Memristor bridge synapse for better artificial neuron perceptron

In artificial neural networks, the fourth passive element memristor can be utilized as an electronic synapse that serves as the interface between neurons. The artificial neuron composed of the memristor bridge synapse not only has the characteristics of low power consumption and high integration but...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:AIP advances 2023-05, Vol.13 (5), p.055118-055118-6
Hauptverfasser: Wang, Nuo, Li, Lei, Chen, Yulong, Wang, Hongyu, Yang, Zheming, Long, Dingyu
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 055118-6
container_issue 5
container_start_page 055118
container_title AIP advances
container_volume 13
creator Wang, Nuo
Li, Lei
Chen, Yulong
Wang, Hongyu
Yang, Zheming
Long, Dingyu
description In artificial neural networks, the fourth passive element memristor can be utilized as an electronic synapse that serves as the interface between neurons. The artificial neuron composed of the memristor bridge synapse not only has the characteristics of low power consumption and high integration but also has a more simplified circuit and weight change conditions. Particularly, it has the ability of bionic intelligent information processing. This paper established two novel synaptic structures on the basis of memristor bridges (type 1 and type 2) and then synthetically analyzed how to realize the artificial neuron perceptron. Herein, the artificial synapses (type 1 and type 2) have the following characteristics: continuous changes in synaptic weights (positive, negative, and zero) and memory properties. Among them, the type 2 memristor bridge has the advantage of a wider range of weight updates for the synaptic circuit, which can realize the function of the artificial neuron perceptron with less error. This work lays the foundation for the future exploitation of artificial intelligence.
doi_str_mv 10.1063/5.0138920
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1063_5_0138920</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_b9312c86f78246dda8782ad39c897047</doaj_id><sourcerecordid>2811068560</sourcerecordid><originalsourceid>FETCH-LOGICAL-c388t-7b3afe38aec5734cdffe03ccd314adea24a6495feb32f2710e4579ef6e250dee3</originalsourceid><addsrcrecordid>eNp9kEtLAzEUhYMoWGoX_oMBVwpT85qZzE4pPgoVN7oOmeSmpLSTMUmF_nunTqmC4N3cw-Xj3MNB6JLgKcEluy2mmDBRU3yCRpQUImeUlqe_9DmaxLjC_fCaYMFH6O4FNsHF5EPWBGeWkMVdq7oImd2fICUImQrJWaedWmctbINvsw6Chi718gKdWbWOMDnsMXp_fHibPeeL16f57H6RayZEyquGKQtMKNBFxbg21gJmWhtGuDKgKFclrwsLDaOWVgQDL6oabAm0wAaAjdF88DVerWQX3EaFnfTKye-DD0u5j6nXIJuaEapFaStBeWmMEr1QhtVa1BXmVe91NXh1wX9sISa58tvQ9vElFaSvUhQl7qnrgdLBxxjAHr8SLPd9y0Ie-u7Zm4GN2iWVnG-P8KcPP6DsjP0P_uv8BfkRjdc</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2811068560</pqid></control><display><type>article</type><title>Memristor bridge synapse for better artificial neuron perceptron</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Alma/SFX Local Collection</source><source>Free Full-Text Journals in Chemistry</source><creator>Wang, Nuo ; Li, Lei ; Chen, Yulong ; Wang, Hongyu ; Yang, Zheming ; Long, Dingyu</creator><creatorcontrib>Wang, Nuo ; Li, Lei ; Chen, Yulong ; Wang, Hongyu ; Yang, Zheming ; Long, Dingyu</creatorcontrib><description>In artificial neural networks, the fourth passive element memristor can be utilized as an electronic synapse that serves as the interface between neurons. The artificial neuron composed of the memristor bridge synapse not only has the characteristics of low power consumption and high integration but also has a more simplified circuit and weight change conditions. Particularly, it has the ability of bionic intelligent information processing. This paper established two novel synaptic structures on the basis of memristor bridges (type 1 and type 2) and then synthetically analyzed how to realize the artificial neuron perceptron. Herein, the artificial synapses (type 1 and type 2) have the following characteristics: continuous changes in synaptic weights (positive, negative, and zero) and memory properties. Among them, the type 2 memristor bridge has the advantage of a wider range of weight updates for the synaptic circuit, which can realize the function of the artificial neuron perceptron with less error. This work lays the foundation for the future exploitation of artificial intelligence.