Tougaard Jakob
Centre for Sound Communication, Institute of Biology, SDU/Odense University, Campusvej 55, 5230 Odense M, Denmark.
Biol Cybern. 2002 Aug;87(2):79-90. doi: 10.1007/s00422-002-0327-0.
Stochastic resonance is a phenomenon in which the performance of certain non-linear detectors can be enhanced by the addition of appropriate levels of random noise. Signal detection theory offers a powerful tool for analysing this type of system, through an ability to separate detection processes into reception and classification, with the former generally being linear and the latter always non-linear. Through appropriate measures of signal detectability it is possible to decide whether a local improvement in detection via stochastic resonance occurs due to the non-linear effects of the classification process. In this case, improvement of detection through the addition of noise can never improve detection beyond that of a corresponding adaptive system. Signal detection and stochastic resonance is investigated in several integrate-and-fire neuron models. It is demonstrated that the stochastic resonance observed in spiking models is caused by non-linear properties of the spike-generation process itself. The true detectability of the signal, as seen by the receiver part of the spiking neuron (the integrator part), decreases monotonically with input noise level for all signal and noise intensities.
随机共振是一种现象，在这种现象中，通过添加适当水平的随机噪声可以增强某些非线性探测器的性能。信号检测理论为分析这类系统提供了一个强大的工具，它能够将检测过程分为接收和分类，前者通常是线性的，而后者总是非线性的。通过对信号可检测性的适当度量，可以确定通过随机共振实现的检测局部改进是否是由于分类过程的非线性效应所致。在这种情况下，通过添加噪声来改进检测永远无法使检测效果超过相应的自适应系统。在几种积分发放神经元模型中研究了信号检测和随机共振。结果表明，在发放模型中观察到的随机共振是由发放产生过程本身的非线性特性引起的。对于所有信号和噪声强度，发放神经元的接收部分（积分器部分）所看到的信号的真实可检测性随输入噪声水平单调下降。
