Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …

A core goal in systems neuroscience and neuroethology is to understand how neural circuits
generate naturalistic behavior. One foundational idea is that complex naturalistic behavior …

An important problem in time-series analysis is modeling systems with time-varying
dynamics. Probabilistic models with joint continuous and discrete latent states offer …

In the near future, value streams associated with Industry 4.0 will be formed by
interconnected cyber–physical elements forming complex networks that generate huge …

The problem of discovering interpretable dynamic patterns from spatiotemporal data is
studied in this paper. For that purpose, we develop a time-varying reduced-rank vector …

Learning interpretable representations of neural dynamics at a population level is a crucial
first step to understanding how observed neural activity relates to perception and behavior …

Many physical processes display complex high-dimensional time-varying behavior, from
global weather patterns to brain activity. An outstanding challenge is to express high …

Modelling biological or engineering swarms is challenging due to the inherently high
dimension of the system, despite the often low-dimensional emergent dynamics. Most …

Neurons in parietal cortex exhibit task-related activity during decision-making tasks.
However, it remains unclear how long-term training to perform different tasks over months or …

Learning interpretable representations of neural dynamics at a population level is a crucial
first step to understanding how observed neural activity relates to perception and behavior …