As traditional von Neumann architectures face limitations in handling the demands of big data and complex computa-tional tasks,neuromorphic computing has emerged as a promising alternative,inspired by the human brain&...As traditional von Neumann architectures face limitations in handling the demands of big data and complex computa-tional tasks,neuromorphic computing has emerged as a promising alternative,inspired by the human brain's neural networks.Volatile memristors,particularly Mott and diffusive memristors,have garnered significant attention for their ability to emulate neuronal dynamics,such as spiking and firing patterns,enabling the development of reconfigurable and adaptive computing systems.Recent advancements include the implementation of leaky integrate-and-fire neurons,Hodgkin-Huxley neurons,opto-electronic neurons,and time-surface neurons,all utilizing volatile memristors to achieve efficient,low-power,and highly inte-grated neuromorphic systems.This paper reviews the latest progress in volatile memristor-based artificial neurons,highlight-ing their potential for energy-efficient computing and integration with artificial synapses.We conclude by addressing chal-lenges such as improving memristor reliability and exploring new architectures to advance memristor-based neuromorphic com-puting.展开更多
Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and l...Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.展开更多
Threshold switching(TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low po...Threshold switching(TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage(0.38 V)and current(200 nA), an extremely steep slope(< 0.1 m V/dec), and a relatively large off/on ratio(> 10^(3)). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.展开更多
Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely us...Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.展开更多
There has been a notable surge of interest in neuromorphic network computation,particularly concerning both non-volatile and volatile threshold devices.In this research,we have developed a multi-layer thin film archit...There has been a notable surge of interest in neuromorphic network computation,particularly concerning both non-volatile and volatile threshold devices.In this research,we have developed a multi-layer thin film architecture consisting of Al/AlN/Ag/AlN/Pt,which functions as a threshold switching(TS)device characterized by rapid switching speeds of 50 ns and minimal leakage current.We have effectively demonstrated biological neuron-like behaviors,such as threshold-driven spikes,all-ornothing spikes,intensity-modulated frequency response,and frequencymodulated frequency response,through the deployment of a leaky integrate-and-fire(LIF)artificial neuron circuit,which surpasses earlier neuronal models.The resistance switching mechanism of the device is likely due to the migration of nitrogen vacancies in conjunction with silver filaments.This threshold switching device shows significant potential for applications in next-generation artificial neural networks.展开更多
Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantage...Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.展开更多
With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing archit...With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core components of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.展开更多
Purpose: This study examines the transformative impact of artificial intelligence (AI) in healthcare, focusing on its applications in medical diagnosis, drug discovery, surgery, and disease management while addressing...Purpose: This study examines the transformative impact of artificial intelligence (AI) in healthcare, focusing on its applications in medical diagnosis, drug discovery, surgery, and disease management while addressing ethical, technological, and social concerns. Method: A comprehensive literature review synthesizes research on AI applications, including AI-assisted diagnosis, drug discovery, robot-assisted surgery, stroke management, and artificial neurons. Findings: AI has enabled significant breakthroughs in healthcare, enhancing outcomes in diagnostics, personalized treatments, and surgical procedures. Despite its promise, challenges such as privacy, safety, and equitable access remain critical concerns. Research Limitations: The study relies on existing literature and lacks empirical validation of AI models, with its scope limited by the rapid evolution of AI technologies. Social Implications: The integration of AI raises concerns about privacy, patient rights, and equitable access, particularly in underserved regions, potentially exacerbating healthcare disparities. Practical Implications: The study urges healthcare practitioners to adopt AI tools for improved diagnostics and treatments while advocating for regulatory frameworks to ensure ethical and safe AI integration. Originality: This study offers a comprehensive review of AI’s transformative role in healthcare, emphasizing ethical considerations and providing actionable insights for researchers and practitioners.展开更多
