This paper investigated an effective and robust mechanism for detecting simple mail transfer protocol (SMTP) traffic anomaly. The detection method cumulates the deviation of current delivering status from history beha...This paper investigated an effective and robust mechanism for detecting simple mail transfer protocol (SMTP) traffic anomaly. The detection method cumulates the deviation of current delivering status from history behavior based on a weighted sum method called the leaky integrate-and-fire model to detect anomaly. The simplicity of the detection method is that the method need not store history profile and low computation overhead, which makes the detection method itself immunes to attacks. The performance is investigated in terms of detection probability, the false alarm ratio, and the detection delay. The results show that leaky integrate-and-fire method is quite effective at detecting constant intensity attacks and increasing intensity attacks. Compared with the non-parametric cumulative sum method, the evaluation results show that the proposed detection method has shorter detection latency and higher detection probability.展开更多
The evoked spike discharges of a neuron depend critically on the recent history of its electrical activity. A well-known example is the phenomenon of spike-frequency adaptation that is a commonly observed property of ...The evoked spike discharges of a neuron depend critically on the recent history of its electrical activity. A well-known example is the phenomenon of spike-frequency adaptation that is a commonly observed property of neurons. In this paper, using a leaky integrate-and-fire model that includes an adaptation current, we propose an event-driven strategy to simulate integrate-and-fire models with spike-frequency adaptation. Such approach is more precise than traditional clock-driven numerical integration approach because the timing of spikes is treated exactly. In experiments, using event-driven and clock-driven strategies we simulated the adaptation time course of single neuron and the random network with spike-timing dependent plasticity, the results indicate that （1） the temporal precision of spiking events impacts on neuronal dynamics of single as well as network in the different simulation strategies and （2） the simulation time in the event-driven simulation strategies. scales linearly with the total number of spiking events展开更多
Diffusive threshold switching(TS)memristors have emerged as a promising candidate for artificial neurons,effectively replicating neuronal functions and enabling spiking neural networks(SNNs)to emulate the low-power pr...Diffusive threshold switching(TS)memristors have emerged as a promising candidate for artificial neurons,effectively replicating neuronal functions and enabling spiking neural networks(SNNs)to emulate the low-power processing of biological brains.In this study,we present an artificial neuron based on a Pt/Ag/ZnO/Pt volatile memristor,which exhibits exceptional TS characteristics,including electro-forming-free operation,low voltage requirements(<0.2 V),high stability(2.25%variation over 1024 cycles),a high on/off ratio(106),and inherent self-compliance.These Pt/Ag/ZnO/Pt diffusive memristors are employed to simultaneously emulate oscillation neurons and leaky integrate-and-fire(LIF)neurons,enabling precise modulation of oscillation and firing frequencies through pulse parameters while maintaining low energy consumption(1.442 nJ per spike).We further integrate the oscillation and LIF neurons as input and output neurons,respectively,in a two-layer SNN,achieving a high classification accuracy of 89.17%on MNIST-based voltage images.This work underscores the potential of ZnO diffusive memristors in emulating stable artificial neurons and highlights their promise for advanced neuromorphic computing applications using SNNs.展开更多
The capability of neurons to discriminate between intensity of external stimulus is measured by its dynamic range.A larger dynamic range indicates a greater probability of neuronal survival.In this study,the potential...The capability of neurons to discriminate between intensity of external stimulus is measured by its dynamic range.A larger dynamic range indicates a greater probability of neuronal survival.In this study,the potential roles of adaptation mechanisms（ion currents） in modulating neuronal dynamic range were numerically investigated.Based on the adaptive exponential integrate-and-fire model,which includes two different adaptation mechanisms,i.e.subthreshold and suprathreshold（spike-triggered） adaptation,our results reveal that the two adaptation mechanisms exhibit rather different roles in regulating neuronal dynamic range.Specifically,subthreshold adaptation acts as a negative factor that observably decreases the neuronal dynamic range,while suprathreshold adaptation has little influence on the neuronal dynamic range.Moreover,when stochastic noise was introduced into the adaptation mechanisms,the dynamic range was apparently enhanced,regardless of what state the neuron was in,e.g.adaptive or non-adaptive.Our model results suggested that the neuronal dynamic range can be differentially modulated by different adaptation mechanisms.Additionally,noise was a non-ignorable factor,which could effectively modulate the neuronal dynamic range.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
In this work,we consider the Fokker-Planck equation of the Nonlinear Noisy Leaky Integrate-and-Fire(NNLIF)model for neuron networks.Due to the firing events of neurons at the microscopic level,this Fokker-Planck equat...In this work,we consider the Fokker-Planck equation of the Nonlinear Noisy Leaky Integrate-and-Fire(NNLIF)model for neuron networks.Due to the firing events of neurons at the microscopic level,this Fokker-Planck equation contains dynamic boundary conditions involving specific internal points.To efficiently solve this problem and explore the properties of the unknown,we construct a flexible numerical scheme for the Fokker-Planck equation in the framework of spectral methods that can accurately handle the dynamic boundary condition.This numerical scheme is stable with suitable choices of test function spaces,and asymptotic preserving,and it is easily extendable to variant models with multiple time scales.We also present extensive numerical examples to verify the scheme properties,including order of convergence and time efficiency,and explore unique properties of the model,including blow-up phenomena for the NNLIF model and learning and discriminative properties for the NNLIF model with learning rules.展开更多
文摘This paper investigated an effective and robust mechanism for detecting simple mail transfer protocol (SMTP) traffic anomaly. The detection method cumulates the deviation of current delivering status from history behavior based on a weighted sum method called the leaky integrate-and-fire model to detect anomaly. The simplicity of the detection method is that the method need not store history profile and low computation overhead, which makes the detection method itself immunes to attacks. The performance is investigated in terms of detection probability, the false alarm ratio, and the detection delay. The results show that leaky integrate-and-fire method is quite effective at detecting constant intensity attacks and increasing intensity attacks. Compared with the non-parametric cumulative sum method, the evaluation results show that the proposed detection method has shorter detection latency and higher detection probability.
