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 …

This research introduces a new method for functional brain imaging via a process of model
inversion. By estimating parameters of a computational model, we are able to track effective …

One of the main limitations of the brain functional connectivity estimation methods based on
Autoregressive Modeling, like the Granger Causality family of estimators, is the hypothesis …

This paper presents a new approach to inverting (fitting) models of coupled dynamical
systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion …

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 …

Data assimilation in dynamical networks is intrinsically challenging. A method is introduced
for the tracking of heterogeneous networks of oscillators or excitable cells in a nonstationary …

Neurocomputational models of large-scale brain dynamics utilizing realistic connectivity
matrices have advanced our understanding of the operational network principles in the …

A Kalman filter can be used to fill space–state reconstruction dynamics based on knowledge
of a system and partial measurements. However, its performance relies on accurate …

Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as
changes in white matter connectivity and grey matter structure through processes including …

Simulations of neural activity at different levels of detail are ubiquitous in modern
neurosciences, aiding the interpretation of experimental data and underlying neural …