An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections

doi:10.1016/j.neunet.2023.06.019

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections
	Dong, Yiting 3,5; Zhao, Dongcheng 3; Li, Yang 3,4; Zeng, Yi 1,2,3,4,5
刊名	NEURAL NETWORKS
	2023-08-01
卷号	165 页码:799-808
关键词	Spiking neural network Unsupervised Plasticity learning rule Brain inspired connection
ISSN号	0893-6080
DOI	10.1016/j.neunet.2023.06.019
通讯作者	Zeng, Yi(yi.zeng@ia.ac.cn)
英文摘要	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/).
资助项目	National Key Research and De- velopment Program[XDB32070100] ; Strategic Priority Research Program of the Chinese Academy of Sciences ; [2020AAA0107800]
WOS关键词	LATERAL-INHIBITION ; PLASTICITY ; NEURONS
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:001057996700001
资助机构	National Key Research and De- velopment Program ; Strategic Priority Research Program of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54107]
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.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artifcial Intelligence Sy, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Automat, Brain Inspired Cognit Intelligence Lab, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
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.