Objective. Dynamic latent state models are widely used to characterize the dynamics of
brain network activity for various neural signal types. To date, dynamic latent state models …

Modeling brain dynamics to better understand and control complex behaviors underlying
various cognitive brain functions have been of interest to engineers, mathematicians and …

Invasive brain-computer interfaces (BCIs) have the capability to simultaneously record
discrete signals across multiple scales, but how to effectively process and analyze these …

New technologies for recording the activity of large neural populations during complex
behavior provide exciting opportunities for investigating the neural computations that …

Ongoing advances in experimental technique are making commonplace simultaneous
recordings of the activity of tens to hundreds of cortical neurons at high temporal resolution …

Latent dynamics models have emerged as powerful tools for modeling and interpreting
neural population activity. Recently, there has been a focus on incorporating simultaneously …

Closed-loop neurotechnologies often need to adaptively learn an encoding model that
relates the neural activity to the brain state, and is used for brain state decoding. The speed …

Networks of spiking neurons with adaption have been shown to be able to reproduce a wide
range of neural activities, including the emergent population bursting and spike synchrony …

Linear parametric state-space models are a ubiquitous tool for analyzing neural time series
data, providing a way to characterize the underlying brain dynamics with much greater …

Brain networks are complex dynamical systems in which directed interactions between
different areas evolve at the sub-second scale of sensory, cognitive and motor processes …