In recent years, advances in neuroscience and artificial intelligence have paved the way for
unprecedented opportunities to understand the complexity of the brain and its emulation …

Elucidating the intricate neural mechanisms underlying brain functions requires integrative
brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose …

Nonlinear operators with long-distance spatiotemporal dependencies are fundamental in
modelling complex systems across sciences; yet, learning these non-local operators …

Understanding the brain requires studying its multiscale interactions from molecules to
networks. The increasing availability of large-scale datasets detailing brain circuit …

We present a general, trainable oscillatory neural network as a large-scale model of brain
dynamics. The model has a cascade of two stages-an oscillatory stage and a complex …

Whole-brain models have proven to be useful to understand the emergence of collective
activity among neural populations or brain regions. These models combine connectivity …

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations
(SOs) between up-and down-states travel across the cortex. While an isolated piece of …

The neural mechanisms underlying brain functions are extremely complicated. Brain
dynamics modeling is an indispensable tool for elucidating these mechanisms by modeling …

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many
genetic, brain organizational, and behavioral characteristics with humans. A macaque brain …

Different whole-brain computational models have been recently developed to investigate
hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) …