We extended classical linear-Gaussian state space models of neural circuit dynamics to capture nonlinear dependencies on experimental conditions, while maintaining ease of fit and interpretability.

Diffusion models disentangle latent sources from multiple, noisy, incomplete observations without source-specific assumptions, yielding generative priors and posterior inference across synthetic tasks and real galaxy data.