Brain,the material foundation of human intelligence,is the most complex tissue in the human body.Brain diseases are among the leading threats to human life,yet our understanding of their pathogenic mechanisms and drug...Brain,the material foundation of human intelligence,is the most complex tissue in the human body.Brain diseases are among the leading threats to human life,yet our understanding of their pathogenic mechanisms and drug development remains limited,largely due to the lack of accurate brain-like tissue models that replicate its complex structure and functions.Therefore,constructing brain-like models—both in morphology and function—possesses significant scientific value for advancing brain science and pathological pharmacology research,representing the frontiers in the biomanufacturing field.This review outlines the primary requirements and challenges in biomanufacturing brain-like tissue,addressing its complex structures,functions,and environments.Also,the existing biomanufacturing technologies,strategies,and characteristics for brain-like models are depicted,and cutting-edge developments in biomanufacturing central neural repair prosthetics,brain development models,brain disease models,and brain-inspired biocomputing models are systematically reviewed.Finally,the paper concludes with future perspectives on the biomanufacturing of brain-like tissue transitioning from structural manufacturing to intelligent functioning.展开更多
Nanostructured Y203 was successfully prepared via a two-step and template-free method. Firstly, yttrium hydroxide precursor was galvanostatically grown on the steel substrate from chloride bath by direct and pulse cur...Nanostructured Y203 was successfully prepared via a two-step and template-free method. Firstly, yttrium hydroxide precursor was galvanostatically grown on the steel substrate from chloride bath by direct and pulse current deposition modes. Direct cunent deposition was carried out at the constant current density of 0.1 A/dm2 for 600 s. The pulse current was also performed at a typical on-time and off-time （ton=l S and Germ s） with an average current density of 0.05 A/dm2 （la=0.05 A/din2） for 600 s. The obtained hydroxide films were then scraped from the substrates and thermally converted into final oxide product via heat-treatment. Thermal behaviors and phase transformations during the heat treatment of the hydroxide powder samples were investigated by differential scanning calorimetry （DSC） and thermogravimetric analysis （TGA）. The final oxide products were characterized by means of X-ray diffraction （XRD）, Fourier transform infrared spectroscopy （FTIR） and scanning electron microscopy （SEM）. The results showed that the well-crystallized Y203 with brainand sphere-like morphology were achievable via pulse and direct deposition modes, respectively. It was concluded that pulse current cathodic electrodeposition offered a facile route for preparation ofnanostructured Y203.展开更多
The anthropomorphic intelligence of autonomous driving has been a research hotspot in the world.However,current studies have not been able to reveal the mechanism of drivers'natural driving behaviors.Therefore,thi...The anthropomorphic intelligence of autonomous driving has been a research hotspot in the world.However,current studies have not been able to reveal the mechanism of drivers'natural driving behaviors.Therefore,this thesis starts from the perspective of cognitive decision-making in the human brain,which is inspired by the regulation of dopamine feedback in the basal ganglia,and a reinforcement learning model is established to solve the brain-like intelligent decision-making problems in the process of interacting with the environment.In this thesis,first,a detailed bionic mechanism architecture based on basal ganglia was proposed by the consideration and analysis of its feedback regulation mechanism;second,the above mechanism was transformed into a reinforcement Q-learning model,so as to implement the learning and adaptation abilities of an intelligent vehicle for brain-like intelligent decision-making during car-following;finally,the feasibility and effectiveness of the proposed method were verified by the simulations and real vehicle tests.展开更多
The human brain performs computations via a highly interconnected network of neurons.Taking inspiration from the information delivery and processing mechanism of the human brain in central nervous systems,bioinspired ...The human brain performs computations via a highly interconnected network of neurons.Taking inspiration from the information delivery and processing mechanism of the human brain in central nervous systems,bioinspired nanofluidic iontronics has been proposed and gradually engineered to overcome the limitations of the conventional electron-based von Neumann architecture,which shows the promising potential to enable efficient brain-like computing.Anomalous and tunable nanofluidic ion transport behaviors and spatial confinement show promising controllability of charge carriers,and a wide range of structural and chemical modification paves new ways for realizing brain-like functions.Herein,a comprehensive framework of mechanisms and design strategy is summarized to enable the rational design of nanofluidic systems and facilitate the further development of bioinspired nanofluidic iontronics.This review provides recent advances and prospects of the bioinspired nanofluidic iontronics,including ion-based brain computing,comprehension of intrinsic mechanisms,design of artificial nanochannels,and the latest artificial neuromorphic functions devices.Furthermore,the challenges and opportunities of bioinspired nanofluidic iontronics in the pioneering and interdisciplinary research fields are proposed,including brain–computer interfaces and artificial neurons.展开更多
