Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes

Understand

Recent advances in recording technology have allowed neuroscientists to monitor activity from thousands of neurons simultaneously.

• Latent variable models are increasingly valuable for distilling these recordings into compact and interpretable representations.
• Here we propose a new approach to neural data analysis that leverages advances in conditional generative modeling to enable the unsupervised inference of disentangled behavioral variables from recorded neural activity.
• Our approach builds on InfoDiffusion, which augments diffusion models with a set of latent variables that capture important factors of variation in the data.