Spike Attention Coding for Spiking Neural Networks
文献类型：期刊论文
| 作者 | Liu, Jiawen2; Hu, Yifan2; Li, Guoqi3 ; Pei, Jing2; Deng, Lei1,2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2023-09-11 |
| 页码 | 7 |
| ISSN号 | 2162-237X |
| 关键词 | Rate coding spike attention coding (SAC) spiking neural networks (SNNs) temporal coding |
| DOI | 10.1109/TNNLS.2023.3310263 |
| 通讯作者 | Deng, Lei(leideng@mail.tsinghua.edu.cn) |
| 英文摘要 | Spiking neural networks (SNNs), an important family of neuroscience-oriented intelligent models, play an essential role in the neuromorphic computing community. Spike rate coding and temporal coding are the mainstream coding schemes in the current modeling of SNNs. However, rate coding usually suffers from limited representation resolution and long latency, while temporal coding usually suffers from under-utilization of spike activities. To this end, we propose spike attention coding (SAC) for SNNs. By introducing learnable attention coefficients for each time step, our coding scheme can naturally unify rate coding and temporal coding, and then flexibly learn optimal coefficients for better performance. Several normalization and regularization techniques are further incorporated to control the range and distribution of the learned attention coefficients. Extensive experiments on classification, generation, and regression tasks are conducted and demonstrate the superiority of the proposed coding scheme. This work provides a flexible coding scheme to enhance the representation power of SNNs and extends their application scope beyond the mainstream classification scenario. |
| WOS关键词 | REPRESENTATION |
| 资助项目 | Science and Technology Innovation 2030-New Generation of Artificial Intelligence,China Project ; National Natural Science Foundation of China[2020AAA0109100] ; National Natural Science Foundation of China[62106119] ; Chinese Institute for Brain Research, Beijing ; [62276151] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001068981500001 |
| 资助机构 | Science and Technology Innovation 2030-New Generation of Artificial Intelligence,China Project ; National Natural Science Foundation of China ; Chinese Institute for Brain Research, Beijing |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53122]  |
| 专题 | 脑图谱与类脑智能实验室 |
| 通讯作者 | Deng, Lei |
| 作者单位 | 1.Chinese Inst Brain Res, Beijing 102206, Peoples R China 2.Tsinghua Univ, Ctr Brain Inspired Comp Res, Dept Precis Instrument, Beijing 100084, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100098, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jiawen,Hu, Yifan,Li, Guoqi,et al. Spike Attention Coding for Spiking Neural Networks[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:7. |
| APA | Liu, Jiawen,Hu, Yifan,Li, Guoqi,Pei, Jing,&Deng, Lei.(2023).Spike Attention Coding for Spiking Neural Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,7. |
| MLA | Liu, Jiawen,et al."Spike Attention Coding for Spiking Neural Networks".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):7. |
入库方式： OAI收割
来源：自动化研究所
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