Brain-inspired global-local learning incorporated with neuromorphic computing
Global and local learning represent two distinct approaches to artificial intelligence. In this manuscript, Wu et al present a hybrid learning strategy, drawing from elements of both approaches, and implement it on a co-designed neuromorphic platform.
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-27653-2 |
| _version_ | 1798026458054000640 |
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| author | Yujie Wu Rong Zhao Jun Zhu Feng Chen Mingkun Xu Guoqi Li Sen Song Lei Deng Guanrui Wang Hao Zheng Songchen Ma Jing Pei Youhui Zhang Mingguo Zhao Luping Shi |
| author_facet | Yujie Wu Rong Zhao Jun Zhu Feng Chen Mingkun Xu Guoqi Li Sen Song Lei Deng Guanrui Wang Hao Zheng Songchen Ma Jing Pei Youhui Zhang Mingguo Zhao Luping Shi |
| author_sort | Yujie Wu |
| collection | DOAJ |
| description | Global and local learning represent two distinct approaches to artificial intelligence. In this manuscript, Wu et al present a hybrid learning strategy, drawing from elements of both approaches, and implement it on a co-designed neuromorphic platform. |
| first_indexed | 2024-04-11T18:35:51Z |
| format | Article |
| id | doaj.art-c636786e6a2c4ad791c5928d459e9941 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-11T18:35:51Z |
| publishDate | 2022-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-c636786e6a2c4ad791c5928d459e99412022-12-22T04:09:17ZengNature PortfolioNature Communications2041-17232022-01-0113111410.1038/s41467-021-27653-2Brain-inspired global-local learning incorporated with neuromorphic computingYujie Wu0Rong Zhao1Jun Zhu2Feng Chen3Mingkun Xu4Guoqi Li5Sen Song6Lei Deng7Guanrui Wang8Hao Zheng9Songchen Ma10Jing Pei11Youhui Zhang12Mingguo Zhao13Luping Shi14Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityDepartment of Computer Science and Technology, Tsinghua UniversityDepartment of Automation, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityLaboratory of Brain and Intelligence, Department of Biomedical Engineering, IDG/ McGovern Institute for Brain Research, CBICR, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityDepartment of Computer Science and Technology, Tsinghua UniversityDepartment of Automation, Tsinghua UniversityDepartment of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua UniversityGlobal and local learning represent two distinct approaches to artificial intelligence. In this manuscript, Wu et al present a hybrid learning strategy, drawing from elements of both approaches, and implement it on a co-designed neuromorphic platform.https://doi.org/10.1038/s41467-021-27653-2 |
| spellingShingle | Yujie Wu Rong Zhao Jun Zhu Feng Chen Mingkun Xu Guoqi Li Sen Song Lei Deng Guanrui Wang Hao Zheng Songchen Ma Jing Pei Youhui Zhang Mingguo Zhao Luping Shi Brain-inspired global-local learning incorporated with neuromorphic computing Nature Communications |
| title | Brain-inspired global-local learning incorporated with neuromorphic computing |
| title_full | Brain-inspired global-local learning incorporated with neuromorphic computing |
| title_fullStr | Brain-inspired global-local learning incorporated with neuromorphic computing |
| title_full_unstemmed | Brain-inspired global-local learning incorporated with neuromorphic computing |
| title_short | Brain-inspired global-local learning incorporated with neuromorphic computing |
| title_sort | brain inspired global local learning incorporated with neuromorphic computing |
| url | https://doi.org/10.1038/s41467-021-27653-2 |
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