Topic: spectral learning

A central problem in systems neuroscience is to understand the relationship between sensory stimuli, neural activity, and decision-making behavior. Latent linear dynamical systems (LDS) models are one powerful framework for characterizing these relationships. For example, LDS models can be used to describe the hidden dynamics that govern how an animal's decision-making strategy evolves continuously over time. However, fitting LDS models to data using standard methods such as the expectation-maximization (EM) algorithm is computationally expensive and prone to local optima. We address these sho