AutoMEA: machine learning-based burst detection for multi-electrode array datasets.

ABSTRACT: Neuronal activity in the highly organized networks of the central nervous system is the vital basis for various functional processes, such as perception, motor control, and cognition. Understanding interneuronal connectivity and how activity is regulated in the neuronal circuits is crucial for interpreting how the brain works. Multi-electrode arrays (MEAs) are particularly useful for studying the dynamics of neuronal network activity and their development as they allow for real-time, high-throughput measurements of neural activity. At present, the key challenge in the utilization of MEA data is the sheer complexity of the measured datasets. Available software offers semi-automated analysis for a fixed set of parameters that allow for the definition of spikes, bursts and network bursts. How

SUBMITTER: Hernandes V 

PROVIDER: S-EPMC11655478 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature