We study the stochastic resonance （SR） in Hodgkin-Huxley （HH） neural systems with small-world （SW） connections under the noise synaptic current and periodic stimulus, focusing on the dependence of properties of ...We study the stochastic resonance （SR） in Hodgkin-Huxley （HH） neural systems with small-world （SW） connections under the noise synaptic current and periodic stimulus, focusing on the dependence of properties of SR on coupling strength c. It is found that there exists a critical coupling strength c^＊ such that if c 〈 c^＊, then the SR can appear on the SW neural network. Especially, dependence of the critical coupling strength c^＊ on the number of neurons N shows the monotonic even almost linear increase of c^＊ as N increases and c^＊ on the SW network is smaller than that on the random network. For the effect of the SW network on the phenomenon of SR, we show that decreasing the connection-rewiring probability p of the network topology leads to an enhancement of SR. This indicates that the SR on the SW network is more prominent than that on the random network （p = 1.0）. In addition, it is noted that the effect becomes remarkable as coupling strength increases. Moreover, it is found that the SR weakens but resonance range becomes wider with the increase of c on the SW neural network.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
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
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
基金Supported by the National Natural Science Foundation of China under Grant Nos 70571017 and 10247005, the Innovation Project of Guangxi Graduate Education under Grant No 2006106020809M36, and Key Project of the National Natural Science Foundation of China under Grant No 70431002.
文摘We study the stochastic resonance （SR） in Hodgkin-Huxley （HH） neural systems with small-world （SW） connections under the noise synaptic current and periodic stimulus, focusing on the dependence of properties of SR on coupling strength c. It is found that there exists a critical coupling strength c^＊ such that if c 〈 c^＊, then the SR can appear on the SW neural network. Especially, dependence of the critical coupling strength c^＊ on the number of neurons N shows the monotonic even almost linear increase of c^＊ as N increases and c^＊ on the SW network is smaller than that on the random network. For the effect of the SW network on the phenomenon of SR, we show that decreasing the connection-rewiring probability p of the network topology leads to an enhancement of SR. This indicates that the SR on the SW network is more prominent than that on the random network （p = 1.0）. In addition, it is noted that the effect becomes remarkable as coupling strength increases. Moreover, it is found that the SR weakens but resonance range becomes wider with the increase of c on the SW neural network.
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 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.
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
