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Abstract How seizures begin at the level of microscopic neural circuits remains unknown. High-density CMOS microelectrode arrays provide a new avenue for investigating neuronal network activity, with unprecedented spatial and temporal resolution. We use high-density CMOS-based microelectrode arrays to probe the network activity of human hippocampal brain slices from six patients with mesial temporal lobe epilepsy in the presence of hyperactivity promoting media. Two slices from the dentate gyrus exhibited epileptiform activity in the presence of low magnesium media with kainic acid. Both slices displayed an electrophysiological phenotype consistent with a reciprocally connected circuit, suggesting a recurrent feedback loop is a key driver of epileptiform onset. Larger prospective studies are needed, but these findings have the potential to elucidate the network signals underlying the initiation of seizure behavior. 

SUMMARY Electrophysiology offers a high-resolution method for real-time measurement of neural activity. Longitudinal recordings from high-density microelectrode arrays (HD-MEAs) can be of considerable size for local storage and of substantial complexity for extracting neural features and network dynamics. Analysis is often demanding due to the need for multiple software tools with different runtime dependencies. To address these challenges, we developed an open-source cloud-based pipeline to store, analyze, and visualize neuronal electrophysiology recordings from HD-MEAs. This pipeline is dependency agnostic by utilizing cloud storage, cloud computing resources, and an Internet of Things messaging protocol. We containerized the services and algorithms to serve as scalable and flexible building blocks within the pipeline. In this paper, we applied this pipeline on two types of cultures, cortical organoids andex vivobrain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.