A Dynamic Spike Sorter for Multiscale Nanoelectrode Array Recordings
Abstract High‐throughput, intracellular electrophysiology is crucial for advancing the understanding of neuronal processing and network dynamics. Nanoelectrode arrays (NEA) offer a promising approach by directly capturing intracellular signals across sub‐neuronal compartments, including action poten...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2025-07-01
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| Series: | Advanced Materials Interfaces |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/admi.202500158 |
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| Summary: | Abstract High‐throughput, intracellular electrophysiology is crucial for advancing the understanding of neuronal processing and network dynamics. Nanoelectrode arrays (NEA) offer a promising approach by directly capturing intracellular signals across sub‐neuronal compartments, including action potentials, postsynaptic potentials, and low‐frequency membrane fluctuations. However, the complexity of NEA datasets, characterized by multiscale events of varying amplitude and duration, demands novel analytical strategies. In this work, a dynamic spike sorting pipeline is introduced and designed to isolate, extract, and sort these diverse electrical signals within a landscape of spontaneous electrical behavior. It is obtained estimates of signal attenuation and distortion using a bespoke biophysical circuit simulation designed to match the specific nanoelectrode interface. Based on these observations, bounds are set for filtering and extracting multiscale waveforms, and validated their isolation using pharmacological data. Finally, it is shown that multiscale analysis of spontaneous electrical recordings reveals interrelationships between high frequency events such as action potentials, and low frequency membrane potential fluctuations which may inform models of neuronal network excitability. Advanced sorting algorithms tailored for nanoelectrode array recordings are essential for unlocking the full potential of next generation, high throughput neuroelectronic devices and achieving a deeper understanding of neuronal dynamics. |
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| ISSN: | 2196-7350 |