Knowledge Commons of Institute of Automation,CAS
An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections | |
Dong, Yiting1,3; Zhao, Dongcheng3; Li, Yang2,3; Zeng, Yi1,2,3,4,5 | |
发表期刊 | Neural Networks |
ISSN | 0893-6080 |
2023 | |
卷号 | 165页码:799-808 |
摘要 | The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn. The human brain can quickly learn various conceptual knowledge in a self-organized and unsupervised manner, accomplished through coordinating various learning rules and structures in the human brain. Spiketiming-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. In this paper, taking inspiration from short-term synaptic plasticity, we design an adaptive synaptic filter and introduce the adaptive spiking threshold as the neuron plasticity to enrich the representation ability of SNNs. We also introduce an adaptive lateral inhibitory connection to adjust the spikes balance dynamically to help the network learn richer features. To speed up and stabilize the training of unsupervised spiking neural networks, we design a samples temporal batch STDP (STB-STDP), which updates weights based on multiple samples and moments. By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets. Further, we tested on the more complex CIFAR10 dataset, and the results fully illustrate the superiority of our algorithm. Our model is also the first work to apply unsupervised STDP-based SNNs to CIFAR10. At the same time, in the small-sample learning scenario, it will far exceed the supervised ANN using the same structure. & COPY; 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
关键词 | Spiking neural network Unsupervised Plasticity learning rule Brain inspired connection |
DOI | 10.1016/j.neunet.2023.06.019 |
关键词[WOS] | LATERAL-INHIBITION ; PLASTICITY ; NEURONS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and De- velopment Program[XDB32070100] ; Strategic Priority Research Program of the Chinese Academy of Sciences ; [2020AAA0107800] |
项目资助者 | National Key Research and De- velopment Program ; Strategic Priority Research Program of the Chinese Academy of Sciences |
WOS研究方向 | Computer Science ; Neurosciences & Neurology |
WOS类目 | Computer Science, Artificial Intelligence ; Neurosciences |
WOS记录号 | WOS:001057996700001 |
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/173211/54108 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Zeng, Yi |
作者单位 | 1.School of Future Technology, University of Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Brain-Inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences (CAS) 4.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences (CAS) 5.State Key Laboratory of Multimodal Artifcial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences (CAS) |
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 | Dong, Yiting,Zhao, Dongcheng,Li, Yang,et al. An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections[J]. Neural Networks,2023,165:799-808. |
APA | Dong, Yiting,Zhao, Dongcheng,Li, Yang,&Zeng, Yi.(2023).An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections.Neural Networks,165,799-808. |
MLA | Dong, Yiting,et al."An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections".Neural Networks 165(2023):799-808. |
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