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Multichannel parallel processing of neural signals in memristor arrays

Fully implantable neural interfaces with massive recording channels bring the gospel to patients with motor or speech function loss. As the number of recording channels rapidly increases, conventional complementary metal-oxide semiconductor (CMOS) chips for neural signal processing face severe chall...

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Detalles Bibliográficos
Autores principales: Liu, Zhengwu, Tang, Jianshi, Gao, Bin, Li, Xinyi, Yao, Peng, Lin, Yudeng, Liu, Dingkun, Hong, Bo, Qian, He, Wu, Huaqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546699/
https://www.ncbi.nlm.nih.gov/pubmed/33036975
http://dx.doi.org/10.1126/sciadv.abc4797
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author Liu, Zhengwu
Tang, Jianshi
Gao, Bin
Li, Xinyi
Yao, Peng
Lin, Yudeng
Liu, Dingkun
Hong, Bo
Qian, He
Wu, Huaqiang
author_facet Liu, Zhengwu
Tang, Jianshi
Gao, Bin
Li, Xinyi
Yao, Peng
Lin, Yudeng
Liu, Dingkun
Hong, Bo
Qian, He
Wu, Huaqiang
author_sort Liu, Zhengwu
collection PubMed
description Fully implantable neural interfaces with massive recording channels bring the gospel to patients with motor or speech function loss. As the number of recording channels rapidly increases, conventional complementary metal-oxide semiconductor (CMOS) chips for neural signal processing face severe challenges on parallelism scalability, computational cost, and power consumption. In this work, we propose a previously unexplored approach for parallel processing of multichannel neural signals in memristor arrays, taking advantage of their rich dynamic characteristics. The critical information of neural signal waveform is extracted and encoded in the memristor conductance modulation. A signal segmentation scheme is developed to adapt to device variations. To verify the fidelity of the processed results, seizure prediction is further demonstrated, with high accuracy above 95% and also more than 1000× improvement in power efficiency compared with CMOS counterparts. This work suggests that memristor arrays could be a promising multichannel signal processing module for future implantable neural interfaces.
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spelling pubmed-75466992020-10-20 Multichannel parallel processing of neural signals in memristor arrays Liu, Zhengwu Tang, Jianshi Gao, Bin Li, Xinyi Yao, Peng Lin, Yudeng Liu, Dingkun Hong, Bo Qian, He Wu, Huaqiang Sci Adv Research Articles Fully implantable neural interfaces with massive recording channels bring the gospel to patients with motor or speech function loss. As the number of recording channels rapidly increases, conventional complementary metal-oxide semiconductor (CMOS) chips for neural signal processing face severe challenges on parallelism scalability, computational cost, and power consumption. In this work, we propose a previously unexplored approach for parallel processing of multichannel neural signals in memristor arrays, taking advantage of their rich dynamic characteristics. The critical information of neural signal waveform is extracted and encoded in the memristor conductance modulation. A signal segmentation scheme is developed to adapt to device variations. To verify the fidelity of the processed results, seizure prediction is further demonstrated, with high accuracy above 95% and also more than 1000× improvement in power efficiency compared with CMOS counterparts. This work suggests that memristor arrays could be a promising multichannel signal processing module for future implantable neural interfaces. American Association for the Advancement of Science 2020-10-09 /pmc/articles/PMC7546699/ /pubmed/33036975 http://dx.doi.org/10.1126/sciadv.abc4797 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Liu, Zhengwu
Tang, Jianshi
Gao, Bin
Li, Xinyi
Yao, Peng
Lin, Yudeng
Liu, Dingkun
Hong, Bo
Qian, He
Wu, Huaqiang
Multichannel parallel processing of neural signals in memristor arrays
title Multichannel parallel processing of neural signals in memristor arrays
title_full Multichannel parallel processing of neural signals in memristor arrays
title_fullStr Multichannel parallel processing of neural signals in memristor arrays
title_full_unstemmed Multichannel parallel processing of neural signals in memristor arrays
title_short Multichannel parallel processing of neural signals in memristor arrays
title_sort multichannel parallel processing of neural signals in memristor arrays
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546699/
https://www.ncbi.nlm.nih.gov/pubmed/33036975
http://dx.doi.org/10.1126/sciadv.abc4797
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