Integrate-and-Fire模型输入的最优解码

研究了随机相关输入的Integrate-and-Fire(IF)神经元模型的最优解码问题.使用Fisher信息,在理论上解决了这样一个问题:当抑制和兴奋输入的比r取何值时,神经元能最精确地解码IF神经元的输入.指出相关输入整体上减小了解码的精确性.

低相关更新输入影响Integrate-and-Fire模型的输出

研究了随机更新输入的IF神经元模型的近似问题,得到了两种新的近似方案.讨论了低相关更新输入对Integrate-and-Fire模型输出的影响.对低的正相关,随着输入相关的增加,平均发放时间缩短.对低的负相关,平均发放时间独立于输入相关.

The Anomaly Detection in SMTP Traffic Based on Leaky Integrate-and-Fire Model

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.

EVENT-DRIVEN SIMULATION OF INTEGRATE-AND-FIRE MODELS WITH SPIKE-FREQUENCY ADAPTATION

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

分段线性脉冲神经元模型的动力学特性分析

结合Hodgkin-Huxley神经元模型的动力学特性与Integrate-and-Fire神经元模型的解析特性,提出了一种新的二维分段线性脉冲神经元模型.该模型的优点在于既可通过分叉理论对兴奋性系统进行定性描述,又可通过状态变量的解析式对神经元行为进行定量分析.通过详细的分析,发现该模型具有许多一维Integrate-and-Fire神经元模型所不具有的新的神经计算特性.在实验中,应用该模型模拟了大部分已知皮层神经元的脉冲和簇放电行为.

Modulation of neuronal dynamic range using two different adaptation mechanisms

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.

Signal-to-noise ratio gain of an adaptive neuron model with Gamma renewal synaptic input

We take an adaptive leaky integrate-and-fire neuron model to explore the effect of non-Poisson neurotransmitter on stochastic resonance and its signal-to-noise ratio(SNR)gain.Event triggered algorithm is adopted to speed up the simulating process.It is revealed that both the output SNR and the SNR gain can be monotonically improved when increasing the shape parameter for Gamma distribution.Particularly,for large signal coupling strength,the 1:1 stochastic phase locking induced by Gamma noise is responsible for the frequency matching stochastic resonance,and the output signal-to-noise ratio can surpass the input signal-to-noise ratio,which is significantly different with Poisson case,while for extremely weak signal coupling strength,the SNR gain peak,which is far larger than unity,is due to noise induced resonance.The observations are meaningful in understanding the neural processing mechanisms from a more realistic viewpoint of synaptic modeling.