{"title":"时间敏感神经网络","authors":"I.L. Davis, P. A. Sandon","doi":"10.1109/IJCNN.1991.170698","DOIUrl":null,"url":null,"abstract":"The problem of recognizing rhythmic patterns characterized by a periodically repeating sequence of events is addressed. An approach to representing temporal information in neural networks and an application that makes use of this representation are described. The Tempnet rhythm system is a particular instantiation of these ideas. It is used to demonstrate the use of temporal representation in the processing of temporal signals. Decaying node activations are used to represent the timing of specific temporal events. This approach was demonstrated in a system for categorizing periodically repeating patterns, independent of time scale. The network simulator is described, along with the results of some sample training and performance runs.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Temporally sensitive neural networks\",\"authors\":\"I.L. Davis, P. A. Sandon\",\"doi\":\"10.1109/IJCNN.1991.170698\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of recognizing rhythmic patterns characterized by a periodically repeating sequence of events is addressed. An approach to representing temporal information in neural networks and an application that makes use of this representation are described. The Tempnet rhythm system is a particular instantiation of these ideas. It is used to demonstrate the use of temporal representation in the processing of temporal signals. Decaying node activations are used to represent the timing of specific temporal events. This approach was demonstrated in a system for categorizing periodically repeating patterns, independent of time scale. The network simulator is described, along with the results of some sample training and performance runs.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170698\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170698","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The problem of recognizing rhythmic patterns characterized by a periodically repeating sequence of events is addressed. An approach to representing temporal information in neural networks and an application that makes use of this representation are described. The Tempnet rhythm system is a particular instantiation of these ideas. It is used to demonstrate the use of temporal representation in the processing of temporal signals. Decaying node activations are used to represent the timing of specific temporal events. This approach was demonstrated in a system for categorizing periodically repeating patterns, independent of time scale. The network simulator is described, along with the results of some sample training and performance runs.<>