Duan Fabing, Chapeau-Blondeau François, Abbott Derek
Institute of Complexity Science, Qingdao University, Qingdao, P. R. China.
Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, Angers, France.
PLoS One. 2014 Mar 14;9(3):e91345. doi: 10.1371/journal.pone.0091345. eCollection 2014.
We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity-a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.
我们分析了在存在有色噪声的情况下使用非线性检验统计量进行信号检测的情况。在小信号和弱噪声相关性的极限条件下，在一般条件下，特别是关于噪声类型，推导了最优检验统计量及其性能。我们还针对阈值非线性（神经模型的关键组成部分）分析了噪声增强性能的条件，确定了有色噪声在检测方面优于白噪声。对于由近似神经元的非线性元件组成的并行阵列，我们通过一种阈上随机共振形式证明了甚至更广泛的条件允许噪声增强检测。
