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
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
Nanostructured Y2O3 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 curre...Nanostructured Y2O3 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 current 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=1 s and toff=1 s)with an average current density of 0.05 A/dm2(Ia=0.05 A/dm2)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 Y2O3 with brain-and 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 of nanostructured Y2O3.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
目的 探究脑出血患者脑组织中Nod样受体蛋白-3 (NLRP3)、NIMA相关蛋白激酶7 (NEK7)表达水平与疾病严重程度的相关性。方法 前瞻性选取2017年1月至2020年12月郑州颐和医院诊治的80例脑出血患者进行研究,取皮层造瘘通道靠近血肿0.5 cm处...目的 探究脑出血患者脑组织中Nod样受体蛋白-3 (NLRP3)、NIMA相关蛋白激酶7 (NEK7)表达水平与疾病严重程度的相关性。方法 前瞻性选取2017年1月至2020年12月郑州颐和医院诊治的80例脑出血患者进行研究,取皮层造瘘通道靠近血肿0.5 cm处的脑组织为靠近组,另取远离血肿位置的脑组织为远离组。依据脑出血患者出血量将其分为少量组(出血量<15 mL) 29例、中量组(出血量15~30 m L) 27例和大量组(出血量>30 mL)24例;按美国国立卫生研究院卒中量表(NIHSS)评分将患者分为轻型组(1~4分) 30例、中型组(5~15分) 27例和重型组(>15分) 23例。采用实时荧光定量PCR (qRT-PCR)法测定各组脑组织中NEK7 m RNA、NLRP3 m RNA表达水平;采用Pearson法分析脑出血患者血肿0.5 cm处脑组织中NEK7 m RNA表达水平与NLRP3 m RNA表达水平的相关性;比较不同出血量、不同严重程度的脑出血患者距离血肿0.5 cm处脑组织中NEK7 m RNA、NLRP3 m RNA表达水平。结果 靠近组患者脑组织中NEK7 m RNA、NLRP3 m RNA表达水平分别为1.72±0.58、1.69±0.57,明显高于远离组的1.03±0.34、1.01±0.33,差异均有统计学意义(P<0.05);脑出血患者血肿0.5 cm处脑组织中NEK7 m RNA表达水平与NLRP3 mRNA表达水平呈正相关(r=0.563,P<0.05);脑出血患者距离血肿0.5 cm处脑组织中NEK7m RNA、NLRP3 m RNA表达水平随着出血量的增加而升高,差异均有统计学意义(P<0.05);脑出血患者距离血肿0.5 cm处脑组织中NEK7 mRNA、NLRP3 m RNA表达水平随着NIHSS评分的增加而升高,差异均有统计学意义(P<0.05)。结论 脑出血患者距离血肿0.5 cm处脑组织中NEK7、NLRP3表达水平明显升高,两者均与出血量和疾病严重程度显著相关,检测距离血肿0.5 cm处脑组织NEK7、NLRP3有利于判断脑出血严重程度及出血情况。展开更多
目的探讨脑梗死患者静脉溶栓疗程中视椎蛋白样蛋白1(visinin like protein-1,VILIP-1)、膜联蛋白A2(Annexin A2)、可溶性血管内皮蛋白C受体(solubleendothelium protein c receptor,sEPCR)动态变化及与病情转归相关性,为临床预测、改善...目的探讨脑梗死患者静脉溶栓疗程中视椎蛋白样蛋白1(visinin like protein-1,VILIP-1)、膜联蛋白A2(Annexin A2)、可溶性血管内皮蛋白C受体(solubleendothelium protein c receptor,sEPCR)动态变化及与病情转归相关性,为临床预测、改善患者预后提供参考。方法选取急性脑梗死患者105例进行前瞻性研究,均接受静脉溶栓治疗。