Topic: expectation maximization

Understanding the neural code depends on capturing latent computational features within the highly complex dynamics of neural activity. While many features of neural dynamics will be relevant for a given computation, other features will not. In contrast with existing methods that describe how latent dynamics depend on experimental variables, we present a model designed to factor out variables that are irrelevant to the latent dynamics. We propose a fluctuating hidden Markov model (FHMM) that simultaneously learns an HMM that captures latent dynamics, and a baseline, dynamic firing rate that ca

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