{"title":"The analysis of continuous temporal sequences by a map of sequential leaky integrators","authors":"C. Privitera, P. Morasso","doi":"10.1109/ICNN.1994.374733","DOIUrl":null,"url":null,"abstract":"The problem to detect and recognize the occurrence of specific events in a continually evolving environment, is particularly important in many fields, starting from motor planning. In this paper, the authors propose a two-dimensional map, where the processing elements correspond to specific instances of leaky integrators whose parameters (or tops) are learned in a self-organizing manner: in this way the map becomes a topologic representation of temporal sequences whose presence in a continuous temporal data flow is detectable by means of the activation level of the corresponding neurons.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374733","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
The problem to detect and recognize the occurrence of specific events in a continually evolving environment, is particularly important in many fields, starting from motor planning. In this paper, the authors propose a two-dimensional map, where the processing elements correspond to specific instances of leaky integrators whose parameters (or tops) are learned in a self-organizing manner: in this way the map becomes a topologic representation of temporal sequences whose presence in a continuous temporal data flow is detectable by means of the activation level of the corresponding neurons.<>