NeuroNella: Automatic identification of neural activity from multielectrode arrays with blind source separation
File(s)
Published version
Author(s)
Germer, C
Farina, D
Baker, SN
Vecchio, A Del
Type
Journal Article
Abstract
Objective. The identification of individual neuronal activity from multielectrode arrays poses significant challenges, including handling data from numerous electrodes, resolving overlapping action potentials and tracking activity across long recordings. This study introduces NeuroNella, an automated algorithm developed to address these challenges. Approach. NeuroNella employs blind source separation to leverage the sparsity of action potentials in multichannel recordings. It was validated using three datasets, including two publicly available ones: (1) in vitro recordings (252 channels) of retinal ganglion cells from mice with simultaneous ground-truth loose patch data to assess accuracy; (2) a Neuropixel recording from an awake mouse, comprising 374 channels spanning different brain areas, to demonstrate scalability with dense multielectrode configurations in in vivo recordings; and (3) data (32 channels) recorded from the medullary reticular formation in a terminally anaesthetised macaque, to showcase decomposition over long periods of time. Main results. The algorithm exhibited an error rate of less than 1% compared to ground-truth data. It reliably identified individual neurons, detected neuronal activity across a wide amplitude range, and tolerated minor probe shifts, maintaining robustness in prolonged experimental sessions. Significance. NeuroNella provides an automated and efficient method for neuronal activity identification. Its adaptability to diverse dataset, species, and recording configurations underscores its potential to advance studies of neuronal dynamics and facilitate real-time neuronal decoding systems.
Date Issued
2025-04-01
Date Acceptance
2025-03-26
Citation
Journal of Neural Engineering, 2025, 22 (2)
ISSN
1741-2560
Publisher
IOP Publishing
Journal / Book Title
Journal of Neural Engineering
Volume
22
Issue
2
Copyright Statement
© 2025 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
License URL
Identifier
10.1088/1741-2552/adc5a4
Subjects
independent component analysis
extracellular decomposition
neural activity
Publication Status
Published
Article Number
ARTN 026059
Date Publish Online
2025-04-16