Patrick Krauß

The paper investigates how syntactic processing, predictive coding, and semantic grounding interact in real time using MEG-EEG and compares neural responses to a large language model.

This study uses EEG to show that prepositional verbs and verb-to-infinitive constructions are processed compositionally, while phrasal verbs are stored as lexical units.

This study uses eye-tracking to compare cognitive processing of phrasal verbs and prepositional verb-particle constructions, finding phrasal verbs are processed more efficiently.

This study examines neural processing of auditory and speech stimuli of varying complexity using MEG, revealing hemispheric lateralization patterns and temporal dynamics in speech perception.

This study uses combined EEG and MEG to analyze spatio-temporal patterns of noun and verb processing in the human brain, finding consistent patterns across two sub-samples.

The paper measures an early subcortical response to continuous speech using MEG, finding a 9 ms latency component similar to EEG measurements.

This study shows that selective attention modulates cortical contributions to the speech-FFR, with attended speakers eliciting stronger neural responses and lower fundamental frequencies also producing larger cortical responses.

This study examines EEG responses to collocations in continuous speech, finding distinct N400 patterns compared to non-collocations, varying by brain region and laterality.

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.

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.

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.

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

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

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.

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

The paper combines natural speech and MEG to create a corpus of speech-evoked neuronal activity using computational corpus linguistics methods.

The paper shows that simulated hearing loss in Mongolian gerbils leads to improved sensitivity at the notched frequency but not outside, and behavioral signs of phantom sounds.

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.