To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural network（ANN） hardware implementation methods,a bit-stream ANN hardware implementation method based on sigma delta（Σ...To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural network（ANN） hardware implementation methods,a bit-stream ANN hardware implementation method based on sigma delta（ΣΔ） modulation is presented.The bit-stream adder,multiplier,threshold function unit and fully digital ΣΔ modulator are implemented in a field programmable gate array（FPGA）,and these bit-stream arithmetical units are employed to build the bit-stream artificial neuron.The function of the bit-stream artificial neuron is verified through the realization of the logic function and a linear classifier.The bit-stream perceptron based on the bit-stream artificial neuron with the pre-processed structure is proved to have the ability of nonlinear classification.The FPGA resource utilization of the bit-stream artificial neuron shows that the bit-stream ANN hardware implementation method can significantly reduce the demand of the ANN hardware resources.展开更多
BACKGROUND Prediction of survival after the treatment of hepatocellular carcinoma(HCC)has been widely investigated,yet remains inadequate.The application of artificial intelligence(AI)is emerging as a valid adjunct to...BACKGROUND Prediction of survival after the treatment of hepatocellular carcinoma(HCC)has been widely investigated,yet remains inadequate.The application of artificial intelligence(AI)is emerging as a valid adjunct to traditional statistics due to the ability to process vast amounts of data and find hidden interconnections between variables.AI and deep learning are increasingly employed in several topics of liver cancer research,including diagnosis,pathology,and prognosis.AIM To assess the role of AI in the prediction of survival following HCC treatment.METHODS A web-based literature search was performed according to the Preferred Reporting Items for Systemic Reviews and Meta-Analysis guidelines using the keywords“artificial intelligence”,“deep learning”and“hepatocellular carcinoma”(and synonyms).The specific research question was formulated following the patient(patients with HCC),intervention(evaluation of HCC treatment using AI),comparison(evaluation without using AI),and outcome(patient death and/or tumor recurrence)structure.English language articles were retrieved,screened,and reviewed by the authors.The quality of the papers was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool.Data were extracted and collected in a database.RESULTS Among the 598 articles screened,nine papers met the inclusion criteria,six of which had low-risk rates of bias.Eight articles were published in the last decade;all came from eastern countries.Patient sample size was extremely heterogenous(n=11-22926).AI methodologies employed included artificial neural networks(ANN)in six studies,as well as support vector machine,artificial plant optimization,and peritumoral radiomics in the remaining three studies.All the studies testing the role of ANN compared the performance of ANN with traditional statistics.Training cohorts were used to train the neural networks that were then applied to validation cohorts.In all cases,the AI models demonstrated superior predictive performance compared with traditional statistics with significantly improved areas under the curve.CONCLUSION AI applied to survival prediction after HCC treatment provided enhanced accuracy compared with conventional linear systems of analysis.Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.展开更多
Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are we...Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.展开更多
Voltage-controlled magnetic skyrmions have attracted special attention because they satisfy the requirements for well-controlled high-efficiency and energy saving for future skyrmion-based neuron device applications.I...Voltage-controlled magnetic skyrmions have attracted special attention because they satisfy the requirements for well-controlled high-efficiency and energy saving for future skyrmion-based neuron device applications.In this work,we propose a compact leaky-integrate-fire(LIF)spiking neuron device by using the voltage-driven skyrmion dynamics in a multiferroic nanodisk structure.The skyrmion dynamics is controlled by well tailoring voltage-induced piezostrains,where the skyrmion radius can be effectively modulated by applying the piezostrain pulses.Like the biological neuron,the proposed skyrmionic neuron will accumulate a membrane potential as skyrmion radius is varied by inputting the continuous piezostrain spikes,and the skyrmion radius will return to the initial state in the absence of piezostrain.Therefore,this skyrmion radius-based membrane potential will reach a definite threshold value by the strain stimuli and then reset by removing the stimuli.Such the LIF neuronal functionality and the behaviors of the proposed skyrmionic neuron device are elucidated through the micromagnetic simulation studies.Our results may benefit the utilization of skyrmionic neuron for constructing the future energy-efficient and voltage-tunable spiking neural networks.展开更多