Nowadays,deep neural networks(DNNs)have been equipped with powerful representation capabilities.The deep convolutional neural networks(CNNs)that draw inspiration from the visual processing mechanism of the primate ear...Nowadays,deep neural networks(DNNs)have been equipped with powerful representation capabilities.The deep convolutional neural networks(CNNs)that draw inspiration from the visual processing mechanism of the primate early visual cortex have outperformed humans on object categorization and have been found to possess many brain-like properties.Recently,vision transformers(ViTs)have been striking paradigms of DNNs and have achieved remarkable improvements on many vision tasks compared to CNNs.It is natural to ask how the brain-like properties of ViTs are.Beyond the model paradigm,we are also interested in the effects of factors,such as model size,multimodality,and temporality,on the ability of networks to model the human visual pathway,especially when considering that existing research has been limited to CNNs.In this paper,we systematically evaluate the brain-like properties of 30 kinds of computer vision models varying from CNNs and ViTs to their hybrids from the perspective of explaining brain activities of the human visual cortex triggered by dynamic stimuli.Experiments on two neural datasets demonstrate that neither CNN nor transformer is the optimal model paradigm for modelling the human visual pathway.ViTs reveal hierarchical correspondences to the visual pathway as CNNs do.Moreover,we find that multi-modal and temporal networks can better explain the neural activities of large parts of the visual cortex,whereas a larger model size is not a sufficient condition for bridging the gap between human vision and artificial networks.Our study sheds light on the design principles for more brain-like networks.The code is available at https://github.com/QYiZhou/LWNeuralEncoding.展开更多
To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network ...To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network with attention mecha-nism(SS-SIRCN),inspired by the behavioral adaptation patterns of biological groups under external threats.First,by thoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed to risk,the study reveals the underlying patterns of trajectory changes influenced by external danger.Then,an attention-based spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features and potential driving risks.Finally,a semi-supervised learning framework is employed to enable risk assessment for autono-mous vehicles using only a small amount of labeled data.Experimental results on real-world vehicle trajectory datasets demonstrate that the proposed method achieves a risk prediction accuracy of 90.76%,outperforming other baseline models in performance.展开更多
基金supported by the Program of the National Natural Science Foundation of China(52275291)(52435006)the Program for Innovation Team of Shaanxi Province(2023CX-TD-17)the Fundamental Research Funds for the Central Universities。
文摘Brain,the material foundation of human intelligence,is the most complex tissue in the human body.Brain diseases are among the leading threats to human life,yet our understanding of their pathogenic mechanisms and drug development remains limited,largely due to the lack of accurate brain-like tissue models that replicate its complex structure and functions.Therefore,constructing brain-like models—both in morphology and function—possesses significant scientific value for advancing brain science and pathological pharmacology research,representing the frontiers in the biomanufacturing field.This review outlines the primary requirements and challenges in biomanufacturing brain-like tissue,addressing its complex structures,functions,and environments.Also,the existing biomanufacturing technologies,strategies,and characteristics for brain-like models are depicted,and cutting-edge developments in biomanufacturing central neural repair prosthetics,brain development models,brain disease models,and brain-inspired biocomputing models are systematically reviewed.Finally,the paper concludes with future perspectives on the biomanufacturing of brain-like tissue transitioning from structural manufacturing to intelligent functioning.
文摘Nanostructured Y203 was successfully prepared via a two-step and template-free method. Firstly, yttrium hydroxide precursor was galvanostatically grown on the steel substrate from chloride bath by direct and pulse current deposition modes. Direct cunent deposition was carried out at the constant current density of 0.1 A/dm2 for 600 s. The pulse current was also performed at a typical on-time and off-time （ton=l S and Germ s） with an average current density of 0.05 A/dm2 （la=0.05 A/din2） for 600 s. The obtained hydroxide films were then scraped from the substrates and thermally converted into final oxide product via heat-treatment. Thermal behaviors and phase transformations during the heat treatment of the hydroxide powder samples were investigated by differential scanning calorimetry （DSC） and thermogravimetric analysis （TGA）. The final oxide products were characterized by means of X-ray diffraction （XRD）, Fourier transform infrared spectroscopy （FTIR） and scanning electron microscopy （SEM）. The results showed that the well-crystallized Y203 with brainand sphere-like morphology were achievable via pulse and direct deposition modes, respectively. It was concluded that pulse current cathodic electrodeposition offered a facile route for preparation ofnanostructured Y203.
基金supported by the National Key Research and Development Program of China(2017YFB0102601)the National Science Foundation of China(51775236).
