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Multiscale Cloud-based Pipeline for Neuronal Electrophysiology Analysis and Visualization

Geng, J.; Voitiuk, K.; Parks, D. F.; Robbins, A.; Spaeth, A.; Sevetson, J. L.; Hernandez, S.; Schweiger, H. E.; Andrews, J. P.; Seiler, S. T.; Elliott, M. A. T.; Chang, E. F.; Nowakowski, T. J.; Currie, R.; Mostajo-Radji, M. A.; Haussler, D.; Sharf, T.; Salama, S. R.; Teodorescu, M.

2024-12-14 neuroscience
10.1101/2024.11.14.623530 bioRxiv
Show abstract

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 and ex vivo brain slice recordings to show that this pipeline simplifies the data analysis process and facilitates understanding neuronal activity.

Published in Array · not in our set (fewer than 10 published preprints to learn from) · training set

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.