To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural networks （ANN） hardware implementation methods, a bit-stream ANN construction method based on direct sigma-delta （Z-A...To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural networks （ANN） hardware implementation methods, a bit-stream ANN construction method based on direct sigma-delta （Z-A） signal processing is presented. The bit-stream adder, multiplier and fully digital X-A modulator used in the bit-stream linear ANN are implemented in a field programmable gate array （FPGA）. A bit-stream linear ANN based on these bit-stream modules is presented and implemented. To verify the function and performance of the bit-stream linear ANN, the bit-stream adaptive predictor and the bit-stream adaptive noise cancellation system are presented. The predicted result of the bit-stream adaptive predictor is very close to the desired signal. Also, the bit-stream adaptive noise cancellation system removes the electric power noise effectively.展开更多
Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of ...Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of system operation, use artificial neuron network for system modeling, water quality forecast and soft measure, and use fuzzy control technology for the intelligence control of wastewater treatment process. Finally, the main problems in applying artificial intelligence technology to wastewater treatment in China are analyzed.展开更多
Interfacial polarization dominates the permittivity spectra of heterogeneous granular materials for the intermediate frequency range(i.e.,from kHz to MHz).In this study,we examine the corresponding dielectric response...Interfacial polarization dominates the permittivity spectra of heterogeneous granular materials for the intermediate frequency range(i.e.,from kHz to MHz).In this study,we examine the corresponding dielectric responsesof compacted glass sphere packings saturated with pore-filling fluids under various compressive stresses.Theeffective permittivity spectra are observed to exhibit consistently a plateau-to-plateau drop,described by lowfrequency permittivity,characteristic frequency,and high-frequency permittivity.The permittivity spectra underdifferent compressive levels are found to be influenced by the packing structure,compressive stress,and electricalproperty contrasts between solid and fluid(specifically permittivity and conductivity).For considered measure-ment conditions,the variation of packing structure and its associated porosity is found to be more significantthan the stress evolution in controlling the interfacial polarization,thus the permittivity spectra,as supportedby analytical and numerical results for unit cells.Furthermore,to gain a general rule for dielectric responses forsaturated granular materials,we train multi-layer artificial neural network(ANN)models based on a series ofsimulations for unit cells with various structures,stresses,and electrical and dielectric properties.The predictions with two-layer ANN agree well with experimental measurements,presenting errors smaller than 5%forboth low-frequency and high-frequency permittivity.This study offers an effective predicting approach for thedielectric behaviour of heterogeneous and multiphase materials.展开更多
Neuromorphic computing has attracted great attention for its massive parallelism and high energy efficiency.As the fundamental components of neuromorphic computing systems,artificial neurons play a key role in informa...Neuromorphic computing has attracted great attention for its massive parallelism and high energy efficiency.As the fundamental components of neuromorphic computing systems,artificial neurons play a key role in information processing.However,the development of artificial neurons that can simultaneously incorporate low hardware overhead,high reliability,high speed,and low energy consumption remains a challenge.To address this challenge,we propose and demonstrate a piezoelectric neuron with a simple circuit structure,consisting of a piezoelectric cantilever,a parallel capacitor,and a series resistor.It operates through the synergy between the converse piezoelectric effect and the capacitive charging/discharging.Thanks to this efficient and robust mechanism,the piezoelectric neuron not only implements critical leaky integrate-and-fire functions(including leaky integration,threshold-driven spiking,all-or-nothing response,refractory period,strength-modulated firing frequency,and spatiotemporal integration),but also demonstrates small cycle-to-cycle and device-to-device variations(∼1.9%and∼10.0%,respectively),high endurance(1010),high speed(integration/firing:∼9.6/∼0.4μs),and low energy consumption(∼13.4 nJ/spike).Furthermore,spiking neural networks based on piezoelectric neurons are constructed,showing capabilities to implement both supervised and unsupervised learning.This study therefore opens up a new way to develop high-performance artificial neurons by using piezoelectrics,which may facilitate the realization of advanced neuromorphic computing systems.展开更多
Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.Application Programming Interfaces(API)calls contain valuable information that can hel...Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.Application Programming Interfaces(API)calls contain valuable information that can help with malware identification.The malware analysis with reduced feature space helps for the efficient identification of malware.The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy.Three swarm optimization methods,viz.,Ant Lion Optimization(ALO),Cuckoo Search Optimization(CSO),and Firefly Optimization(FO)are applied to API calls using auto-encoders for identification of most influential features.The nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning(ML)classifiers such as Linear Regression(LR),Decision Tree(DT),Random Forest(RF),K-Nearest Neighbor(KNN)&SupportVector Machine(SVM).A hybrid Artificial Neuronal Classifier(ANC)is proposed for improving the classification of android malware.The experimental results yielded an accuracy of 98.87%with just seven features out of hundred API call features,i.e.,a massive 93%of data optimization.展开更多
One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire min...One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study, the contribution of ten major UB causative parameters ha,; been scrutinised based on a published UB predicting artificial neuron network （ANN） model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, i.e., connection weight algorithm （CWA） and profile method （PM） have been applied. As a result of CWA and PM applications, adjusted Qrate （AQ） revealed as the most influential parameter to UB with contribution of 22,40% in CWA and 20,48% in PM respectively. The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine.展开更多
A data fusion method of online multisensors is prop os ed in this paper based on artificial neuron. First, the dynamic data fusion mode l on artificial neuron is built. Then the calibration of data fusion is discusse ...A data fusion method of online multisensors is prop os ed in this paper based on artificial neuron. First, the dynamic data fusion mode l on artificial neuron is built. Then the calibration of data fusion is discusse d with self-adaptive weighing technique. Finally performance of the method is d emonstrated by an online vibration measurement case. The results show that the f used data are more stable, sensitive, accurate, reliable than that of single sen sor data.展开更多
Recently,it has been proposed that spin torque oscillators(STOs)and spin torque diodes could be used as artificial neurons and synapses to directly process microwave signals,which could lower latency and power consump...Recently,it has been proposed that spin torque oscillators(STOs)and spin torque diodes could be used as artificial neurons and synapses to directly process microwave signals,which could lower latency and power consumption greatly.However,one critical challenge is to make the microwave emission frequency of the STO stay constant with a varying input current.In this work,we study the microwave emission characteristics of STOs based on magnetic tunnel junction with MgO cap layer.By applying a small magnetic field,we realize the invariability of the microwave emission frequency of the STO,making it qualified to act as artificial neuron.Furthermore,we have simulated an artificial neural network using STO neuron to recognize the handwritten digits in the Mixed National Institute of Standards and Technology database,and obtained a high accuracy of 92.28%.Our work paves the way for the development of radio-frequency-oriented neuromorphic computing systems.展开更多
基金supported by the Joint R&D Fund of Beijing Smartchip Microelectronics Technology Co.,Ltd.,SGSC0000XSQT2207067.
文摘As traditional von Neumann architectures face limitations in handling the demands of big data and complex computa-tional tasks,neuromorphic computing has emerged as a promising alternative,inspired by the human brain's neural networks.Volatile memristors,particularly Mott and diffusive memristors,have garnered significant attention for their ability to emulate neuronal dynamics,such as spiking and firing patterns,enabling the development of reconfigurable and adaptive computing systems.Recent advancements include the implementation of leaky integrate-and-fire neurons,Hodgkin-Huxley neurons,opto-electronic neurons,and time-surface neurons,all utilizing volatile memristors to achieve efficient,low-power,and highly inte-grated neuromorphic systems.This paper reviews the latest progress in volatile memristor-based artificial neurons,highlight-ing their potential for energy-efficient computing and integration with artificial synapses.We conclude by addressing chal-lenges such as improving memristor reliability and exploring new architectures to advance memristor-based neuromorphic com-puting.
