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!

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. youtu.be/bJ0stLORdgQ

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! snel.ai/positions

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!

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

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/ arxiv.org/abs/2309.064021/