Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation

doi:10.1109/TNNLS.2021.3085966

	Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation
	Zhang, Tielin1,2 ; Jia, Shuncheng 1,2; Cheng, Xiang 1,2; Xu, Bo1,3,4
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2021-06-11
页码	11
通讯作者	Zhang, Tielin(tielin.zhang@ia.ac.cn) ; Xu, Bo(xubo@ia.ac.cn)
摘要	Spiking neural networks (SNNs) contain more biologically realistic structures and biologically inspired learning principles than those in standard artificial neural networks (ANNs). SNNs are considered the third generation of ANNs, powerful on the robust computation with a low computational cost. The neurons in SNNs are nondifferential, containing decayed historical states and generating event-based spikes after their states reaching the firing threshold. These dynamic characteristics of SNNs make it difficult to be directly trained with the standard backpropagation (BP), which is also considered not biologically plausible. In this article, a biologically plausible reward propagation (BRP) algorithm is proposed and applied to the SNN architecture with both spiking-convolution (with both 1-D and 2-D convolutional kernels) and full-connection layers. Unlike the standard BP that propagates error signals from postsynaptic to presynaptic neurons layer by layer, the BRP propagates target labels instead of errors directly from the output layer to all prehidden layers. This effort is more consistent with the top-down reward-guiding learning in cortical columns of the neocortex. Synaptic modifications with only local gradient differences are induced with pseudo-BP that might also be replaced with the spike-timing-dependent plasticity (STDP). The performance of the proposed BRP-SNN is further verified on the spatial (including MNIST and Cifar-10) and temporal (including TIDigits and DvsGesture) tasks, where the SNN using BRP has reached a similar accuracy compared to other state-of-the-art (SOTA) BP-based SNNs and saved 50% more computational cost than ANNs. We think that the introduction of biologically plausible learning rules to the training procedure of biologically realistic SNNs will give us more hints and inspiration toward a better understanding of the biological system's intelligent nature.
关键词	Neurons Biology Convolution Biological neural networks Tuning Membrane potentials Kernel Biologically plausible computing neuronal dynamics reward propagation spiking neural network (SNN)
DOI	10.1109/TNNLS.2021.3085966
关键词[WOS]	TIMING-DEPENDENT PLASTICITY ; BACKPROPAGATION ; NEURONS ; DYNAMICS ; MODEL
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2020AAA0104305] ; National Natural Science Foundation of China[61806195] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32070100] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010404] ; Beijing Brain Science Project[Z181100001518006]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Beijing Brain Science Project
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000733548300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	类脑模型与计算
引用统计	被引频次：24[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46858
专题	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	Zhang, Tielin; Xu, Bo
作者单位	1.Chinese Acad Sci CASIA, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, 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	Zhang, Tielin,Jia, Shuncheng,Cheng, Xiang,et al. Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:11.
APA	Zhang, Tielin,Jia, Shuncheng,Cheng, Xiang,&Xu, Bo.(2021).Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,11.
MLA	Zhang, Tielin,et al."Tuning Convolutional Spiking Neural Network With Biologically Plausible Reward Propagation".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):11.