It has been proved recently that the spike timing can play an important role in information transmission, so in this paper we develop a network with N-unlt FitzHugh-Nagumo neurons coupled by gap junctions and discuss ...It has been proved recently that the spike timing can play an important role in information transmission, so in this paper we develop a network with N-unlt FitzHugh-Nagumo neurons coupled by gap junctions and discuss the dependence of the spike timing precision on synaptic coupling strength, the noise intensity and the size of the neuron ensemble. The calculated results show that the spike timing precision decreases as the noise intensity increases; and the ensemble spike timing precision increases with coupling strength increasing. The electric synapse coupling has a more important effect on the spike timing precision than the chemical synapse coupling.展开更多
Neural activity extraction and neural decoding from neural signals are an important part of critical components of brain-computer interface systems.With the development of brain-computer interface technology,the deman...Neural activity extraction and neural decoding from neural signals are an important part of critical components of brain-computer interface systems.With the development of brain-computer interface technology,the demand for precise external control and nervous activities in macaque monkey during unilateral hand grasp has increased the complexity of control and neural decoding,which puts forward higher requirements for the accuracy and stability of feature extraction and neural decoding.In this study,a micro Capsnet network architecture that consists of a few network layers,a vector feature structure,and optimization network parameters,is proposed to decrease the computing time and complexity,decrease artificial debugging,and improve the decoding accuracy.Compared with KNN,SVM,XGBOOST,CNN,Simple RNN,and LSTM,the algorithm in this study improves the decoding accuracy by 98.03%,and achieves state-of-the-art accuracy and stronger robustness.Furthermore,the proposed algorithm can further enhance the control accuracy in the brain-computer interface.展开更多
Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics ...Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.展开更多
基金Project supported by the National Natural Science Foundation of China （Grant Nos 10375016 and 10474018） and the Natural Science Foundation of Hebei Province （Grant Nos C2005000011and A2004000005） and the Key Subject Construction Project of Hebei Provincial University.
文摘It has been proved recently that the spike timing can play an important role in information transmission, so in this paper we develop a network with N-unlt FitzHugh-Nagumo neurons coupled by gap junctions and discuss the dependence of the spike timing precision on synaptic coupling strength, the noise intensity and the size of the neuron ensemble. The calculated results show that the spike timing precision decreases as the noise intensity increases; and the ensemble spike timing precision increases with coupling strength increasing. The electric synapse coupling has a more important effect on the spike timing precision than the chemical synapse coupling.
基金supported by the Research Fund of Science and Technology Innovation 2030-Major Project(Grant No.2021ZD0201600)the Research Fund of PLA of China(Grant Nos.AWS17J011 and BWS17J024)。
文摘Neural activity extraction and neural decoding from neural signals are an important part of critical components of brain-computer interface systems.With the development of brain-computer interface technology,the demand for precise external control and nervous activities in macaque monkey during unilateral hand grasp has increased the complexity of control and neural decoding,which puts forward higher requirements for the accuracy and stability of feature extraction and neural decoding.In this study,a micro Capsnet network architecture that consists of a few network layers,a vector feature structure,and optimization network parameters,is proposed to decrease the computing time and complexity,decrease artificial debugging,and improve the decoding accuracy.Compared with KNN,SVM,XGBOOST,CNN,Simple RNN,and LSTM,the algorithm in this study improves the decoding accuracy by 98.03%,and achieves state-of-the-art accuracy and stronger robustness.Furthermore,the proposed algorithm can further enhance the control accuracy in the brain-computer interface.
基金supported by National Key R&D Program of China(2019YFB2103202).
文摘Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.
