Brain–machine interfaces (BMIs) create closed-loop control systems that interact with the
brain by recording and modulating neural activity and aim to restore lost function, most …

It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear
behaviours. Here we challenge this assumption by leveraging mathematical models derived …

Objective. Complex spatiotemporal neural activity encodes rich information related to
behavior and cognition. Conventional research has focused on neural activity acquired …

Direct electrical stimulation can modulate the activity of brain networks for the treatment of
several neurological and neuropsychiatric disorders and for restoring lost function. However …

Neural activity exhibits complex dynamics related to various brain functions, internal states
and behaviors. Understanding how neural dynamics explain specific measured behaviors …

Modelling the spatiotemporal dynamics in the activity of neural populations while also
enabling their flexible inference is hindered by the complexity and noisiness of neural …

A central challenge in the computational modeling of neural dynamics is the trade-off
between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are …

Motor function depends on neural dynamics spanning multiple spatiotemporal scales of
population activity, from spiking of neurons to larger-scale local field potentials (LFP). How …

Neural dynamics can reflect intrinsic dynamics or dynamic inputs, such as sensory inputs or
inputs from other brain regions. To avoid misinterpreting temporally structured inputs as …

Recent advances in computational models of signal propagation and routing in the human
brain have underscored the critical role of white-matter structure. A complementary …