Claus Metzner

Deep learning based decoding of local field potential events

This paper uses an auto-encoder to decode single-trial local field potential events, revealing neural activity patterns and information flux direction in rodents and humans.

Intrinsic noise improves speech recognition in a computational model of the auditory pathway

This study shows that intrinsic noise improves speech recognition in a deep neural network model of the auditory pathway, suggesting potential benefits for both biological and machine learning systems.

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.

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.

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.