Short term memory for bipolar temporal patterns
Summary form only given. A study of the short-term memory requirements of temporal pattern recognition prompts the creation of a new model for neural computation. It is hypothesized that neural responses resemble hysteresis loops, instead of the simple sigmoid. The upper and lower halves of the hyst...
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| creator | Tom, M.D. Tenorio, M.F. |
| description | Summary form only given. A study of the short-term memory requirements of temporal pattern recognition prompts the creation of a new model for neural computation. It is hypothesized that neural responses resemble hysteresis loops, instead of the simple sigmoid. The upper and lower halves of the hysteresis loop are described by two equations. Generalizing the two equations to two families of curves accommodates loops of various sizes. It is conjectured that this unit is capable of memorizing the entire history of its inputs.< > |
| doi_str_mv | 10.1109/IJCNN.1991.155659 |
| format | Conference Proceeding |
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A study of the short-term memory requirements of temporal pattern recognition prompts the creation of a new model for neural computation. It is hypothesized that neural responses resemble hysteresis loops, instead of the simple sigmoid. The upper and lower halves of the hysteresis loop are described by two equations. Generalizing the two equations to two families of curves accommodates loops of various sizes. It is conjectured that this unit is capable of memorizing the entire history of its inputs.< ></description><identifier>ISBN: 0780301641</identifier><identifier>ISBN: 9780780301641</identifier><identifier>DOI: 10.1109/IJCNN.1991.155659</identifier><language>eng</language><publisher>IEEE</publisher><subject>Computational modeling ; Concurrent computing ; Distributed computing ; Equations ; History ; Hysteresis ; Laboratories ; Neurons ; Pattern recognition</subject><ispartof>IJCNN-91-Seattle International Joint Conference on Neural Networks, 1991, Vol.ii, p.992 vol.2</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/155659$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,4035,4036,27904,54899</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/155659$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tom, M.D.</creatorcontrib><creatorcontrib>Tenorio, M.F.</creatorcontrib><title>Short term memory for bipolar temporal patterns</title><title>IJCNN-91-Seattle International Joint Conference on Neural Networks</title><addtitle>IJCNN</addtitle><description>Summary form only given. A study of the short-term memory requirements of temporal pattern recognition prompts the creation of a new model for neural computation. It is hypothesized that neural responses resemble hysteresis loops, instead of the simple sigmoid. The upper and lower halves of the hysteresis loop are described by two equations. Generalizing the two equations to two families of curves accommodates loops of various sizes. It is conjectured that this unit is capable of memorizing the entire history of its inputs.< ></description><subject>Computational modeling</subject><subject>Concurrent computing</subject><subject>Distributed computing</subject><subject>Equations</subject><subject>History</subject><subject>Hysteresis</subject><subject>Laboratories</subject><subject>Neurons</subject><subject>Pattern recognition</subject><isbn>0780301641</isbn><isbn>9780780301641</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1991</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpjYJA0NNAzNDSw1Pf0cvbz0zO0tDTUMzQ1NTO1ZGbgMjC3MDA2MDQzMeRg4C0uzjIAAhNTAzNTC04G_eCM_KIShZLUolyF3NTc_KJKhbT8IoWkzIL8nMQioHhuQX5RYo5CQWIJUE1eMQ8Da1piTnEqL5TmZpBycw1x9tDNTE1NjS8oysxNLKqMh1htjFcSAIIEMpc</recordid><startdate>1991</startdate><enddate>1991</enddate><creator>Tom, M.D.</creator><creator>Tenorio, M.F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1991</creationdate><title>Short term memory for bipolar temporal patterns</title><author>Tom, M.D. ; Tenorio, M.F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_1556593</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1991</creationdate><topic>Computational modeling</topic><topic>Concurrent computing</topic><topic>Distributed computing</topic><topic>Equations</topic><topic>History</topic><topic>Hysteresis</topic><topic>Laboratories</topic><topic>Neurons</topic><topic>Pattern recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Tom, M.D.</creatorcontrib><creatorcontrib>Tenorio, M.F.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tom, M.D.</au><au>Tenorio, M.F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Short term memory for bipolar temporal patterns</atitle><btitle>IJCNN-91-Seattle International Joint Conference on Neural Networks</btitle><stitle>IJCNN</stitle><date>1991</date><risdate>1991</risdate><volume>ii</volume><spage>992 vol.2</spage><pages>992 vol.2-</pages><isbn>0780301641</isbn><isbn>9780780301641</isbn><abstract>Summary form only given. A study of the short-term memory requirements of temporal pattern recognition prompts the creation of a new model for neural computation. It is hypothesized that neural responses resemble hysteresis loops, instead of the simple sigmoid. The upper and lower halves of the hysteresis loop are described by two equations. Generalizing the two equations to two families of curves accommodates loops of various sizes. It is conjectured that this unit is capable of memorizing the entire history of its inputs.< ></abstract><pub>IEEE</pub><doi>10.1109/IJCNN.1991.155659</doi></addata></record> |
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| identifier | ISBN: 0780301641 |
| ispartof | IJCNN-91-Seattle International Joint Conference on Neural Networks, 1991, Vol.ii, p.992 vol.2 |
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| language | eng |
| recordid | cdi_ieee_primary_155659 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computational modeling Concurrent computing Distributed computing Equations History Hysteresis Laboratories Neurons Pattern recognition |
| title | Short term memory for bipolar temporal patterns |
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