Switching autoregressive low-rank tensor (SALT) models for neural dynamics
Hyun Dong Lee, Joshua I. Glaser, Andrew Warrington, Vladislav Susoy, Aravinthan D.T. Samuel, Liam Paninski, Scott Linderman · COSYNE 2023
Switching linear dynamical system (SLDS) models are widely used for modeling neural and behavioral time series data [Glaser et al., 2020, Linderman et al., 2019, Nair et al., 2022, Zoltowski et al., 2022]. These models characterize high-dimensional time series through a combination of discrete and continuous latent states. The continuous states offer a low-dimensional representation of the data, while the discrete state defines how the continuous states evolve over time. Together, they provide quantitative and interpretable summaries of multidimensional time series. Unfortunately, fitting thes
Fri, Mar 10, 2023