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Article: Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

TitleBridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges
Authors
Keywordsartificial neural systems
biological neural systems
brain-inspired computing
memristors
neural networks
neuromorphic devices
Issue Date2019
Citation
Advanced Materials, 2019, v. 31, n. 49, article no. 1902761 How to Cite?
Abstract© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re-visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.
Persistent Identifierhttp://hdl.handle.net/10722/287003
ISSN
2020 Impact Factor: 30.849
2020 SCImago Journal Rankings: 10.707
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTang, Jianshi-
dc.contributor.authorYuan, Fang-
dc.contributor.authorShen, Xinke-
dc.contributor.authorWang, Zhongrui-
dc.contributor.authorRao, Mingyi-
dc.contributor.authorHe, Yuanyuan-
dc.contributor.authorSun, Yuhao-
dc.contributor.authorLi, Xinyi-
dc.contributor.authorZhang, Wenbin-
dc.contributor.authorLi, Yijun-
dc.contributor.authorGao, Bin-
dc.contributor.authorQian, He-
dc.contributor.authorBi, Guoqiang-
dc.contributor.authorSong, Sen-
dc.contributor.authorYang, J. Joshua-
dc.contributor.authorWu, Huaqiang-
dc.date.accessioned2020-09-07T11:46:14Z-
dc.date.available2020-09-07T11:46:14Z-
dc.date.issued2019-
dc.identifier.citationAdvanced Materials, 2019, v. 31, n. 49, article no. 1902761-
dc.identifier.issn0935-9648-
dc.identifier.urihttp://hdl.handle.net/10722/287003-
dc.description.abstract© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim As the research on artificial intelligence booms, there is broad interest in brain-inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re-visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.-
dc.languageeng-
dc.relation.ispartofAdvanced Materials-
dc.subjectartificial neural systems-
dc.subjectbiological neural systems-
dc.subjectbrain-inspired computing-
dc.subjectmemristors-
dc.subjectneural networks-
dc.subjectneuromorphic devices-
dc.titleBridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/adma.201902761-
dc.identifier.pmid31550405-
dc.identifier.scopuseid_2-s2.0-85074017570-
dc.identifier.volume31-
dc.identifier.issue49-
dc.identifier.spagearticle no. 1902761-
dc.identifier.epagearticle no. 1902761-
dc.identifier.eissn1521-4095-
dc.identifier.isiWOS:000488035400001-
dc.identifier.issnl0935-9648-

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