以门诊方式随访28 d,根据患者病情转归情况分为良好组、不良组,检测2组溶栓前、溶栓后1 d、3 d的VILIP-1、Annexin A2、sEPCR水平,分析其与mRS评分的相关性,随机森林算法筛选特征变量,并分析病情转归的影响因素,分析不同时间点VILIP-1、Annexin A2、sEPCR及联合预测病情转归价值。结果不良组美国国立卫生研究院卒中量表(National Institutes of Health Stroke Scale,NIHSS)评分、D-二聚体、纤维蛋白原、三酰甘油高于良好组(P<0.05)。2组VILIP-1、sEPCR水平随着溶栓时间的延长而减低,Annexin A2随着溶栓时间的延长而升高(P<0.05);不良组VILIP-1、sEPCR和Annexin A2波动高于良好组(P<0.05)。溶栓后1 d、溶栓后3 d的VILIP-1、sEPCR与mRS评分呈正相关,Annexin A2与mRS评分呈负相关(P<0.05)。选取决策树数量ntree为390颗,得到最优结果,重要性排序前4的变量分别是sEPCR、Annexin A2、NIHSS评分、VILIP-1,均与mRS评分相关(P<0.05);溶栓后3 d三者联合的AUC最大,其预测敏感度为78.79%,特异度为95.83%。结论血清VILIP-1、Annexin A2、sEPCR异常表达与脑梗死患者脑损伤程度、溶栓预后有关,溶栓后3 d联合检测的预后价值较高,可作为脑梗死患者预后预测的重要标志物。展开更多
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.
文摘Nanostructured Y2O3 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 current 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=1 s and toff=1 s)with an average current density of 0.05 A/dm2(Ia=0.05 A/dm2)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 Y2O3 with brain-and 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 of nanostructured Y2O3.
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.
文摘目的 探究脑出血患者脑组织中Nod样受体蛋白-3 (NLRP3)、NIMA相关蛋白激酶7 (NEK7)表达水平与疾病严重程度的相关性。方法 前瞻性选取2017年1月至2020年12月郑州颐和医院诊治的80例脑出血患者进行研究,取皮层造瘘通道靠近血肿0.5 cm处的脑组织为靠近组,另取远离血肿位置的脑组织为远离组。依据脑出血患者出血量将其分为少量组(出血量<15 mL) 29例、中量组(出血量15~30 m L) 27例和大量组(出血量>30 mL)24例;按美国国立卫生研究院卒中量表(NIHSS)评分将患者分为轻型组(1~4分) 30例、中型组(5~15分) 27例和重型组(>15分) 23例。采用实时荧光定量PCR (qRT-PCR)法测定各组脑组织中NEK7 m RNA、NLRP3 m RNA表达水平;采用Pearson法分析脑出血患者血肿0.5 cm处脑组织中NEK7 m RNA表达水平与NLRP3 m RNA表达水平的相关性;比较不同出血量、不同严重程度的脑出血患者距离血肿0.5 cm处脑组织中NEK7 m RNA、NLRP3 m RNA表达水平。结果 靠近组患者脑组织中NEK7 m RNA、NLRP3 m RNA表达水平分别为1.72±0.58、1.69±0.57,明显高于远离组的1.03±0.34、1.01±0.33,差异均有统计学意义(P<0.05);脑出血患者血肿0.5 cm处脑组织中NEK7 m RNA表达水平与NLRP3 mRNA表达水平呈正相关(r=0.563,P<0.05);脑出血患者距离血肿0.5 cm处脑组织中NEK7m RNA、NLRP3 m RNA表达水平随着出血量的增加而升高,差异均有统计学意义(P<0.05);脑出血患者距离血肿0.5 cm处脑组织中NEK7 mRNA、NLRP3 m RNA表达水平随着NIHSS评分的增加而升高,差异均有统计学意义(P<0.05)。结论 脑出血患者距离血肿0.5 cm处脑组织中NEK7、NLRP3表达水平明显升高,两者均与出血量和疾病严重程度显著相关,检测距离血肿0.5 cm处脑组织NEK7、NLRP3有利于判断脑出血严重程度及出血情况。