基金supported financially by the fund from the Ministry of Science and Technology of China(Grant No.2019YFB2205100)the National Science Fund for Distinguished Young Scholars(No.52025022)+3 种基金the National Nature Science Foundation of China(Grant Nos.U19A2091,62004016,51732003,52072065,1197407252272140 and 52372137)the‘111’Project(Grant No.B13013)the Fundamental Research Funds for the Central Universities(Nos.2412023YQ004 and 2412022QD036)the funding from Jilin Province(Grant Nos.20210201062GX,20220502002GH,20230402072GH,20230101017JC and 20210509045RQ)。
文摘Spiking neural network(SNN),widely known as the third-generation neural network,has been frequently investigated due to its excellent spatiotemporal information processing capability,high biological plausibility,and low energy consumption characteristics.Analogous to the working mechanism of human brain,the SNN system transmits information through the spiking action of neurons.Therefore,artificial neurons are critical building blocks for constructing SNN in hardware.Memristors are drawing growing attention due to low consumption,high speed,and nonlinearity characteristics,which are recently introduced to mimic the functions of biological neurons.Researchers have proposed multifarious memristive materials including organic materials,inorganic materials,or even two-dimensional materials.Taking advantage of the unique electrical behavior of these materials,several neuron models are successfully implemented,such as Hodgkin–Huxley model,leaky integrate-and-fire model and integrate-and-fire model.In this review,the recent reports of artificial neurons based on memristive devices are discussed.In addition,we highlight the models and applications through combining artificial neuronal devices with sensors or other electronic devices.Finally,the future challenges and outlooks of memristor-based artificial neurons are discussed,and the development of hardware implementation of brain-like intelligence system based on SNN is also prospected.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.61804079 and 61964012)the open research fund of the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology (Grant No.KFJJ20200102)+2 种基金the Natural Science Foundation of Jiangsu Province of China (Grant Nos.BK20211273 and BZ2021031)the Nanjing University of Posts and Telecommunications (Grant No.NY220112)the Foundation of Jiangxi Science and Technology Department (Grant No.20202ACBL21200)。
文摘Threshold switching(TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage(0.38 V)and current(200 nA), an extremely steep slope(< 0.1 m V/dec), and a relatively large off/on ratio(> 10^(3)). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.
基金support from the Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund(No.L233009)National Natural Science Foundation of China(NSFC Nos.62422409,62174152,and 62374159)from the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020115).
文摘Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.
基金supported by the National Natural Science Foundation Joint Regional Innovation Development Project(Grant No.U23A20365)the National R&D Plan“Nano Frontier”Key Special Project(Grant No.2021YFA1200502)+15 种基金the Cultivation Projects of National Major R&D Project(Grant No.92164109)the National Natural Science Foundation of China(Grant Nos.61874158,62004056,and 62104058)the Special Project of Strategic Leading Science and Technology of Chinese Academy of Sciences(Grant No.XDB44000000-7)Hebei Basic Research Special Key Project(Grant No.F2021201045)the Support Program for the Top Young Talents of Hebei Province(Grant No.70280011807)the Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province(Grant No.SLRC2019018)the Interdisciplinary Research Program of Natural Science of Hebei University(No.DXK202101)the Institute of Life Sciences and Green Development(No.521100311)the Natural Science Foundation of Hebei Province(Nos.F2022201054 and F2021201022)the Outstanding Young Scientific Research and Innovation Team of Hebei University(Grant No.605020521001)the Special Support Funds for National High Level Talents(Grant No.041500120001)the Advanced Talents Incubation Program of the Hebei University(Grant Nos.521000981426,521100221071,and 521000981363)High-level Talent Funding Program of Hebei Province(Grant No.B20231003)Yanzhao Young Science Project(Grant No.F2023201076)Science and Technology Project of Hebei Education Department(Grant Nos.QN2020178 and QN2021026)Baoding Science and Technology Plan Project(Grant Nos.2172P011 and 2272P014).
文摘There has been a notable surge of interest in neuromorphic network computation,particularly concerning both non-volatile and volatile threshold devices.In this research,we have developed a multi-layer thin film architecture consisting of Al/AlN/Ag/AlN/Pt,which functions as a threshold switching(TS)device characterized by rapid switching speeds of 50 ns and minimal leakage current.We have effectively demonstrated biological neuron-like behaviors,such as threshold-driven spikes,all-ornothing spikes,intensity-modulated frequency response,and frequencymodulated frequency response,through the deployment of a leaky integrate-and-fire(LIF)artificial neuron circuit,which surpasses earlier neuronal models.The resistance switching mechanism of the device is likely due to the migration of nitrogen vacancies in conjunction with silver filaments.This threshold switching device shows significant potential for applications in next-generation artificial neural networks.
基金supported by the Key-Area Research and Development Program of Guangdong Province(Grants No.2021B0909060002)National Natural Science Foundation of China(Grants No.62204219,62204140)Major Program of Natural Science Foundation of Zhejiang Province(Grants No.LDT23F0401).
文摘Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.
