Topic: autoregressive hidden Markov model

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

Behavior analysis typically relies on trial-based repetitions of constrained tasks. We aimed to uncover how age, pain, and itch influence the patterns of behavioral sequences and their transitions in freely behaving animals using MoSeq, a machine learning network analysis tool developed by the Datta lab. Mice of different ages were treated with chloroquine (itch) or formalin (pain) and placed in an open field arena and recorded for 30 minutes via a high-speed color camera and infrared depth camera. A trained autoregressive hidden Markov model classified the mouse movements into sub-second syll