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Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks | |
Shen, Guobin1,3; Zhao, Dongcheng1![]() ![]() | |
发表期刊 | Neural Networks
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ISSN | 0893-6080 |
2024-02-01 | |
卷号 | 170页码:190-201 |
摘要 | Inspired by the information transmission process in the brain, Spiking Neural Networks (SNNs) have gained considerable attention due to their event-driven nature. However, as the network structure grows complex, managing the spiking behavior within the network becomes challenging. Networks with excessively dense or sparse spikes fail to transmit sufficient information, inhibiting SNNs from exhibiting superior performance. Current SNNs linearly sum presynaptic information in postsynaptic neurons, overlooking the adaptive adjust-ment effect of dendrites on information processing. In this study, we introduce the Dendritic Spatial Gating Module (DSGM), which scales and translates the input, reducing the loss incurred when transforming the continuous membrane potential into discrete spikes. Simultaneously, by implementing the Dendritic Temporal Adjust Module (DTAM), dendrites assign different importance to inputs of different time steps, facilitating the establishment of the temporal dependency of spiking neurons and effectively integrating multi-step time information. The fusion of these two modules results in a more balanced spike representation within the network, significantly enhancing the neural network's performance. This approach has achieved state-of-the -art performance on static image datasets, including CIFAR10 and CIFAR100, as well as event datasets like DVS-CIFAR10, DVS-Gesture, and N-Caltech101. It also demonstrates competitive performance compared to the current state-of-the-art on the ImageNet dataset. |
关键词 | Dendritic Nonlinearity Dendritic Spatial Gating Module Dendritic Temporal Adjust Module Spiking Neural Networks |
DOI | 10.1016/j.neunet.2023.10.056 |
关键词[WOS] | NEURONS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Devel-opment Program[2020AAA0107800] |
项目资助者 | National Key Research and Devel-opment Program |
WOS研究方向 | Computer Science ; Neurosciences & Neurology |
WOS类目 | Computer Science, Artificial Intelligence ; Neurosciences |
WOS记录号 | WOS:001121849100001 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
七大方向——子方向分类 | 类脑模型与计算 |
国重实验室规划方向分类 | 认知机理与类脑学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/55037 |
专题 | 脑图谱与类脑智能实验室 |
通讯作者 | Zeng, Yi |
作者单位 | 1.Brain-Inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China 2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences (CAS), Shanghai, China 3.School of Future Technology, University of Chinese Academy of Sciences, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Shen, Guobin,Zhao, Dongcheng,Zeng, Yi. Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks[J]. Neural Networks,2024,170:190-201. |
APA | Shen, Guobin,Zhao, Dongcheng,&Zeng, Yi.(2024).Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks.Neural Networks,170,190-201. |
MLA | Shen, Guobin,et al."Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks".Neural Networks 170(2024):190-201. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
NN_STDentric.pdf(2752KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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