Neuronal networks in the brain can process information particularly well when they are close to a critical point – or so brain researchers had assumed based on theoretical considerations. However, experimental investigations of brain activity revealed much fewer indicators of such critical states than expected. Scientists from Forschungszentrum Jülich and RWTH Aachen University have now proposed a possible explanation. They showed that neuronal networks can assume a second, previously unknown critical mode whose hidden dynamics are almost impossible to measure with conventional methods.

The long-lasting aftereffects of non-invasive transcranial direct current stimulation (tDCS) promise an alleviation of severe symptoms of diseases like depressive disorder or chronic pain. In a new modeling study, researchers from the Bernstein Center Freiburg suggest that the aftereffects observed in experiments may be a consequence of homeostatic network growth. Their model is based on the idea that the stimulation triggers a rearrangement of synaptic couplings among stimulated and unstimulated neurons, eventually leading to network remodeling and cell assembly formation.

Funding of "Deep Learning Methods for Association Studies of Transcriptomic and Systemic Dynamics in Morphogenetically Active Tissues"

Göttingen research project "Deep Movement Diagnostics" receives around 1.2 million euros for the development of three-dimensional reconstructions of movement patterns

Tuan Pham from the University of Chicago (USA) will be awarded this year's Brains for Brains Young Researchers Award of the Bernstein Network Computational Neuroscience.

HBP scientists argue for brain simulators as “mathematical observatories” for neuroscience

Researchers at the University Medical Center Göttingen (UMG) and the German Primate Center – Leibniz Institute for Primate Research (DPZ) are demonstrating improved frequency resolution of artificial hearing using optical stimulation of the inner ear.

New study investigates 'social acceleration'

A new study carried out in a collaboration between researchers from LMU and UC San Diego suggests that new sensory experiences are encoded in pre-existing patterns of neuronal activity, which are recalled, modulated and enhanced following periods of behavioral activity.

Chemnitz University of Technology develops learning algorithm for improved car body manufacturing in BMBF-funded project