{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T06:41:28Z","timestamp":1698043288126},"reference-count":3,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2007,3,21]],"date-time":"2007-03-21T00:00:00Z","timestamp":1174435200000},"content-version":"vor","delay-in-days":6988,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems & Computers in Japan"],"published-print":{"date-parts":[[1988,2]]},"abstract":"Abstract<\/jats:title>A new self\u2010organizing neural network model is proposed and evaluated for the mechanism of temporal pattern recognition of the auditory system. The model is constructed based on the hypothesis that total recognition of temporal patterns approximately of the length of words, is carried out by hierarchical identification and integration of the temporal relations of the constituent features. The model has a hierarchical structure in which short\u2010term memories storing spatial patterns, the circuits extracting temporally transient components of the pattern, and the feature detection circuits to identify spatial patterns are iteratively cascaded. After the circuit is self\u2010organized by repetitive presentation of training patterns, the model can correctly identify the training patterns and their temporally compressed and stretched patterns. It is also indicated that the short\u2010term memory function at each layer of the model is essential to the acceptance of temporally deformed patterns. Studies to expand the model to top\u2010down processing are also discussed.<\/jats:p>","DOI":"10.1002\/scj.4690190210","type":"journal-article","created":{"date-parts":[[2007,7,7]],"date-time":"2007-07-07T14:48:25Z","timestamp":1183819705000},"page":"102-109","source":"Crossref","is-referenced-by-count":1,"title":["A neural network model based on short\u2010term memories for the hierarchical recognition of temporal patterns"],"prefix":"10.1002","volume":"19","author":[{"given":"Ryoko","family":"Futami","sequence":"first","affiliation":[]},{"given":"Nozomu","family":"Hoshimiya","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2007,3,21]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00342633"},{"issue":"5","key":"e_1_2_1_3_2","first-page":"481","article-title":"A neural network model for the discrimination of spatiotemporal patterns","volume":"68","author":"Futami","year":"1985","journal-title":"Trans. I.E.C.E., Japan"},{"key":"e_1_2_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00337288"}],"container-title":["Systems and Computers in Japan"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fscj.4690190210","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/scj.4690190210","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,22]],"date-time":"2023-10-22T07:59:14Z","timestamp":1697961554000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/scj.4690190210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1988,2]]},"references-count":3,"journal-issue":{"issue":"2","published-print":{"date-parts":[[1988,2]]}},"alternative-id":["10.1002\/scj.4690190210"],"URL":"https:\/\/doi.org\/10.1002\/scj.4690190210","archive":["Portico"],"relation":{},"ISSN":["0882-1666","1520-684X"],"issn-type":[{"value":"0882-1666","type":"print"},{"value":"1520-684X","type":"electronic"}],"subject":[],"published":{"date-parts":[[1988,2]]}}}