A brain-inspired robot pain model based on a spiking neural network

doi:10.3389/fnbot.2022.1025338

	A brain-inspired robot pain model based on a spiking neural network
	Feng, Hui 1,2; Zeng, Yi1,2,3,4
发表期刊	FRONTIERS IN NEUROROBOTICS
ISSN	1662-5218
	2022-12-20
卷号	16 页码:13
通讯作者	Zeng, Yi(yi.zeng@ia.ac.cn)
摘要	IntroductionPain is a crucial function for organisms. Building a "Robot Pain" model inspired by organisms' pain could help the robot learn self-preservation and extend longevity. Most previous studies about robots and pain focus on robots interacting with people by recognizing their pain expressions or scenes, or avoiding obstacles by recognizing dangerous objects. Robots do not have human-like pain capacity and cannot adaptively respond to danger. Inspired by the evolutionary mechanisms of pain emergence and the Free Energy Principle (FEP) in the brain, we summarize the neural mechanisms of pain and construct a Brain-inspired Robot Pain Spiking Neural Network (BRP-SNN) with spike-time-dependent-plasticity (STDP) learning rule and population coding method. MethodsThe proposed model can quantify machine injury by detecting the coupling relationship between multi-modality sensory information and generating "robot pain" as an internal state. ResultsWe provide a comparative analysis with the results of neuroscience experiments, showing that our model has biological interpretability. We also successfully tested our model on two tasks with real robots-the alerting actual injury task and the preventing potential injury task. DiscussionOur work has two major contributions: (1) It has positive implications for the integration of pain concepts into robotics in the intelligent robotics field. (2) Our summary of pain's neural mechanisms and the implemented computational simulations provide a new perspective to explore the nature of pain, which has significant value for future pain research in the cognitive neuroscience field.
关键词	brain-inspired intelligent robot robot pain spiking neural network free energy principle spike-time-dependent-plasticity
DOI	10.3389/fnbot.2022.1025338
关键词[WOS]	FREE-ENERGY PRINCIPLE ; ANTERIOR CINGULATE ; EXPECTANCY
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Robotics ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS记录号	WOS:000905739000001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：2[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/173211/51100
专题	脑图谱与类脑智能实验室_类脑认知计算
通讯作者	Zeng, Yi
作者单位	1.Chinese Acad Sci, Inst Automat, Brain inspired Cognit Intelligence Lab, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, 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; 模式识别国家重点实验室
推荐引用方式 GB/T 7714	Feng, Hui,Zeng, Yi. A brain-inspired robot pain model based on a spiking neural network[J]. FRONTIERS IN NEUROROBOTICS,2022,16:13.
APA	Feng, Hui,&Zeng, Yi.(2022).A brain-inspired robot pain model based on a spiking neural network.FRONTIERS IN NEUROROBOTICS,16,13.
MLA	Feng, Hui,et al."A brain-inspired robot pain model based on a spiking neural network".FRONTIERS IN NEUROROBOTICS 16(2022):13.