Understanding the dynamical processes driving the functioning of the brain, especially inter-regional connectivity, remains a significant challenge. This study examines methods for analyzing the electrophysiological activity and connectivity of in-vitro neural networks, which are pivotal for getting insights into brain functions and neurological disorders such as epilepsy and Alzheimer's disease. Using multi-electrode arrays (MEAs) with 4096 electrodes, we recorded extracellular local field potentials from cultured neural networks. We describe our experimental setup, focusing on high-density MEA technology, and outline protocols for data collection and analysis using Python and Fortran. Our results, on the analysis of MEA signals, contribute to the understanding of the dynamics of cultured neural networks and in the development of new methods for future research.

Analysis of MEA recordings in cultured neural networks

Tonelli, F;Calcagnile, LM;Cremisi, F;
2024

Abstract

Understanding the dynamical processes driving the functioning of the brain, especially inter-regional connectivity, remains a significant challenge. This study examines methods for analyzing the electrophysiological activity and connectivity of in-vitro neural networks, which are pivotal for getting insights into brain functions and neurological disorders such as epilepsy and Alzheimer's disease. Using multi-electrode arrays (MEAs) with 4096 electrodes, we recorded extracellular local field potentials from cultured neural networks. We describe our experimental setup, focusing on high-density MEA technology, and outline protocols for data collection and analysis using Python and Fortran. Our results, on the analysis of MEA signals, contribute to the understanding of the dynamics of cultured neural networks and in the development of new methods for future research.
2024
Settore BIO/09 - Fisiologia
Settore BIOS-06/A - Fisiologia
2024 IEEE Workshop on Complexity in Engineering, COMPENG 2024
Firenze, Italia
22-24 Luglio 2024
2024 IEEE Workshop on Complexity in Engineering (COMPENG)
IEEE
979-8-3503-8279-2
   Artificial Intelligence with Cultured Neuronal Networks
   AICult
   Ministero della pubblica istruzione, dell'università e della ricerca
   2022M95RC7

   Tuscany Health Ecosystem
   THE
   MUR
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/165943
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