文摘The evoked spike discharges of a neuron depend critically on the recent history of its electrical activity. A well-known example is the phenomenon of spike-frequency adaptation that is a commonly observed property of neurons. In this paper, using a leaky integrate-and-fire model that includes an adaptation current, we propose an event-driven strategy to simulate integrate-and-fire models with spike-frequency adaptation. Such approach is more precise than traditional clock-driven numerical integration approach because the timing of spikes is treated exactly. In experiments, using event-driven and clock-driven strategies we simulated the adaptation time course of single neuron and the random network with spike-timing dependent plasticity, the results indicate that （1） the temporal precision of spiking events impacts on neuronal dynamics of single as well as network in the different simulation strategies and （2） the simulation time in the event-driven simulation strategies. scales linearly with the total number of spiking events
基金supported by the National Natural Science Foundation of China(62471190)the Natural Science Foundation of Hubei Province,China(2022CFA031)。
文摘Diffusive threshold switching(TS)memristors have emerged as a promising candidate for artificial neurons,effectively replicating neuronal functions and enabling spiking neural networks(SNNs)to emulate the low-power processing of biological brains.In this study,we present an artificial neuron based on a Pt/Ag/ZnO/Pt volatile memristor,which exhibits exceptional TS characteristics,including electro-forming-free operation,low voltage requirements(<0.2 V),high stability(2.25%variation over 1024 cycles),a high on/off ratio(106),and inherent self-compliance.These Pt/Ag/ZnO/Pt diffusive memristors are employed to simultaneously emulate oscillation neurons and leaky integrate-and-fire(LIF)neurons,enabling precise modulation of oscillation and firing frequencies through pulse parameters while maintaining low energy consumption(1.442 nJ per spike).We further integrate the oscillation and LIF neurons as input and output neurons,respectively,in a two-layer SNN,achieving a high classification accuracy of 89.17%on MNIST-based voltage images.This work underscores the potential of ZnO diffusive memristors in emulating stable artificial neurons and highlights their promise for advanced neuromorphic computing applications using SNNs.
基金supported by a grant from Beijing Municipal Commission of Science and Technology of China,No.Z151100000915070
文摘The capability of neurons to discriminate between intensity of external stimulus is measured by its dynamic range.A larger dynamic range indicates a greater probability of neuronal survival.In this study,the potential roles of adaptation mechanisms（ion currents） in modulating neuronal dynamic range were numerically investigated.Based on the adaptive exponential integrate-and-fire model,which includes two different adaptation mechanisms,i.e.subthreshold and suprathreshold（spike-triggered） adaptation,our results reveal that the two adaptation mechanisms exhibit rather different roles in regulating neuronal dynamic range.Specifically,subthreshold adaptation acts as a negative factor that observably decreases the neuronal dynamic range,while suprathreshold adaptation has little influence on the neuronal dynamic range.Moreover,when stochastic noise was introduced into the adaptation mechanisms,the dynamic range was apparently enhanced,regardless of what state the neuron was in,e.g.adaptive or non-adaptive.Our model results suggested that the neuronal dynamic range can be differentially modulated by different adaptation mechanisms.Additionally,noise was a non-ignorable factor,which could effectively modulate the neuronal dynamic range.
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.
基金partially supported by the National Key R&D Program of China(Project Nos.2020YFA0712000,2021YFA1001200)the National Natural Science Foundation of China(Grant Nos.12031013,12171013)+1 种基金partially supported by the National Natural Science Foundation of China(Grant Nos.12171026,U2230402 and 12031013)Foundation of President of China Academy of Engineering Physics(YZJJZQ2022017).
文摘In this work,we consider the Fokker-Planck equation of the Nonlinear Noisy Leaky Integrate-and-Fire(NNLIF)model for neuron networks.Due to the firing events of neurons at the microscopic level,this Fokker-Planck equation contains dynamic boundary conditions involving specific internal points.To efficiently solve this problem and explore the properties of the unknown,we construct a flexible numerical scheme for the Fokker-Planck equation in the framework of spectral methods that can accurately handle the dynamic boundary condition.This numerical scheme is stable with suitable choices of test function spaces,and asymptotic preserving,and it is easily extendable to variant models with multiple time scales.We also present extensive numerical examples to verify the scheme properties,including order of convergence and time efficiency,and explore unique properties of the model,including blow-up phenomena for the NNLIF model and learning and discriminative properties for the NNLIF model with learning rules.
