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The use of graphene- and hBN-based substrates for auditory neuroelectronic interfaces

Activity IG of the STIM REI „Design and application of new nanostructured materials in neuro-electronic interfaces for biomedical application“ aims to examine new nanostructured materials suitable for interaction with nerve cells. Such research is carried out in the field of medical bionics, a rapidly growing research and technology field that can effectively connect electronic systems such as a computer chip with nervous systems. The two most successful examples of such interfaces are the cochlear implant (CI) and the auditory brainstem implant (ABI), which allow direct stimulation of the auditory nerve and the cochlear nucleus in the brainstem, respectively. These devices may evoke partial hearing and even establish full-fledged auditory communication in a completely deaf person. Nevertheless, today's fundamental limitations of non-selectivity and widespread electrical stimulation lead to large variability in the success of the implants. We intend to “attack” this fundamental limitation using nanostructured materials, such as graphene and hBN, which possess a whole range of intriguing physical and chemical properties, theoretically enabling full integration and intimate interaction with the neural tissue. In particular, we are assessing the suitability of graphene and hBN for in-vitro growth of cell cultures of spiral ganglion neurons (SGN). To assess the quality of the auditory neuro-electronic interface, parametrization of the biomedical protocol for in- vitro culturing of auditory neurons is necessary, and the identification of design specifications of graphene and hBN to be used as stimulation and recording platform. In this talk, I will first overview the state-of- the-art in auditory neuroelectronic interfaces and present the results of morphological analyses of in-vitro SGN cultures extracted from neonatal rat pups and grown on graphene and h-BN substrates, previously coated with poly-L-ornithine and laminin. The cultures were immunocytochemically stained at seven days in vitro (7DIV), and the subsequent fluorescence images were analyzed with the custom-made machine learning-based image processing allowing successful segmentation and classification of neurons. Neurons were examined for various morphological properties, including cell density, neurite length, and cell dispersion as a measure of cellular clusterization. Acknowledgment. This research was partially supported from STIM – REI, Contract Number: KK.01.1.1.01.0003, a project funded by the European Union through the European Regional Development Fund – the Operational Programme Competitiveness and Cohesion 2014-2020 (KK.01.1.1.01).

neuroelectronics ; graphene ; hBN ; miicroelectrode array

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