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 …

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 …

The neurophysiological processes underlying non-invasive brain activity measurements are
incompletely understood. Here, we developed a connectome-based brain network model …

A key challenge for neuroscience is to develop generative, causal models of the human
nervous system in an individualized, data-driven manner. Previous initiatives have either …

Most human neuroscience research to date has focused on statistical approaches that
describe stationary patterns of localized neural activity or blood flow. While these patterns …

Brain dynamics are highly complex and yet hold the key to understanding brain function and
dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging …

Neuroscientists today can measure activity from more neurons than ever before, and are
facing the challenge of connecting these brain-wide neural recordings to computation and …

At rest, mammalian brains display remarkable spatiotemporal complexity, evolving through
recurrent functional connectivity (FC) states on a slow timescale of the order of tens of …

The brain is in a state of perpetual reverberant neural activity, even in the absence of
specific tasks or stimuli. Shedding light on the origin and functional significance of such a …

Studies of resting state functional MRI (rs-fRMI) are increasingly focused on “dynamics”, or
on those properties of brain activation that manifest and vary on timescales shorter than the …