</description><identifier>ISSN: 2158-3226</identifier><identifier>EISSN: 2158-3226</identifier><identifier>DOI: 10.1063/5.0138920</identifier><identifier>CODEN: AAIDBI</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Artificial intelligence ; Artificial neural networks ; Bionics ; Circuits ; Data processing ; Memristors ; Power consumption ; Synapses</subject><ispartof>AIP advances, 2023-05, Vol.13 (5), p.055118-055118-6</ispartof><rights>Author(s)</rights><rights>2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c388t-7b3afe38aec5734cdffe03ccd314adea24a6495feb32f2710e4579ef6e250dee3</cites><orcidid>0000-0002-4800-2238 ; 0000-0002-5955-8266 ; 0000-0001-5602-2151 ; 0000-0001-5624-7465</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,862,2098,27907,27908</link.rule.ids></links><search><creatorcontrib>Wang, Nuo</creatorcontrib><creatorcontrib>Li, Lei</creatorcontrib><creatorcontrib>Chen, Yulong</creatorcontrib><creatorcontrib>Wang, Hongyu</creatorcontrib><creatorcontrib>Yang, Zheming</creatorcontrib><creatorcontrib>Long, Dingyu</creatorcontrib><title>Memristor bridge synapse for better artificial neuron perceptron</title><title>AIP advances</title><description>In artificial neural networks, the fourth passive element memristor can be utilized as an electronic synapse that serves as the interface between neurons. The artificial neuron composed of the memristor bridge synapse not only has the characteristics of low power consumption and high integration but also has a more simplified circuit and weight change conditions. Particularly, it has the ability of bionic intelligent information processing. This paper established two novel synaptic structures on the basis of memristor bridges (type 1 and type 2) and then synthetically analyzed how to realize the artificial neuron perceptron. Herein, the artificial synapses (type 1 and type 2) have the following characteristics: continuous changes in synaptic weights (positive, negative, and zero) and memory properties. Among them, the type 2 memristor bridge has the advantage of a wider range of weight updates for the synaptic circuit, which can realize the function of the artificial neuron perceptron with less error. This work lays the foundation for the future exploitation of artificial intelligence.</description><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Bionics</subject><subject>Circuits</subject><subject>Data processing</subject><subject>Memristors</subject><subject>Power consumption</subject><subject>Synapses</subject><issn>2158-3226</issn><issn>2158-3226</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9kEtLAzEUhYMoWGoX_oMBVwpT85qZzE4pPgoVN7oOmeSmpLSTMUmF_nunTqmC4N3cw-Xj3MNB6JLgKcEluy2mmDBRU3yCRpQUImeUlqe_9DmaxLjC_fCaYMFH6O4FNsHF5EPWBGeWkMVdq7oImd2fICUImQrJWaedWmctbINvsw6Chi718gKdWbWOMDnsMXp_fHibPeeL16f57H6RayZEyquGKQtMKNBFxbg21gJmWhtGuDKgKFclrwsLDaOWVgQDL6oabAm0wAaAjdF88DVerWQX3EaFnfTKye-DD0u5j6nXIJuaEapFaStBeWmMEr1QhtVa1BXmVe91NXh1wX9sISa58tvQ9vElFaSvUhQl7qnrgdLBxxjAHr8SLPd9y0Ie-u7Zm4GN2iWVnG-P8KcPP6DsjP0P_uv8BfkRjdc</recordid><startdate>20230501</startdate><enddate>20230501</enddate><creator>Wang, Nuo</creator><creator>Li, Lei</creator><creator>Chen, Yulong</creator><creator>Wang, Hongyu</creator><creator>Yang, Zheming</creator><creator>Long, Dingyu</creator><general>American Institute of Physics</general><general>AIP Publishing LLC</general><scope>AJDQP</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4800-2238</orcidid><orcidid>https://orcid.org/0000-0002-5955-8266</orcidid><orcidid>https://orcid.org/0000-0001-5602-2151</orcidid><orcidid>https://orcid.org/0000-0001-5624-7465</orcidid></search><sort><creationdate>20230501</creationdate><title>Memristor