Addressing the multiscale organization of the brain, which is fundamental to the dynamic
repertoire of the organ, remains challenging. In principle, it should be possible to model …

To meet the requirement of big data era and neuromorphic computations, nonvolatile
memory with fast speed, high density, and low power consumption is urgently needed. As an …

MEG-EEG Primer presents the basic A-to-Z of two non-invasive human electrophysiological
methods, magnetoencephalography (MEG) and electroencephalography (EEG). These …

An" elegant"," engrossing"(Carol Tavris, Wall Street Journal) examination of what we think
we know about the brain and why--despite technological advances--the workings of our …

Traditional general circulation models, or GCMs—that is, three-dimensional dynamical
models with unresolved terms represented in equations with tunable parameters—have …

Model-based data analysis of whole-brain dynamics links the observed data to model
parameters in a network of neural masses. Recently, studies focused on the role of regional …

Background Neuroergonomics combines neuroscience with ergonomics to study human
performance using recorded brain signals. Such neural signatures of performance can be …

The increasing availability of brain data within and outside the biomedical field, combined
with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for …

Recent decades have ushered in tremendous progress in understanding the neural basis of
language. Most of our current knowledge on language and the brain, however, is derived …

Despite significant improvements in our understanding of brain diseases, many barriers
remain. Cognitive neuroscience faces four major challenges: complex structure–function …