Artificial intelligence is currently achieving impressive success in all fields.However,autonomous navigation remains a major challenge for AI.Reinforcement learning is used for target navigation to simulate the inter...Artificial intelligence is currently achieving impressive success in all fields.However,autonomous navigation remains a major challenge for AI.Reinforcement learning is used for target navigation to simulate the interaction between the brain and the environment at the behavioral level,but the Artificial Neural Network trained by reinforcement learning cannot match the autonomous mobility of humans and animals.The hippocampus–striatum circuits are considered as key circuits for target navigation planning and decision-making.This paper aims to construct a bionic navigation model of reinforcement learning corresponding to the nervous system to improve the autonomous navigation performance of the robot.The ventral striatum is considered to be the behavioral evaluation region,and the hippocampal–striatum circuit constitutes the position–reward association.In this paper,a set of episode cognition and reinforcement learning system simulating the mechanism of hippocampus and ventral striatum is constructed,which is used to provide target guidance for the robot to perform autonomous tasks.Compared with traditional methods,this system reflects the high efficiency of learning and better Environmental Adaptability.Our research is an exploration of the intersection and fusion of artificial intelligence and neuroscience,which is conducive to the development of artificial intelligence and the understanding of the nervous system.展开更多
The hippocampal formation of the brain contains a series of nerve cells related to environmental cognition and navigation.These cells can integrate their moment information and external perceptual information and acqu...The hippocampal formation of the brain contains a series of nerve cells related to environmental cognition and navigation.These cells can integrate their moment information and external perceptual information and acquire episodic cognitive memory.Through episodic cognition and memory,organisms can achieve autonomous navigation in complex environments.This paper mainly studies the strategy of robot episode navigation in complex environments.After exploring the environment,the robot obtains subjective environmental cognition and forms a cognition map.The grid cells information contained in the cognitive map can obtain the direction and distance of the target through vector calculation,which can get a shortcut through the inexperienced area.The synaptic connection of place cells in the cognitive map can be used as the topological relationship between episode nodes.When the target-oriented vector navigation encounters obstacles,the obstacles can be realized by setting closer sub-targets.Based on the known obstacle information obtained from boundary cells in the cognitive map,topological paths can be divided into multi-segment vector navigation to avoid encountering obstacles.This paper combines vector and topological navigation to achieve goal-oriented and robust navigation capability in a complex environment.展开更多
The coronavirus disease 2019(COVID-19)vaccine effectively reduces the possibility of severe illness and mortality in older adults and is essential for controlling the epidemic.Compared with developed countries,the cov...The coronavirus disease 2019(COVID-19)vaccine effectively reduces the possibility of severe illness and mortality in older adults and is essential for controlling the epidemic.Compared with developed countries,the coverage of full vaccination and booster vaccination for older adults aged 60 or above in China is poor,making it urgent to accelerate their vaccination in China.We discussed potential reasons for low vaccination coverage for older adults aged 60 or above and presented strategies to promote their COVID-19 vaccination in China.展开更多
基金funded by National Key R&D Program of China to Fusheng Zha with Grant numbers 2020YFB13134Natural Science Foundation of China to Fusheng Zha with Grant numbers U2013602,52075115,51521003,61911530250.
文摘Artificial intelligence is currently achieving impressive success in all fields.However,autonomous navigation remains a major challenge for AI.Reinforcement learning is used for target navigation to simulate the interaction between the brain and the environment at the behavioral level,but the Artificial Neural Network trained by reinforcement learning cannot match the autonomous mobility of humans and animals.The hippocampus–striatum circuits are considered as key circuits for target navigation planning and decision-making.This paper aims to construct a bionic navigation model of reinforcement learning corresponding to the nervous system to improve the autonomous navigation performance of the robot.The ventral striatum is considered to be the behavioral evaluation region,and the hippocampal–striatum circuit constitutes the position–reward association.In this paper,a set of episode cognition and reinforcement learning system simulating the mechanism of hippocampus and ventral striatum is constructed,which is used to provide target guidance for the robot to perform autonomous tasks.Compared with traditional methods,this system reflects the high efficiency of learning and better Environmental Adaptability.Our research is an exploration of the intersection and fusion of artificial intelligence and neuroscience,which is conducive to the development of artificial intelligence and the understanding of the nervous system.
基金National Natural Science Foundation of China,61773139,Fusheng Zha51521003,Fusheng Zha+6 种基金52075115,Fusheng ZhaU2013602,Fusheng Zha61911530250,Fusheng ZhaShenzhen Science and Technology Research and Development Foundation,JCYJ20190813171009236,Fusheng ZhaShenzhen Science and Technology Program,KQTD2016112515134654,Fusheng ZhaSelf-Planned Task of State Key Laboratory of Robotics and System(HIT),SKLRS202001B,Fusheng ZhaSKLRS202110B,Fusheng Zha.
文摘The hippocampal formation of the brain contains a series of nerve cells related to environmental cognition and navigation.These cells can integrate their moment information and external perceptual information and acquire episodic cognitive memory.Through episodic cognition and memory,organisms can achieve autonomous navigation in complex environments.This paper mainly studies the strategy of robot episode navigation in complex environments.After exploring the environment,the robot obtains subjective environmental cognition and forms a cognition map.The grid cells information contained in the cognitive map can obtain the direction and distance of the target through vector calculation,which can get a shortcut through the inexperienced area.The synaptic connection of place cells in the cognitive map can be used as the topological relationship between episode nodes.When the target-oriented vector navigation encounters obstacles,the obstacles can be realized by setting closer sub-targets.Based on the known obstacle information obtained from boundary cells in the cognitive map,topological paths can be divided into multi-segment vector navigation to avoid encountering obstacles.This paper combines vector and topological navigation to achieve goal-oriented and robust navigation capability in a complex environment.
基金Soft Science Research Project of Shanghai“Science and Technology Innovation Action Plan”(22692107600).
文摘The coronavirus disease 2019(COVID-19)vaccine effectively reduces the possibility of severe illness and mortality in older adults and is essential for controlling the epidemic.Compared with developed countries,the coverage of full vaccination and booster vaccination for older adults aged 60 or above in China is poor,making it urgent to accelerate their vaccination in China.We discussed potential reasons for low vaccination coverage for older adults aged 60 or above and presented strategies to promote their COVID-19 vaccination in China.