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Article: Multichannel parallel processing of neural signals in memristor arrays

TitleMultichannel parallel processing of neural signals in memristor arrays
Authors
Issue Date2020
Citation
Science Advances, 2020, v. 6, n. 41, article no. eabc4797 How to Cite?
AbstractFully 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.
Persistent Identifierhttp://hdl.handle.net/10722/334692
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Zhengwu-
dc.contributor.authorTang, Jianshi-
dc.contributor.authorGao, Bin-
dc.contributor.authorLi, Xinyi-
dc.contributor.authorYao, Peng-
dc.contributor.authorLin, Yudeng-
dc.contributor.authorLiu, Dingkun-
dc.contributor.authorHong, Bo-
dc.contributor.authorQian, He-
dc.contributor.authorWu, Huaqiang-
dc.date.accessioned2023-10-20T06:49:58Z-
dc.date.available2023-10-20T06:49:58Z-
dc.date.issued2020-
dc.identifier.citationScience Advances, 2020, v. 6, n. 41, article no. eabc4797-
dc.identifier.urihttp://hdl.handle.net/10722/334692-
dc.description.abstractFully 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.-
dc.languageeng-
dc.relation.ispartofScience Advances-
dc.titleMultichannel parallel processing of neural signals in memristor arrays-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1126/sciadv.abc4797-
dc.identifier.pmid33036975-
dc.identifier.scopuseid_2-s2.0-85092753772-
dc.identifier.volume6-
dc.identifier.issue41-
dc.identifier.spagearticle no. eabc4797-
dc.identifier.epagearticle no. eabc4797-
dc.identifier.eissn2375-2548-
dc.identifier.isiWOS:000579161500035-

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