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TÍTULO: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons  Full Text
AUTORES: Manubens Gil, Linus; Cox, Daniel N.; Hawrylycz, Michael; Zhou, Zhi; Chen, Hanbo; Ramanathan, Arvind; Liu, Xiaoxiao; Liu, Yufeng; Bria, Alessandro; Gillette, Todd; Ruan, Zongcai; Yang, Jian; Radojevic, Miroslav; Zhao, Ting; Cheng, Li; Qu, Lei; Liu, Siqi; Bouchard, Kristofer E.; Gu, Lin; Cai, Weidong; Ji, Shuiwang; Roysam, Badrinath; Wang, Ching Wei; Yu, Hongchuan; Sironi, Amos; Iascone, Daniel Maxim; Zhou, Jie; Bas, Erhan; Conde Sousa, Eduardo; Aguiar, Paulo; Li, Xiang; Li, Yujie; Nanda, Sumit; Wang, Yuan; Muresan, Leila; Fua, Pascal; Ye, Bing; He, Hai yan; Staiger, Jochen F.; Peter, Manuel; Simonneau, Michel; Oberlaender, Marcel; Jefferis, Gregory; Ito, Kei; Gonzalez Bellido, Paloma; Kim, Jinhyun; Rubel, Edwin; Cline, Hollis T.; Zeng, Hongkui; Nern, Aljoscha; Chiang, Ann Shyn; Yao, Jianhua; Roskams, Jane; Livesey, Rick; Stevens, Janine; Liu, Tianming; Dang, Chinh; Guo, Yike; Zhong, Ning; Tourassi, Georgia; Hill, Sean; Koch, Christof; Meijering, Erik; Ascoli, Giorgio A.; Peng, Hanchuan; ...Mais
PUBLICAÇÃO: 2024, FONTE: 32nd Annual Computational Neuroscience Meeting (CNS) in JOURNAL OF COMPUTATIONAL NEUROSCIENCE, VOLUME: 52
INDEXADO EM: WOS