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Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays
Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703587/ https://www.ncbi.nlm.nih.gov/pubmed/29131818 http://dx.doi.org/10.1371/journal.pcbi.1005842 |
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author | Mena, Gonzalo E. Grosberg, Lauren E. Madugula, Sasidhar Hottowy, Paweł Litke, Alan Cunningham, John Chichilnisky, E. J. Paninski, Liam |
author_facet | Mena, Gonzalo E. Grosberg, Lauren E. Madugula, Sasidhar Hottowy, Paweł Litke, Alan Cunningham, John Chichilnisky, E. J. Paninski, Liam |
author_sort | Mena, Gonzalo E. |
collection | PubMed |
description | Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible. |
format | Online Article Text |
id | pubmed-5703587 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57035872017-12-08 Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays Mena, Gonzalo E. Grosberg, Lauren E. Madugula, Sasidhar Hottowy, Paweł Litke, Alan Cunningham, John Chichilnisky, E. J. Paninski, Liam PLoS Comput Biol Research Article Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible. Public Library of Science 2017-11-13 /pmc/articles/PMC5703587/ /pubmed/29131818 http://dx.doi.org/10.1371/journal.pcbi.1005842 Text en © 2017 Mena et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Mena, Gonzalo E. Grosberg, Lauren E. Madugula, Sasidhar Hottowy, Paweł Litke, Alan Cunningham, John Chichilnisky, E. J. Paninski, Liam Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays |
title | Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays |
title_full | Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays |
title_fullStr | Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays |
title_full_unstemmed | Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays |
title_short | Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays |
title_sort | electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703587/ https://www.ncbi.nlm.nih.gov/pubmed/29131818 http://dx.doi.org/10.1371/journal.pcbi.1005842 |
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