bridge synapse for better artificial neuron perceptron</title><author>Wang, Nuo ; Li, Lei ; Chen, Yulong ; Wang, Hongyu ; Yang, Zheming ; Long, Dingyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c388t-7b3afe38aec5734cdffe03ccd314adea24a6495feb32f2710e4579ef6e250dee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Bionics</topic><topic>Circuits</topic><topic>Data processing</topic><topic>Memristors</topic><topic>Power consumption</topic><topic>Synapses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Nuo</creatorcontrib><creatorcontrib>Li, Lei</creatorcontrib><creatorcontrib>Chen, Yulong</creatorcontrib><creatorcontrib>Wang, Hongyu</creatorcontrib><creatorcontrib>Yang, Zheming</creatorcontrib><creatorcontrib>Long, Dingyu</creatorcontrib><collection>AIP Open Access Journals</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>AIP advances</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Nuo</au><au>Li, Lei</au><au>Chen, Yulong</au><au>Wang, Hongyu</au><au>Yang, Zheming</au><au>Long, Dingyu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Memristor bridge synapse for better artificial neuron perceptron</atitle><jtitle>AIP advances</jtitle><date>2023-05-01</date><risdate>2023</risdate><volume>13</volume><issue>5</issue><spage>055118</spage><epage>055118-6</epage><pages>055118-055118-6</pages><issn>2158-3226</issn><eissn>2158-3226</eissn><coden>AAIDBI</coden><abstract>In artificial neural networks, the fourth passive element memristor can be utilized as an electronic synapse that serves as the interface between neurons. The artificial neuron composed of the memristor bridge synapse not only has the characteristics of low power consumption and high integration but also has a more simplified circuit and weight change conditions. Particularly, it has the ability of bionic intelligent information processing. This paper established two novel synaptic structures on the basis of memristor bridges (type 1 and type 2) and then synthetically analyzed how to realize the artificial neuron perceptron. Herein, the artificial synapses (type 1 and type 2) have the following characteristics: continuous changes in synaptic weights (positive, negative, and zero) and memory properties. Among them, the type 2 memristor bridge has the advantage of a wider range of weight updates for the synaptic circuit, which can realize the function of the artificial neuron perceptron with less error. This work lays the foundation for the future exploitation of artificial intelligence.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0138920</doi><tpages>6</tpages><orcidid>https://orcid.org/0000-0002-4800-2238</orcidid><orcidid>https://orcid.org/0000-0002-5955-8266</orcidid><orcidid>https://orcid.org/0000-0001-5602-2151</orcidid><orcidid>https://orcid.org/0000-0001-5624-7465</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2158-3226
ispartof AIP advances, 2023-05, Vol.13 (5), p.055118-055118-6
issn 2158-3226
2158-3226
language eng
recordid cdi_crossref_primary_10_1063_5_0138920
source DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry
subjects Artificial intelligence
Artificial neural networks
Bionics
Circuits
Data processing
Memristors
Power consumption
Synapses
title Memristor bridge synapse for better artificial neuron perceptron
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T14%3A09%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Memristor%20bridge%20synapse%20for%20better%20artificial%20neuron%20perceptron&rft.jtitle=AIP%20advances&rft.au=Wang,%20Nuo&rft.date=2023-05-01&rft.volume=13&rft.issue=5&rft.spage=055118&rft.epage=055118-6&rft.pages=055118-055118-6&rft.issn=2158-3226&rft.eissn=2158-3226&rft.coden=AAIDBI&rft_id=info:doi/10.1063/5.0138920&rft_dat=%3Cproquest_cross%3E2811068560%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2811068560&rft_id=info:pmid/&rft_doaj_id=oai_doaj_org_article_b9312c86f78246dda8782ad39c897047&rfr_iscdi=true