基金supported by the National Natural Science Foundation of China(Nos.U20A20209 and 62304228)the China National Postdoctoral Program for Innovative Talents(No.BX2021326)+3 种基金the China Postdoctoral Science Foundation(No.2021M703310)the Zhejiang Provincial Natural Science Foundation of China(No.LQ22F040003)the Ningbo Natural Science Foundation of China(No.2023J356)the State Key Laboratory for Environment-Friendly Energy Materials(No.20kfhg09).
文摘With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core components of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.
文摘Purpose: This study examines the transformative impact of artificial intelligence (AI) in healthcare, focusing on its applications in medical diagnosis, drug discovery, surgery, and disease management while addressing ethical, technological, and social concerns. Method: A comprehensive literature review synthesizes research on AI applications, including AI-assisted diagnosis, drug discovery, robot-assisted surgery, stroke management, and artificial neurons. Findings: AI has enabled significant breakthroughs in healthcare, enhancing outcomes in diagnostics, personalized treatments, and surgical procedures. Despite its promise, challenges such as privacy, safety, and equitable access remain critical concerns. Research Limitations: The study relies on existing literature and lacks empirical validation of AI models, with its scope limited by the rapid evolution of AI technologies. Social Implications: The integration of AI raises concerns about privacy, patient rights, and equitable access, particularly in underserved regions, potentially exacerbating healthcare disparities. Practical Implications: The study urges healthcare practitioners to adopt AI tools for improved diagnostics and treatments while advocating for regulatory frameworks to ensure ethical and safe AI integration. Originality: This study offers a comprehensive review of AI’s transformative role in healthcare, emphasizing ethical considerations and providing actionable insights for researchers and practitioners.
基金The National Natural Science Foundation of China (No.60576028)the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.11KJB510004)
文摘To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural network（ANN） hardware implementation methods,a bit-stream ANN hardware implementation method based on sigma delta（ΣΔ） modulation is presented.The bit-stream adder,multiplier,threshold function unit and fully digital ΣΔ modulator are implemented in a field programmable gate array（FPGA）,and these bit-stream arithmetical units are employed to build the bit-stream artificial neuron.The function of the bit-stream artificial neuron is verified through the realization of the logic function and a linear classifier.The bit-stream perceptron based on the bit-stream artificial neuron with the pre-processed structure is proved to have the ability of nonlinear classification.The FPGA resource utilization of the bit-stream artificial neuron shows that the bit-stream ANN hardware implementation method can significantly reduce the demand of the ANN hardware resources.
文摘BACKGROUND Prediction of survival after the treatment of hepatocellular carcinoma(HCC)has been widely investigated,yet remains inadequate.The application of artificial intelligence(AI)is emerging as a valid adjunct to traditional statistics due to the ability to process vast amounts of data and find hidden interconnections between variables.AI and deep learning are increasingly employed in several topics of liver cancer research,including diagnosis,pathology,and prognosis.AIM To assess the role of AI in the prediction of survival following HCC treatment.METHODS A web-based literature search was performed according to the Preferred Reporting Items for Systemic Reviews and Meta-Analysis guidelines using the keywords“artificial intelligence”,“deep learning”and“hepatocellular carcinoma”(and synonyms).The specific research question was formulated following the patient(patients with HCC),intervention(evaluation of HCC treatment using AI),comparison(evaluation without using AI),and outcome(patient death and/or tumor recurrence)structure.English language articles were retrieved,screened,and reviewed by the authors.The quality of the papers was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool.Data were extracted and collected in a database.RESULTS Among the 598 articles screened,nine papers met the inclusion criteria,six of which had low-risk rates of bias.Eight articles were published in the last decade;all came from eastern countries.Patient sample size was extremely heterogenous(n=11-22926).AI methodologies employed included artificial neural networks(ANN)in six studies,as well as support vector machine,artificial plant optimization,and peritumoral radiomics in the remaining three studies.All the studies testing the role of ANN compared the performance of ANN with traditional statistics.Training cohorts were used to train the neural networks that were then applied to validation cohorts.In all cases,the AI models demonstrated superior predictive performance compared with traditional statistics with significantly improved areas under the curve.CONCLUSION AI applied to survival prediction after HCC treatment provided enhanced accuracy compared with conventional linear systems of analysis.Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.
基金This work was partially supported by the National Natural Science Foundation of China(62073173,61833011)the Natural Science Foundation of Jiangsu Province,China(BK20191376)the Nanjing University of Posts and Telecommunications(NY220193,NY220145)。
文摘Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.
