Topic: subspace identification

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

Learning low dimensional latent dynamics in population spiking and field potential activities together can reveal the relationship between different spatiotemporal scales of population activity and can improve performance of brain-machine interfaces (BMIs). But developing a multiscale learning algorithm is challenging because spikes are discrete-valued signals while field potentials are continuous-valued signals with slower time-scales than spikes. Recently, a multiscale learning method based on expectation maximization was developed (multiscale-EM) for multimodal discrete-continuous data, whi