Nature Communications (Jan 2022)
Brain-inspired global-local learning incorporated with neuromorphic computing
- 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
Affiliations
- Yujie Wu
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Rong Zhao
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Jun Zhu
- Department of Computer Science and Technology, Tsinghua University
- Feng Chen
- Department of Automation, Tsinghua University
- Mingkun Xu
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Guoqi Li
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Sen Song
- Laboratory of Brain and Intelligence, Department of Biomedical Engineering, IDG/ McGovern Institute for Brain Research, CBICR, Tsinghua University
- Lei Deng
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Guanrui Wang
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Hao Zheng
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Songchen Ma
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Jing Pei
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- Youhui Zhang
- Department of Computer Science and Technology, Tsinghua University
- Mingguo Zhao
- Department of Automation, Tsinghua University
- Luping Shi
- Department of Precision Instrument, Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, Tsinghua University
- DOI
- https://doi.org/10.1038/s41467-021-27653-2
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 14
Abstract
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.
