Ruben A Herzog

The paper maps intrinsic neural timescales across rest and tasks, showing task-induced changes while preserving temporal hierarchy and structural constraints.

This paper introduces a homeostatic inhibitory plasticity rule into a biophysical model to study how local adaptive processes influence global brain dynamics.

The paper uses multivariate information theory to analyze brain states and finds that higher-order interactions distinguish wakefulness, anaesthesia, and recovery.

This paper introduces a modified neural mass model with inhibitory synaptic plasticity to better simulate EEG and fMRI recordings during NREM sleep and capture the spectral richness of brain activity.

This study identifies CG10738 as the Drosophila EHR, showing its role in ecdysis and its expression in ETH-producing cells and other neuronal targets.

The paper examines how dimensionality reduction techniques preserve synergistic interactions in brain dynamics using resting-state fMRI data from 100 healthy participants.

The paper identifies EEG features distinguishing idiopathic hypersomnia from controls using sleep microarchitecture and hypnodensity metrics.

The paper uses whole-brain models to simulate the effects of serotonergic psychedelics on disorders of consciousness, showing they can shift brain dynamics toward conscious states.

This paper uses deep learning models to analyze whole-brain dynamics in neurodegenerative diseases, identifying key regions for inducing transitions from pathological to healthy states.

The paper introduces FastDMF, a computationally efficient implementation of the Dynamic Mean Field model, enabling simulations of whole-brain dynamics with significantly faster performance and reduced memory usage.

The paper introduces an automated method for detecting neuronal ensembles in spiking neural networks, showing it accurately identifies ensembles in both real and synthetic data.

The paper studies how retinal neuronal ensembles process natural images by analyzing their synchronized activity in response to visual stimuli.

This paper develops a mechanistic model explaining how 5HT2A-R activation increases neural entropy in the brain, showing non-uniform regional effects linked to anatomical connectivity.

This paper introduces a dimensionality reduction framework for spatio-temporal maximum entropy models of spiking networks, showing significant reduction in retinal data.

This study identifies functional differences between central and peripheral retinal ganglion cells in diurnal rodents, showing distinct receptive field sizes, response dynamics, and temporal sensitivity.