基金the National Natural Science Foundation of China(Grant Nos.11902316,51902300,and 11972333)the Natural Science Foundation of Zhejiang Province,China(Grant Nos.LQ19F010005,LY21F010011,and LZ19A020001).
文摘Voltage-controlled magnetic skyrmions have attracted special attention because they satisfy the requirements for well-controlled high-efficiency and energy saving for future skyrmion-based neuron device applications.In this work,we propose a compact leaky-integrate-fire(LIF)spiking neuron device by using the voltage-driven skyrmion dynamics in a multiferroic nanodisk structure.The skyrmion dynamics is controlled by well tailoring voltage-induced piezostrains,where the skyrmion radius can be effectively modulated by applying the piezostrain pulses.Like the biological neuron,the proposed skyrmionic neuron will accumulate a membrane potential as skyrmion radius is varied by inputting the continuous piezostrain spikes,and the skyrmion radius will return to the initial state in the absence of piezostrain.Therefore,this skyrmion radius-based membrane potential will reach a definite threshold value by the strain stimuli and then reset by removing the stimuli.Such the LIF neuronal functionality and the behaviors of the proposed skyrmionic neuron device are elucidated through the micromagnetic simulation studies.Our results may benefit the utilization of skyrmionic neuron for constructing the future energy-efficient and voltage-tunable spiking neural networks.
基金Supported by the National Natural Science Foundation of China （No. 60576028） and the National High Technology Research and Development Program of China （No. 2007AA01Z2a5）
文摘To solve the excessive huge scale problem of the traditional multi-bit digital artificial neural networks （ANN） hardware implementation methods, a bit-stream ANN construction method based on direct sigma-delta （Z-A） signal processing is presented. The bit-stream adder, multiplier and fully digital X-A modulator used in the bit-stream linear ANN are implemented in a field programmable gate array （FPGA）. A bit-stream linear ANN based on these bit-stream modules is presented and implemented. To verify the function and performance of the bit-stream linear ANN, the bit-stream adaptive predictor and the bit-stream adaptive noise cancellation system are presented. The predicted result of the bit-stream adaptive predictor is very close to the desired signal. Also, the bit-stream adaptive noise cancellation system removes the electric power noise effectively.
基金Funded by the Natural Science Foundation of Chongqing City(No.2005BB7250)
文摘Current applications of artificial intelligence technology to wastewater treatment in China are summarized. Wastewater treatment plants use expert system mainly in the operation decision-making and fault diagnosis of system operation, use artificial neuron network for system modeling, water quality forecast and soft measure, and use fuzzy control technology for the intelligence control of wastewater treatment process. Finally, the main problems in applying artificial intelligence technology to wastewater treatment in China are analyzed.
文摘Interfacial polarization dominates the permittivity spectra of heterogeneous granular materials for the intermediate frequency range(i.e.,from kHz to MHz).In this study,we examine the corresponding dielectric responsesof compacted glass sphere packings saturated with pore-filling fluids under various compressive stresses.Theeffective permittivity spectra are observed to exhibit consistently a plateau-to-plateau drop,described by lowfrequency permittivity,characteristic frequency,and high-frequency permittivity.The permittivity spectra underdifferent compressive levels are found to be influenced by the packing structure,compressive stress,and electricalproperty contrasts between solid and fluid(specifically permittivity and conductivity).For considered measure-ment conditions,the variation of packing structure and its associated porosity is found to be more significantthan the stress evolution in controlling the interfacial polarization,thus the permittivity spectra,as supportedby analytical and numerical results for unit cells.Furthermore,to gain a general rule for dielectric responses forsaturated granular materials,we train multi-layer artificial neural network(ANN)models based on a series ofsimulations for unit cells with various structures,stresses,and electrical and dielectric properties.The predictions with two-layer ANN agree well with experimental measurements,presenting errors smaller than 5%forboth low-frequency and high-frequency permittivity.This study offers an effective predicting approach for thedielectric behaviour of heterogeneous and multiphase materials.
