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A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule | |
Hao, Yunzhe1,2; Huang, Xuhui1; Dong, Meng1; Xu, Bo1,2,3 | |
发表期刊 | NEURAL NETWORKS |
ISSN | 0893-6080 |
2020 | |
卷号 | 121期号:2页码:387-395 |
摘要 | Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance. Hence, biologically plausible (bio-plausible) high-performance supervised learning (SL) methods for SNNs remain deficient. In this paper, we proposed a novel bioplausible SNN model for SL based on the symmetric spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset). To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability. Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance. As the learning rules were bio-plausible and based purely on local spike events, our model could be easily applied to neuromorphic hardware for online training and may be helpful for understanding SL information processing at the synaptic level in biological neural systems. (C) 2019 Elsevier Ltd. All rights reserved. |
关键词 | Spiking neural networks Dopamine-modulated spike-timing dependent plasticity Pattern recognition Supervised learning Biologically plausibility |
DOI | 10.1016/j.neunet.2019.09.007 |
关键词[WOS] | TIMING-DEPENDENT PLASTICITY ; DOPAMINERGIC MODULATION ; VISUAL-CORTEX ; HOMEOSTASIS ; REWARD ; ENERGY |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070000] ; Beijing Brain Science Project, China[Z181100001518006] ; National Natural Science Foundation of China[11505283] ; National Natural Science Foundation of China[11505283] ; Beijing Brain Science Project, China[Z181100001518006] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070000] |
WOS研究方向 | Computer Science ; Neurosciences & Neurology |
WOS类目 | Computer Science, Artificial Intelligence ; Neurosciences |
WOS记录号 | WOS:000500922700030 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
七大方向——子方向分类 | 类脑模型与计算 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29428 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 复杂系统认知与决策实验室_听觉模型与认知计算 |
通讯作者 | Huang, Xuhui; Xu, Bo |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R 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 | Hao, Yunzhe,Huang, Xuhui,Dong, Meng,et al. A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule[J]. NEURAL NETWORKS,2020,121(2):387-395. |
APA | Hao, Yunzhe,Huang, Xuhui,Dong, Meng,&Xu, Bo.(2020).A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule.NEURAL NETWORKS,121(2),387-395. |
MLA | Hao, Yunzhe,et al."A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule".NEURAL NETWORKS 121.2(2020):387-395. |
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