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Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks

文献类型：期刊论文

作者Shen, Guobin1,3; Zhao, Dongcheng1; Zeng, Yi1,2,3
刊名NEURAL NETWORKS
出版日期2024-02-01
卷号170页码:190-201
ISSN号0893-6080
关键词Dendritic Nonlinearity Dendritic Spatial Gating Module Dendritic Temporal Adjust Module Spiking Neural Networks
DOI10.1016/j.neunet.2023.10.056
通讯作者Zeng, Yi(yi.zeng@ia.ac.cn)
英文摘要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.
WOS关键词NEURONS
资助项目National Key Research and Devel-opment Program[2020AAA0107800]
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:001121849100001
资助机构National Key Research and Devel-opment Program
源URL[http://ir.ia.ac.cn/handle/173211/55036]  
专题脑图谱与类脑智能实验室
通讯作者Zeng, Yi
作者单位1.Chinese Acad Sci, Inst Automat, Brain Inspired Cognit Intelligence Lab, Beijing 100190, Peoples R China
2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
3.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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