A brain-inspired robot pain model based on a spiking neural network
文献类型：期刊论文
| 作者 | Feng, Hui3,4 ; Zeng, Yi1,2,3,4
|
| 刊名 | FRONTIERS IN NEUROROBOTICS
 |
| 出版日期 | 2022-12-20 |
| 卷号 | 16页码:13 |
| 关键词 | brain-inspired intelligent robot robot pain spiking neural network free energy principle spike-time-dependent-plasticity |
| ISSN号 | 1662-5218 |
| DOI | 10.3389/fnbot.2022.1025338 |
| 通讯作者 | Zeng, Yi(yi.zeng@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | FREE-ENERGY PRINCIPLE ; ANTERIOR CINGULATE ; EXPECTANCY |
| WOS研究方向 | Computer Science ; Robotics ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000905739000001 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51099]  |
| 专题 | 类脑智能研究中心_类脑认知计算 |
| 通讯作者 | Zeng, Yi |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Brain inspired Cognit Intelligence Lab, Beijing, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。