文摘The anthropomorphic intelligence of autonomous driving has been a research hotspot in the world.However,current studies have not been able to reveal the mechanism of drivers'natural driving behaviors.Therefore,this thesis starts from the perspective of cognitive decision-making in the human brain,which is inspired by the regulation of dopamine feedback in the basal ganglia,and a reinforcement learning model is established to solve the brain-like intelligent decision-making problems in the process of interacting with the environment.In this thesis,first,a detailed bionic mechanism architecture based on basal ganglia was proposed by the consideration and analysis of its feedback regulation mechanism;second,the above mechanism was transformed into a reinforcement Q-learning model,so as to implement the learning and adaptation abilities of an intelligent vehicle for brain-like intelligent decision-making during car-following;finally,the feasibility and effectiveness of the proposed method were verified by the simulations and real vehicle tests.
基金supported by the National Natural Science Foundation of China(Nos.21975209,52273305,22205185,52025132,T2241022,21621091,22021001,and 22121001)the 111 Project(Nos.B17027 and B16029)+2 种基金the National Science Foundation of Fujian Province of China(No.2022J02059)the Science and Technology Projects of Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(No.RD2022070601)the Tencent Foundation(The XPLORER PRIZE).
文摘The human brain performs computations via a highly interconnected network of neurons.Taking inspiration from the information delivery and processing mechanism of the human brain in central nervous systems,bioinspired nanofluidic iontronics has been proposed and gradually engineered to overcome the limitations of the conventional electron-based von Neumann architecture,which shows the promising potential to enable efficient brain-like computing.Anomalous and tunable nanofluidic ion transport behaviors and spatial confinement show promising controllability of charge carriers,and a wide range of structural and chemical modification paves new ways for realizing brain-like functions.Herein,a comprehensive framework of mechanisms and design strategy is summarized to enable the rational design of nanofluidic systems and facilitate the further development of bioinspired nanofluidic iontronics.This review provides recent advances and prospects of the bioinspired nanofluidic iontronics,including ion-based brain computing,comprehension of intrinsic mechanisms,design of artificial nanochannels,and the latest artificial neuromorphic functions devices.Furthermore,the challenges and opportunities of bioinspired nanofluidic iontronics in the pioneering and interdisciplinary research fields are proposed,including brain–computer interfaces and artificial neurons.
基金supported by National Natural Science Foundation of China(Nos.61976209 and 62020106015)the CAS International Collaboration Key Project,China(No.173211KYSB20190024)the Strategic Priority Research Program of CAS,China(No.XDB32040000)。
文摘Nowadays,deep neural networks(DNNs)have been equipped with powerful representation capabilities.The deep convolutional neural networks(CNNs)that draw inspiration from the visual processing mechanism of the primate early visual cortex have outperformed humans on object categorization and have been found to possess many brain-like properties.Recently,vision transformers(ViTs)have been striking paradigms of DNNs and have achieved remarkable improvements on many vision tasks compared to CNNs.It is natural to ask how the brain-like properties of ViTs are.Beyond the model paradigm,we are also interested in the effects of factors,such as model size,multimodality,and temporality,on the ability of networks to model the human visual pathway,especially when considering that existing research has been limited to CNNs.In this paper,we systematically evaluate the brain-like properties of 30 kinds of computer vision models varying from CNNs and ViTs to their hybrids from the perspective of explaining brain activities of the human visual cortex triggered by dynamic stimuli.Experiments on two neural datasets demonstrate that neither CNN nor transformer is the optimal model paradigm for modelling the human visual pathway.ViTs reveal hierarchical correspondences to the visual pathway as CNNs do.Moreover,we find that multi-modal and temporal networks can better explain the neural activities of large parts of the visual cortex,whereas a larger model size is not a sufficient condition for bridging the gap between human vision and artificial networks.Our study sheds light on the design principles for more brain-like networks.The code is available at https://github.com/QYiZhou/LWNeuralEncoding.
基金the Jilin Provincial Department of Science and Technology Youth Science and Technology Talent Cultivation Project(20250602051RC)Fundamental Research Funds for the Central Universities(2025-JCXK-19)National Natural Science Foundation of China under Grant 52272417.
文摘To address the critical challenge of risk perception and assessment for autonomous vehicles in dynamic interactive envi-ronments,this study proposes a semi-supervised spatiotemporal interaction risk cognition network with attention mecha-nism(SS-SIRCN),inspired by the behavioral adaptation patterns of biological groups under external threats.First,by thoroughly analyzing the dynamic interaction characteristics exhibited by typical biological collectives when exposed to risk,the study reveals the underlying patterns of trajectory changes influenced by external danger.Then,an attention-based spatiotemporal risk cognition network is designed to establish a mapping between driving behavior features and potential driving risks.Finally,a semi-supervised learning framework is employed to enable risk assessment for autono-mous vehicles using only a small amount of labeled data.Experimental results on real-world vehicle trajectory datasets demonstrate that the proposed method achieves a risk prediction accuracy of 90.76%,outperforming other baseline models in performance.
