A computational neural network model leverages a simple unsupervised learning principle to account for recent findings on when memories move apart (differentiate) or together (integrate) in the brain.

Decoding of neural outcome and response representations allows dissecting goal-directed and habitual contributions to behaviour and reveals how acute stress biases the instrumental control of behaviour.

Electrocorticography (ECoG) reveals that phase-amplitude coupling relates to phase-dependent coding in the human brain.

Individualized cross-frequency analyses reveal regionally specific slow oscillation–sleep spindle coupling precision as predictor for gross-motor learning dynamics.

Event-based sequencing models for Alzheimer’s disease progression revealed that abnormal neural synchrony occurs during the earliest preclinical stages of the disease, preceding brain atrophy and cognitive decline.

Combined TMS-EEG can be used to further elucidate the mechanisms for pain perception and to discover biomarkers for the development of chronic pain.

The patterning of the nephron is driven by a gradient in the activity of β-catenin and further defined by a network of BMP, PTEN and NOTCH signalling.

Direct in-vivo measurements in the human brain test validity of detailed computational models of trancranial electric stimulation and show that electric fields in the brain are weaker than currently assumed.

Receptive fields for time in the hippocampus, like receptive fields for retinal space and receptive fields for numerosity, obey the Weber-Fechner law.