Memristor-based neural circuits
Biological neural systems use self- reconfigurable and self-learning primitive elements (synapses) to extract relevant information from complex and noisy environments, to detect specific spatio-temporal patterns in the data of interest and to compute and simultaneously store some significant feature...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 420 |
|---|---|
| container_issue | |
| container_start_page | 417 |
| container_title | |
| container_volume | |
| creator | Corinto, Fernando Ascoli, Alon Sung-Mo Kang |
| description | Biological neural systems use self- reconfigurable and self-learning primitive elements (synapses) to extract relevant information from complex and noisy environments, to detect specific spatio-temporal patterns in the data of interest and to compute and simultaneously store some significant features. All these desirable attributes may be realized by using two-terminal elements, memristors (memory resistors), which most closely resemble biological synapses. This article is organized according to the rule of the ISCAS2013 special session having the same title. We present a short summary of the state-of-the-art of memristor theory and Hodgkin-Huxley neural model. In addition, we briefly introduce a comprehensive nonlinear circuit-theoretic foundation for a novel circuit implementation of the Hodgkin-Huxley neural model with memristors. |
| doi_str_mv | 10.1109/ISCAS.2013.6571869 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6571869</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6571869</ieee_id><sourcerecordid>6571869</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-b4fbe4d90ac7b9b6ae9ffd9f13c1c66b06ca79d7f23eb254b559a7330b2891f53</originalsourceid><addsrcrecordid>eNpVj8tKAzEUQOMLHOr8gC7sD2S8N5nkzl2WwUeh4qK6LkkmgUirkrQL_17BblydxYEDR4hrhA4R-G65HhfrTgHqzhrCwfKJaJkG7C1pQxbtqWgUmkGiUebsnwM-Fw0oQtlrUJeirfUdAH67FhU14vY57kqu-88ivatxmn_EQ3HbecglHPK-XomL5LY1tkfOxNvD_ev4JFcvj8txsZIZyeyl75OP_cTgAnn21kVOaeKEOmCw1oMNjniipHT0yvTeGHakNXg1MCajZ-Lmr5tjjJuvkneufG-Ou_oHZOtDwA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Memristor-based neural circuits</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Corinto, Fernando ; Ascoli, Alon ; Sung-Mo Kang</creator><creatorcontrib>Corinto, Fernando ; Ascoli, Alon ; Sung-Mo Kang</creatorcontrib><description>Biological neural systems use self- reconfigurable and self-learning primitive elements (synapses) to extract relevant information from complex and noisy environments, to detect specific spatio-temporal patterns in the data of interest and to compute and simultaneously store some significant features. All these desirable attributes may be realized by using two-terminal elements, memristors (memory resistors), which most closely resemble biological synapses. This article is organized according to the rule of the ISCAS2013 special session having the same title. We present a short summary of the state-of-the-art of memristor theory and Hodgkin-Huxley neural model. In addition, we briefly introduce a comprehensive nonlinear circuit-theoretic foundation for a novel circuit implementation of the Hodgkin-Huxley neural model with memristors.</description><identifier>ISSN: 0271-4302</identifier><identifier>ISBN: 9781467357609</identifier><identifier>ISBN: 146735760X</identifier><identifier>EISSN: 2158-1525</identifier><identifier>EISBN: 9781467357616</identifier><identifier>EISBN: 1467357618</identifier><identifier>EISBN: 9781467357623</identifier><identifier>EISBN: 1467357626</identifier><identifier>DOI: 10.1109/ISCAS.2013.6571869</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biomembranes ; Equations ; Integrated circuit modeling ; Memristors ; Nerve fibers ; Nonlinear dynamical systems ; RLC circuits</subject><ispartof>2013 IEEE International Symposium on Circuits and Systems (ISCAS), 2013, p.417-420</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6571869$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6571869$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Corinto, Fernando</creatorcontrib><creatorcontrib>Ascoli, Alon</creatorcontrib><creatorcontrib>Sung-Mo Kang</creatorcontrib><title>Memristor-based neural circuits</title><title>2013 IEEE International Symposium on Circuits and Systems (ISCAS)</title><addtitle>ISCAS</addtitle><description>Biological neural systems use self- reconfigurable and self-learning primitive elements (synapses) to extract relevant information from complex and noisy environments, to detect specific spatio-temporal patterns in the data of interest and to compute and simultaneously store some significant features. All these desirable attributes may be realized by using two-terminal elements, memristors (memory resistors), which most closely resemble biological synapses. This article is organized according to the rule of the ISCAS2013 special session having the same title. We present a short summary of the state-of-the-art of memristor theory and Hodgkin-Huxley neural model. In addition, we briefly introduce a comprehensive nonlinear circuit-theoretic foundation for a novel circuit implementation of the Hodgkin-Huxley neural model with memristors.