Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …

This paper presents methods for the estimation of the time-varying directions of arrival
(DOAs) of signals emitted by moving sources. Following the sparse Bayesian learning (SBL) …

Objective: The reconstruction of an input based on a sparse combination of signals, known
as sparse coding, has found widespread use in signal processing. In this work, the …

Time-varying linear state-space models are powerful tools for obtaining mathematically
interpretable representations of neural signals. For example, switching and decomposed …

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 …

Objective. Neuromodulation systems that use closed-loop brain stimulation to control brain
states can provide new therapies for brain disorders. To date, closed-loop brain stimulation …

Optical imaging of calcium signals in the brain has enabled researchers to observe the
activity of hundreds-to-thousands of individual neurons simultaneously. Current methods …

The development of online algorithms to track time-varying systems has drawn a lot of
attention in the last years, in particular in the framework of online convex optimization …

This paper presents a Bayesian estimation method for sequential direction finding. The
proposed method estimates the number of directions of arrivals (DOAs) and their DOAs …

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 …