Jonathan McCart

Machine Learning PhD Student @GeorgiaTech
Systems Neural Engineering Lab (PI: @chethan) (

[Jonathan McCart @JonathanDMcCart]

Modern neuroscientists routinely record the complex, goal-oriented, and time-varying activity of thousands of neurons. Can we find representations of neural activity that 1) are human-interpretable and 2) enable the generation of neural activity for unrecorded behavioral conditions? We present our recent work on Generating Neural Observations Conditioned on Codes with High Information (GNOCCHI) 🥔🍝 !!

By leveraging unsupervised, information-based diffusion models, GNOCCHI can discover interpretable latent spaces from neural data and generate high-quality neural activity for specific conditions outside of the set of available neural recordings!

[Jonathan McCart @JonathanDMcCart]

@chethan @SergeyStavisky @andpru Wow, I am honored!

[Jonathan McCart @JonathanDMcCart]

For more information, including additional comparisons to LFADS, check out the pre-print at the link below! Major thanks to all the co-authors who helped make this happen! @arsedle, @chris_versteeg, @DomenickMifsud, Mattia Rigotti-Thompson and @chethan !

https://t.co/IdmzvwzQ8t

[Jonathan McCart @JonathanDMcCart]

Modern neuroscientists routinely record the complex, goal-oriented, and time-varying activity of thousands of neurons. Can we find representations of neural activity that 1) are human-interpretable and 2) enable the generation of neural activity for unrecorded behavioral conditions? We present our recent work on Generating Neural Observations Conditioned on Codes with High Information (GNOCCHI) 🥔🍝 !!

By leveraging unsupervised, information-based diffusion models, GNOCCHI can discover interpretable latent spaces from neural data and generate high-quality neural activity for specific conditions outside of the set of available neural recordings!

[Jonathan McCart @JonathanDMcCart]

On biological neural data, GNOCCHI-inferred codes can predict target position for held-out trials better than LFADS, suggesting that the representations learned by GNOCCHI can generalize to unseen conditions better than previous models!

[Chris Versteeg @chris_versteeg]

Cosyne Workshop Alert! On Tuesday March 5th, @chethan and I are proud to bring you:
Understanding Neural Computation using Task-trained and Data-trained Networks.
https://t.co/k0wmgwz1C3

[Chethan Pandarinath @chethan]

Are you a postbac interested in neural engineering / BCIs? 🧠🤖🗣️

Come join our team!! Work directly with our amazing BCI participants.

Great exposure for prospective grad/med school applicants! https://t.co/nBh3af0O4Y

[Chris Versteeg @chris_versteeg]

Stop by NeurReps 2023 tomorrow morning to hear about how ODIN can help you accurately infer neural latent dynamics from neural recordings! Hint: combine low-dimensional dynamical models with injective, nonlinear readouts!

[Jonathan McCart @JonathanDMcCart]

If you are interested in estimating neural dynamics from population recordings, come to our poster Tuesday afternoon to hear more about how injectivity can improve the interpretability of your models! #SfN23
PSTR445.20 / XX40

[Chris Versteeg @chris_versteeg]

Ever wondered whether the dynamics learned by LFADS-like models could help us understand neural computation? @chethan,@arsedle, @JonathanDMcCart, and I developed ODIN to robustly recover latent dynamical features through the power of injectivity! 📜 1/ https://t.co/dovP0LxVNt