</description><subject>Biomembranes</subject><subject>Equations</subject><subject>Integrated circuit modeling</subject><subject>Memristors</subject><subject>Nerve fibers</subject><subject>Nonlinear dynamical systems</subject><subject>RLC circuits</subject><issn>0271-4302</issn><issn>2158-1525</issn><isbn>9781467357609</isbn><isbn>146735760X</isbn><isbn>9781467357616</isbn><isbn>1467357618</isbn><isbn>9781467357623</isbn><isbn>1467357626</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVj8tKAzEUQOMLHOr8gC7sD2S8N5nkzl2WwUeh4qK6LkkmgUirkrQL_17BblydxYEDR4hrhA4R-G65HhfrTgHqzhrCwfKJaJkG7C1pQxbtqWgUmkGiUebsnwM-Fw0oQtlrUJeirfUdAH67FhU14vY57kqu-88ivatxmn_EQ3HbecglHPK-XomL5LY1tkfOxNvD_ev4JFcvj8txsZIZyeyl75OP_cTgAnn21kVOaeKEOmCw1oMNjniipHT0yvTeGHakNXg1MCajZ-Lmr5tjjJuvkneufG-Ou_oHZOtDwA</recordid><startdate>201305</startdate><enddate>201305</enddate><creator>Corinto, Fernando</creator><creator>Ascoli, Alon</creator><creator>Sung-Mo Kang</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201305</creationdate><title>Memristor-based neural circuits</title><author>Corinto, Fernando ; Ascoli, Alon ; Sung-Mo Kang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b4fbe4d90ac7b9b6ae9ffd9f13c1c66b06ca79d7f23eb254b559a7330b2891f53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Biomembranes</topic><topic>Equations</topic><topic>Integrated circuit modeling</topic><topic>Memristors</topic><topic>Nerve fibers</topic><topic>Nonlinear dynamical systems</topic><topic>RLC circuits</topic><toplevel>online_resources</toplevel><creatorcontrib>Corinto, Fernando</creatorcontrib><creatorcontrib>Ascoli, Alon</creatorcontrib><creatorcontrib>Sung-Mo Kang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Corinto, Fernando</au><au>Ascoli, Alon</au><au>Sung-Mo Kang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Memristor-based neural circuits</atitle><btitle>2013 IEEE International Symposium on Circuits and Systems (ISCAS)</btitle><stitle>ISCAS</stitle><date>2013-05</date><risdate>2013</risdate><spage>417</spage><epage>420</epage><pages>417-420</pages><issn>0271-4302</issn><eissn>2158-1525</eissn><isbn>9781467357609</isbn><isbn>146735760X</isbn><eisbn>9781467357616</eisbn><eisbn>1467357618</eisbn><eisbn>9781467357623</eisbn><eisbn>1467357626</eisbn><abstract>Biological neural systems use self- reconfigurable and self-learning primitive elements (synapses) to extract relevant information from complex and noisy environments, to detect specific spatio-temporal patterns in the data of interest and to compute and simultaneously store some significant features. All these desirable attributes may be realized by using two-terminal elements, memristors (memory resistors), which most closely resemble biological synapses. This article is organized according to the rule of the ISCAS2013 special session having the same title. We present a short summary of the state-of-the-art of memristor theory and Hodgkin-Huxley neural model. In addition, we briefly introduce a comprehensive nonlinear circuit-theoretic foundation for a novel circuit implementation of the Hodgkin-Huxley neural model with memristors.</abstract><pub>IEEE</pub><doi>10.1109/ISCAS.2013.6571869</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0271-4302 |
| ispartof | 2013 IEEE International Symposium on Circuits and Systems (ISCAS), 2013, p.417-420 |
| issn | 0271-4302 2158-1525 |
| language | eng |
| recordid | cdi_ieee_primary_6571869 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biomembranes Equations Integrated circuit modeling Memristors Nerve fibers Nonlinear dynamical systems RLC circuits |
| title | Memristor-based neural circuits |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-13T02%3A56%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Memristor-based%20neural%20circuits&rft.btitle=2013%20IEEE%20International%20Symposium%20on%20Circuits%20and%20Systems%20(ISCAS)&rft.au=Corinto,%20Fernando&rft.date=2013-05&rft.spage=417&rft.epage=420&rft.pages=417-420&rft.issn=0271-4302&rft.eissn=2158-1525&rft.isbn=9781467357609&rft.isbn_list=146735760X&rft_id=info:doi/10.1109/ISCAS.2013.6571869&rft_dat=%3Cieee_6IE%3E6571869%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781467357616&rft.eisbn_list=1467357618&rft.eisbn_list=9781467357623&rft.eisbn_list=1467357626&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6571869&rfr_iscdi=true |