非高斯噪声激励下FitzHugh-Nagumo神经元系统的随机共振

Stochastic resonance in FHN neural system driven by non-Gaussian noise

研究了乘性非高斯噪声和加性高斯白噪声共同激励下FitzHugh-Nagumo(FHN)神经元系统的随机共振问题.利用路径积分法和两态模型理论,推导出系统信噪比的表达式.研究结果表明:系统参数在不同的取值条件下,FHN神经元模型出现了随机共振和双重随机共振现象.此外,非高斯参数q在不同的取值条件下,乘性噪声强度和加性噪声强度对信噪比的影响是不同的.非高斯噪声的加入有利于增强FHN神经元系统的信号响应.

Stochastic resonance （SR） is studied in the FitzHugh-Nagumo （FHN） neural system subject to multiplicative non-Gaussian noise, additive Gaussian white noise and a periodic signal. Using the path integral approach and the two-state theory, the expression of the signal-to-noise ratio （SNR） is derived. The simulation results show that conventional SR and double SR occur in the FHN neural model under different values of system parameters. The effects of the additive and multiplicative noise intensities on SNR are different. Moreover, the addition of non-Ganssian noise is conductive to the enhancement of the response to the output signal of the FHN neural system.