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
To achieve the artificial general intelligence （AGI）, imitate the intelligence？ or imitate the brain？ This is the question! Most artificial intelligence （AI） approaches set the understanding of the intelligence ...To achieve the artificial general intelligence （AGI）, imitate the intelligence？ or imitate the brain？ This is the question! Most artificial intelligence （AI） approaches set the understanding of the intelligence principle as their premise. This may be correct to implement specific intelligence such as computing, symbolic logic, or what the AlphaGo could do. However, this is not correct for AGI, because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings. It is not wise to set such a question as the premise of the AGI mission. To achieve AGI, a practical approach is to build the so-called neurocomputer, which could be trained to produce autonomous intelligence and AGI. A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons, synapses and other essential neural components. The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body. The philosophy under the ＂new＂ approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded. This paper compares the neurocomputer with the conventional computer. The major progress about neurocomputer is also reviewed.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
Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,exces...Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the development of programming and deployment tools has lowered the threshold for the use of SNNs.SNNs show a broad development prospect for brain-like computing.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
Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of co...Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.展开更多
Brain-like computer research and development have been growing rapidly in recent years. It is necessary to design large scale dynamical neural networks (more than 106 neurons) to simulate complex process of our brain....Brain-like computer research and development have been growing rapidly in recent years. It is necessary to design large scale dynamical neural networks (more than 106 neurons) to simulate complex process of our brain. But such kind of task is not easy to achieve only based on the analysis of partial differential equations, especially for those complex neural models, e.g. Rose-Hindmarsh (RH) model. So in this paper, we develop a novel approach by combining fuzzy logical designing with Proximal Support Vector Machine Classifiers (PSVM) learning in the designing of large scale neural networks. Particularly, our approach can effectively simplify the designing process, which is crucial for both cognition science and neural science. At last, we conduct our approach on an artificial neural system with more than 108 neurons for haze-free task, and the experimental results show that texture features extracted by fuzzy logic can effectively increase the texture information entropy and improve the effect of haze-removing in some degree.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 Natural Science Foundation of China(Nos.61425025 and 61390515)
文摘To achieve the artificial general intelligence （AGI）, imitate the intelligence？ or imitate the brain？ This is the question! Most artificial intelligence （AI） approaches set the understanding of the intelligence principle as their premise. This may be correct to implement specific intelligence such as computing, symbolic logic, or what the AlphaGo could do. However, this is not correct for AGI, because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings. It is not wise to set such a question as the premise of the AGI mission. To achieve AGI, a practical approach is to build the so-called neurocomputer, which could be trained to produce autonomous intelligence and AGI. A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons, synapses and other essential neural components. The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body. The philosophy under the ＂new＂ approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded. This paper compares the neurocomputer with the conventional computer. The major progress about neurocomputer is also reviewed.
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 the National Natural Science Foundation of China(Nos.61974164,62074166,62004219,62004220,and 62104256).
文摘Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the development of programming and deployment tools has lowered the threshold for the use of SNNs.SNNs show a broad development prospect for brain-like computing.
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Supported by the National Natural Science Foundation of China(No.61305042,61202098)Projects of Center for Remote Sensing Mission Study of China National Space Administration(No.2012A03A0939)Science and Technological Research of Key Projects of Education Department of Henan Province of China(No.13A520071)
文摘Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.
文摘Brain-like computer research and development have been growing rapidly in recent years. It is necessary to design large scale dynamical neural networks (more than 106 neurons) to simulate complex process of our brain. But such kind of task is not easy to achieve only based on the analysis of partial differential equations, especially for those complex neural models, e.g. Rose-Hindmarsh (RH) model. So in this paper, we develop a novel approach by combining fuzzy logical designing with Proximal Support Vector Machine Classifiers (PSVM) learning in the designing of large scale neural networks. Particularly, our approach can effectively simplify the designing process, which is crucial for both cognition science and neural science. At last, we conduct our approach on an artificial neural system with more than 108 neurons for haze-free task, and the experimental results show that texture features extracted by fuzzy logic can effectively increase the texture information entropy and improve the effect of haze-removing in some degree.
