Head of Department:
Prof. Dr. med. Dr. h. c. Heinrich Iro

Neurophysiology of hearing: Hearing loss, tinnitus and auditory learning

Our standard animal model is the Mongolian gerbil (Meriones unguiculatus); iit is a widely used model in auditory research as it has a human-like hearing range. The organization of the auditory system of these animals is well described; therefore, they are perfectly suited to investigate pathological neuro-plastic changes of that system, e.g., after acoustic noise trauma induced hearing loss or a resulting tinnitus percept development. We investigate the neurophysiological mechanisms of such pathological changes and correlate them with behavioral tests for hearing or for the existence of a tinnitus percept. Our aim is to allow the develop of new therapeutical approaches  by a deeper understanding of these patho-mechanisms. In cooperation with the audiology (Prof. Hoppe) of our hospital we are able to test our acquired knowledge directly on the patients.

Beyond that, our animals are well suited for investigating auditory learning due to their balanced nature. We utilize the so called shuttle box training to answer questions regarding the neuroplasticity in tinnitus or after brain lesions. In this approach we can watch the brain learning as we can record during the behavioral task with up to 32 electrodes in the auditory Cortex.

Sensory threshold determination

An objective, reproducible method for sensory threshold determination based on neurophysiological or behavioral parameters is essential for sensory research of all modalities as well as for diagnosis, e.g., measurement of hearing level in human infants. The latter one still is done subjectively by eye by clinical personal and therefore it may be affected by huge errors. On the basis of audiometric data of patients and animals (audiometric brain stem responses or acoustic evoked behavioral responses) we develop new, innovative and fully automated methods for threshold determination applicable also on other sensory systems.

Physiology and Pathophysiology of Sleep

In cooperation with the sleep laboratory of the ENT hospital (Dr. Traxdorf) we develop methods to objectively identify sleep stages as well as obstructive apnoe events based on EEG data. For this purpose we use a newly in out lab developed technique allowing a statistical comparison of spatio-temporal patterns in the brain. Our aim is for example the objective quantification of the therapeutic success of the CPAP (Continuous Positive Airway Pressure) therapy.

Neurophysiological correlates of Metaphors

This project aims to identify the neurophysiological correlates of metaphor development and representation in the brain. In literature and every day speech we use metaphors as a stylistic tool to transfer a known concept from its genuine context to a new context. We follow the central hypothesis that objects or concepts are represented in the brain by spatially distributed activity patterns of neuronal networks. These representations are grouped in semantic categories (=domains), e.g. humans, animals, tools, music instruments, physics and literature. In case of metaphorical use of a given concept there should occur a transfer or shift of activation from the original domain to the new domain. For this process the term “cross-domain-mapping” has been coined in cognitive linguistics. In a highly interdisciplinary team of literature scientist, linguists, cognitive scientists, neuroscientists and physicists we design experimental paradigms and apply MEG and EEG measurements to first, identify the patterns that correspond to certain object representations and, second, to identify correlates of the above described cross-domain-mapping.

Computational Neuroscience and Theoretical Neurophysics

In this project we apply the approaches and methods of physics to study (simulated) neural systems and circuits. We aim to analyze, simulate and model fundamental principles of such networks and therefore brain function to understand the relation between neural structure and dynamics. In contrast to common machine learning approaches, we here do not try to optimize a given classification or prediction task, but rather use artificial neural networks as a tool to study the dynamics and rules of (unsupervised) learning. We therefore use methods and concepts from the fields of statistical mechanics, theory of complex systems, graph and network theory, information theory, dynamical systems and chaos theory. The ultimate goal of this project is to describe the minimum set of features of an information processing system (artificial and natural) that are necessary and sufficient for perception, cognition and motor control.