Active inference is a normative principle underwriting perception, action, planning, decision-
making and learning in biological or artificial agents. From its inception, its associated …

Predictive coding is possibly one of the most influential, comprehensive, and controversial
theories of neural function. While proponents praise its explanatory potential, critics object …

Abstract Dynamic Causal Modelling (DCM) is the predominant method for inferring effective
connectivity from neuroimaging data. In the 15 years since its introduction, the neural …

Background: Neuronal-and circuit-level abnormalities of excitation and inhibition are shown
to be associated with tau and amyloid-beta (Aβ) in preclinical models of Alzheimer's disease …

This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual
(Taylor) approximation to neuronal dynamics with a neural mass model of the canonical …

At the inception of human brain mapping, two principles of functional anatomy underwrote
most conceptions—and analyses—of distributed brain responses: namely, functional …

Psychiatry faces fundamental challenges with regard to mechanistically guided differential
diagnosis, as well as prediction of clinical trajectories and treatment response of individual …

Continuous-time neural networks are a class of machine learning systems that can tackle
representation learning on spatiotemporal decision-making tasks. These models are …

The human connectome will provide a detailed mapping of the brain's connectivity, with
fundamental insights for health and disease. However, further understanding of brain …

Various methods have been proposed to characterize the functional connectivity between
nodes in a network measured with different modalities (electrophysiology, functional …