Scholar
Patrick Krauss
Google Scholar ID: WRThjRAAAAAJ
University Erlangen-Nurnberg
Citations & Impact
Citations
2,179
H-index
27
i10-index
54
Publications
20
Co-authors
20
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Information not available
Research Experience
- Research areas include:
- - Cognitive and Computational Neuroscience
- - Speech and Language Processing
- - Investigating how speech and language are represented and processed in the human brain and AI systems, with a focus on Large Language Models (LLMs)
- - Using advanced neuroimaging techniques such as MEG, EEG, and invasive iEEG to study natural speech (e.g., audiobooks) and uncover complex brain functions
- - Studying phenomena like tinnitus and hyperacusis to advance understanding of pathological speech and hearing disorders
- - Modeling how the brain organizes and navigates thoughts and complex information, such as linguistic structures, through neural network-based successor representations
- - Developing biologically plausible models to simulate brain functions, advancing robust, explainable AI inspired by neuroscience
- - Investigating the structural and dynamic properties of RNNs using theoretical physics methods like dynamical systems, chaos theory, and information theory
- - Exploring the role of noise and randomness in neural information processing
- - Understanding how auditory and speech perceptions emerge from interactions between top-down predictive coding and bottom-up adaptive stochastic resonance
- - Using NLP tools to align continuous speech stimuli with neural recordings, enabling real-time analysis of linguistic processing in the brain
- - Investigating neural responses to varying linguistic structures (e.g., words, phonemes) using NLP-based segmentation and forced alignment techniques
- - Integrating computational corpus linguistics with neuroimaging to explore real-world language processing and generate hypotheses from large-scale datasets
- - Developing cutting-edge methodologies to analyze and visualize high-dimensional, multi-modal neuroimaging data
- - Applying NLP frameworks to enhance the precision of neural data synchronization and the study of linguistic phenomena in naturalistic conditions
- - Addressing AI’s black box problem by reverse-engineering and interpreting the behavior of neural networks, including LLMs
Education
- Information not available
Background
- Physicist, neuroscientist, cognitive scientist, and AI researcher, currently working as a postdoctoral researcher and lecturer at the Friedrich-Alexander-University Erlangen-Nuremberg (FAU). His interdisciplinary research bridges neuroscience and artificial intelligence, exploring the intricate mechanisms of the brain and their applications to cutting-edge AI technologies.
Miscellany
- Information not available
