{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T05:40:24Z","timestamp":1698471624323},"reference-count":8,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2007,3,21]],"date-time":"2007-03-21T00:00:00Z","timestamp":1174435200000},"content-version":"vor","delay-in-days":4097,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems &amp; Computers in Japan"],"published-print":{"date-parts":[[1996,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Hebbian rule might be the most popular one as an unsupervised learning model of neural nets. Recently, the opposite of the Hebbian rule, i.e., the so\u2010called anti\u2010Hebbian rule, has drawn attention as a new learning paradigm. This paper first clarifies some fundamental properties of the anti\u2010Hebbian rule, and then shows that a variety of networks can be acquired by some anti\u2010Hebbian rules.<\/jats:p>","DOI":"10.1002\/scj.4690270308","type":"journal-article","created":{"date-parts":[[2007,7,8]],"date-time":"2007-07-08T10:26:02Z","timestamp":1183890362000},"page":"84-93","source":"Crossref","is-referenced-by-count":1,"title":["The learning of linear neural nets with anti\u2010hebbian rules"],"prefix":"10.1002","volume":"27","author":[{"given":"Kiyotoshi","family":"Matsuoka","sequence":"first","affiliation":[]},{"given":"Mitsuru","family":"Kawamoto","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2007,3,21]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"crossref","unstructured":"P.Foldi\u00e1k.Adaptive network for optimal linear feature extraction. Proceedings of the International Joint Conference on Neural Networks Washington D.C. I pp.401\u2013405(1989).","DOI":"10.1109\/IJCNN.1989.118615"},{"key":"e_1_2_1_3_2","doi-asserted-by":"crossref","unstructured":"T. K.Leen.Dynamics of learning in linear feature\u2010discovery networks. Network 2 pp.85\u2013105(1990).","DOI":"10.1088\/0954-898X_2_1_005"},{"key":"e_1_2_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/21.97460"},{"key":"e_1_2_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(94)90097-3"},{"key":"e_1_2_1_6_2","volume-title":"Self\u2010organization and associative memory","author":"Kohonen T.","year":"1984"},{"key":"e_1_2_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1684(91)90079-X"},{"key":"e_1_2_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(94)00083-X"},{"key":"e_1_2_1_9_2","volume-title":"Theory of Neural Networks","author":"Amari S.","year":"1978"}],"container-title":["Systems and Computers in Japan"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fscj.4690270308","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/scj.4690270308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T05:36:12Z","timestamp":1698384972000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/scj.4690270308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1996,1]]},"references-count":8,"journal-issue":{"issue":"3","published-print":{"date-parts":[[1996,1]]}},"alternative-id":["10.1002\/scj.4690270308"],"URL":"https:\/\/doi.org\/10.1002\/scj.4690270308","archive":["Portico"],"relation":{},"ISSN":["0882-1666","1520-684X"],"issn-type":[{"value":"0882-1666","type":"print"},{"value":"1520-684X","type":"electronic"}],"subject":[],"published":{"date-parts":[[1996,1]]}}}