Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and
symptoms that appear in early childhood. ASD is also associated with communication …

Neural network models are potential tools for improving our understanding of complex brain
functions. To address this goal, these models need to be neurobiologically realistic …

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 …

Living systems break detailed balance at small scales, consuming energy and producing
entropy in the environment to perform molecular and cellular functions. However, it remains …

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 …

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical
models of abnormal activities to generate spatio-temporal seizure patterns as observed in …

We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian
state-space modeling approach used to infer effective connectivity from noninvasive …

Biological agents can act in ways that express a sensitivity to context-dependent relevance.
So far it has proven difficult to engineer this capacity for context-dependent sensitivity to …

We offer an account of mental health and well-being using the predictive processing
framework (PPF). According to this framework, the difference between mental health and …

Neural complexity and brain entropy (BEN) have gained greater interest in recent years. The
dynamics of neural signals and their relations with information processing continue to be …