Topic: switching linear dynamical systems

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

Higher-order brain activity can be quite complex, switching between fast, stimulus- or motor-driven dynamics, and slower, rising and falling persistent activity. Many of these activity motifs can be successfully modelled by recurrent neural networks (RNNs) that are trained on specific tasks. However, the training of RNNs is usually an ill-posed problem so that, at least in principle, multiple solutions exist for any particular task. Accordingly, a specific match of a trained RNN to data can be serendipitous, providing only limited insight into the reasons underlying the similarity. Here we tak

In the past decades, functional MRI research has investigated task processing in largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard paradigms with limited dynamics. In the present work, we applied Switching Linear Dynamical Systems (SLDS), a framework that estimates a generative model of the data with both states and transitions like HMMs, to uncover the dynamics of threat processing during a continuous threat-of-s