Latent diffusion for neural spiking data for generating realistic neural time series
Auguste Schulz, Jaivardhan Kapoor, Julius Vetter, Felix Pei, Richard Gao, Jakob Macke · COSYNE 2025
Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings from such recordings, using them to generate realistic spiking data, especially in a behavior-dependent manner, still poses a challenge. Here, we present Latent Diffusion for Neural Spiking data (LDNS), a diffusion-based generative model with a low-dimensional latent space: LDNS employs an autoencoder with structured state-space (S4) layers to proj
ePoster · Neuroscience