{"title":"认识作者彭汉川、谢鹏和熊峰","authors":"Hanchuan Peng, Peng Xie, Feng Xiong","doi":"10.1016/j.patter.2023.100912","DOIUrl":null,"url":null,"abstract":"<p>In a recent paper at <em>Patterns</em>, Hanchuan Peng, Peng Xie, and Feng Xiong from Southeast University describe a deep learning method to characterize complete single-neuron morphologies, which can discover neuron projection patterns of diverse cells and learn neuronal morphology representation. In this interview, the authors shared the story behind the paper and their research experience.</p><p>This interview is a companion to these authors’ recent paper, “DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.”<span><sup>1</sup></span></p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Meet the authors: Hanchuan Peng, Peng Xie, and Feng Xiong\",\"authors\":\"Hanchuan Peng, Peng Xie, Feng Xiong\",\"doi\":\"10.1016/j.patter.2023.100912\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In a recent paper at <em>Patterns</em>, Hanchuan Peng, Peng Xie, and Feng Xiong from Southeast University describe a deep learning method to characterize complete single-neuron morphologies, which can discover neuron projection patterns of diverse cells and learn neuronal morphology representation. In this interview, the authors shared the story behind the paper and their research experience.</p><p>This interview is a companion to these authors’ recent paper, “DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.”<span><sup>1</sup></span></p>\",\"PeriodicalId\":36242,\"journal\":{\"name\":\"Patterns\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Patterns\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.patter.2023.100912\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patterns","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.patter.2023.100912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Meet the authors: Hanchuan Peng, Peng Xie, and Feng Xiong
In a recent paper at Patterns, Hanchuan Peng, Peng Xie, and Feng Xiong from Southeast University describe a deep learning method to characterize complete single-neuron morphologies, which can discover neuron projection patterns of diverse cells and learn neuronal morphology representation. In this interview, the authors shared the story behind the paper and their research experience.
This interview is a companion to these authors’ recent paper, “DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.”1
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