基金the National Key Research and Development Programs of China(Grant No.2022YFB3807603)the National Natural Science Foundation of China(Grant Nos.92163210 and 52172143)+1 种基金the Science and Technology Projects in Guangzhou(Grant Nos.202201000008 and SL2022A04J00031)the Guangdong Natural Science Funds for Distinguished Young Scholar(Grant No.2024B1515020053).
文摘Neuromorphic computing has attracted great attention for its massive parallelism and high energy efficiency.As the fundamental components of neuromorphic computing systems,artificial neurons play a key role in information processing.However,the development of artificial neurons that can simultaneously incorporate low hardware overhead,high reliability,high speed,and low energy consumption remains a challenge.To address this challenge,we propose and demonstrate a piezoelectric neuron with a simple circuit structure,consisting of a piezoelectric cantilever,a parallel capacitor,and a series resistor.It operates through the synergy between the converse piezoelectric effect and the capacitive charging/discharging.Thanks to this efficient and robust mechanism,the piezoelectric neuron not only implements critical leaky integrate-and-fire functions(including leaky integration,threshold-driven spiking,all-or-nothing response,refractory period,strength-modulated firing frequency,and spatiotemporal integration),but also demonstrates small cycle-to-cycle and device-to-device variations(∼1.9%and∼10.0%,respectively),high endurance(1010),high speed(integration/firing:∼9.6/∼0.4μs),and low energy consumption(∼13.4 nJ/spike).Furthermore,spiking neural networks based on piezoelectric neurons are constructed,showing capabilities to implement both supervised and unsupervised learning.This study therefore opens up a new way to develop high-performance artificial neurons by using piezoelectrics,which may facilitate the realization of advanced neuromorphic computing systems.
文摘Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.Application Programming Interfaces(API)calls contain valuable information that can help with malware identification.The malware analysis with reduced feature space helps for the efficient identification of malware.The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy.Three swarm optimization methods,viz.,Ant Lion Optimization(ALO),Cuckoo Search Optimization(CSO),and Firefly Optimization(FO)are applied to API calls using auto-encoders for identification of most influential features.The nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning(ML)classifiers such as Linear Regression(LR),Decision Tree(DT),Random Forest(RF),K-Nearest Neighbor(KNN)&SupportVector Machine(SVM).A hybrid Artificial Neuronal Classifier(ANC)is proposed for improving the classification of android malware.The experimental results yielded an accuracy of 98.87%with just seven features out of hundred API call features,i.e.,a massive 93%of data optimization.
文摘One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study, the contribution of ten major UB causative parameters ha,; been scrutinised based on a published UB predicting artificial neuron network （ANN） model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, i.e., connection weight algorithm （CWA） and profile method （PM） have been applied. As a result of CWA and PM applications, adjusted Qrate （AQ） revealed as the most influential parameter to UB with contribution of 22,40% in CWA and 20,48% in PM respectively. The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine.
文摘A data fusion method of online multisensors is prop os ed in this paper based on artificial neuron. First, the dynamic data fusion mode l on artificial neuron is built. Then the calibration of data fusion is discusse d with self-adaptive weighing technique. Finally performance of the method is d emonstrated by an online vibration measurement case. The results show that the f used data are more stable, sensitive, accurate, reliable than that of single sen sor data.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11974379 and 12204357)K.C.Wong Education Foundation(Grant No.GJTD2019-14)+2 种基金Jiangxi Province“Double Thousand Plan”(Grant No.S2019CQKJ2638)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.22KB140017)Wuxi University Research Start-up Fund for Introduced Talents(Grant No.2022r006)。
文摘Recently,it has been proposed that spin torque oscillators(STOs)and spin torque diodes could be used as artificial neurons and synapses to directly process microwave signals,which could lower latency and power consumption greatly.However,one critical challenge is to make the microwave emission frequency of the STO stay constant with a varying input current.In this work,we study the microwave emission characteristics of STOs based on magnetic tunnel junction with MgO cap layer.By applying a small magnetic field,we realize the invariability of the microwave emission frequency of the STO,making it qualified to act as artificial neuron.Furthermore,we have simulated an artificial neural network using STO neuron to recognize the handwritten digits in the Mixed National Institute of Standards and Technology database,and obtained a high accuracy of 92.28%.Our work paves the way for the development of radio-frequency-oriented neuromorphic computing systems.
