Datensatz Zitierlink: 
http://hdl.handle.net/20.500.12708/190881
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Titel: 
Identification of Axon Bendings in Neurons by Multiphysics FEM Simulations of High-Density MEA Extracellular Recordings
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Zitat: 
Leva, F., Corna, A., Werginz, P., Palestri, P., Zeck, G. M., & Selmi, L. (2023). Identification of Axon Bendings in Neurons by Multiphysics FEM Simulations of High-Density MEA Extracellular Recordings. In 2023 IEEE SENSORS (pp. 1–4). IEEE. https://doi.org/10.1109/SENSORS56945.2023.10325212
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Verlags-DOI: 
10.1109/SENSORS56945.2023.10325212
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Publikationstyp: 
Konferenzbeitrag - Full-Paper Contribution
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Sprache: 
Englisch
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Autor_innen: 
Leva, Federico 
Corna, Andrea 
Werginz, Paul 
Palestri, Pierpaolo 
Zeck, Günther Michael 
Selmi, Luca 
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Organisations­einheit: 
E363 - Institut für Biomedizinische Elektronik 
E101-03 - Forschungsbereich Scientific Computing and Modelling 
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Erschienen in: 
2023 IEEE SENSORS 
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ISBN: 
979-8-3503-0387-2
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Datum (veröffentlicht): 
2023
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Veranstaltungs­name: 
2023 IEEE SENSORS 
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Veranstaltungs­zeitraum: 
29-Okt-2023 - 1-Nov-2023
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Veranstaltungs­ort: 
VIenna, Österreich
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Umfang: 
4
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Verlag: 
IEEE, Piscataway
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Keywords: 
Axons; Microelectrodes; Neurons; Bending; Retina; Sensors; Finite element analysis
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Abstract: 
A three-dimensional finite element method (FEM) simulation of a neuron (retinal ganglion cell) interfaced to a high-density CMOS-based microelectrode array (MEA) is performed and compared to recordings. The adopted multiscale simulation approach accurately reproduces the signals recorded by the patch clamp and the MEA sensors. The simulation leads us to identify bendings of the axon initial segment of a few neurons embedded in tissue, which are reported here for the first time.
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Forschungs­schwerpunkte: 
Beyond TUW-research foci: 100%
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Wissenschafts­zweig: 
3019 - Sonstige Medizinisch-theoretische Wissenschaften: 30%
3014 - Neurowissenschaften: 50%
2020 - Elektrotechnik, Elektronik, Informationstechnik: 20%
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Enthalten in den Sammlungen:
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