Unsupervised learning of stationary and switching dynamical system models from Poisson observations.

ABSTRACT: Objective. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanatory power and interpretability by piecing together successive regimes of simpler dynamics to capture more complex ones. However, in many cases, reliable regime labels are not available, thus demanding accurate unsupervised learning methods for Poisson observations. Existing learning methods, however, rely on inference of latent states in neural activity using the Laplace approximation, which may not capture the broader properties of densities and may lead to inaccurate learning. Thus, there is a need for new inference methods th

SUBMITTER: Song CY 

PROVIDER: S-EPMC10714100 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature