental circumstances, ask Eva Dyer and Blake Richards Blake Richards in the latest entry in our NeuroAI series. thetransmitter.org/neuroai/accept…