Holger Schulze

Chronic stress does not induce behavioural signs of tinnitus or cochlear synaptopathy in Mongolian gerbils

This study shows that chronic stress alone does not cause tinnitus or cochlear synaptopathy in Mongolian gerbils, suggesting stress may not be a primary cause of tinnitus.

Behavioral assessment of Zwicker tone percepts in rodents

The paper introduces a modified GPIAS paradigm to assess Zwicker tone perception in rodents and finds a significant increase in the GPIAS effect with notched noise, suggesting perception of the Zwicker tone.

Sleep as a random walk - A superstatistical analysis of EEG data across sleep stages

This paper analyzes EEG data across sleep stages using superstatistics to show that sleep is a continuous process rather than discrete stages.

Tinnitus development is associated with synaptopathy of inner hair cells in Mongolian gerbils

This study links tinnitus development to inner hair cell synaptopathy in Mongolian gerbils following acoustic trauma, independent of hearing loss severity.

Analysis and Visualization of Sleep Stages based on Deep Neural Networks

This study develops a deep learning method to analyze and visualize sleep stages using EEG data, showing it can estimate sleep cycle lengths and detect disturbances.

Acute and long-term circuit-level effects in the auditory cortex after sound trauma

This study investigates acute and long-term changes in auditory cortex circuitry after sound trauma in gerbils, showing compensatory mechanisms in thalamocortical and intracortical inputs.

The Stochastic Resonance model of auditory perception: A unified explanation of tinnitus development, Zwicker tone illusion, and residual inhibition.

The paper proposes a stochastic resonance model to explain tinnitus, Zwicker tone illusion, and residual inhibition through auditory system adaptation and somatosensory input changes.

Weight statistics controls dynamics in recurrent neural networks

This paper shows that balance between excitatory and inhibitory connections in recurrent neural networks controls their dynamics, enabling optimal information processing at the edge of chaos.