Department of Otorhinolaryngology, University Erlangen, Nürnberg, Germany.
Department of Physics, University Erlangen, Nürnberg, Germany.
Sci Rep. 2017 May 26;7(1):2450. doi: 10.1038/s41598-017-02644-w.
All sensors have a threshold, defined by the smallest signal amplitude that can be detected. The detection of sub-threshold signals, however, is possible by using the principle of stochastic resonance, where noise is added to the input signal so that it randomly exceeds the sensor threshold. The choice of an optimal noise level that maximizes the mutual information between sensor input and output, however, requires knowledge of the input signal, which is not available in most practical applications. Here we demonstrate that the autocorrelation of the sensor output alone is sufficient to find this optimal noise level. Furthermore, we demonstrate numerically and analytically the equivalence of the traditional mutual information approach and our autocorrelation approach for a range of model systems. We furthermore show how the level of added noise can be continuously adapted even to highly variable, unknown input signals via a feedback loop. Finally, we present evidence that adaptive stochastic resonance based on the autocorrelation of the sensor output may be a fundamental principle in neuronal systems.
所有传感器都有一个阈值，由能够检测到的最小信号幅度定义。然而，通过使用随机共振原理，可以检测到低于阈值的信号，其中向输入信号添加噪声，使得它随机超过传感器阈值。然而，选择最大化传感器输入和输出之间互信息的最佳噪声水平需要输入信号的知识，而在大多数实际应用中，这是不可用的。在这里，我们证明仅传感器输出的自相关就足以找到这个最佳噪声水平。此外，我们数值和分析地证明了传统的互信息方法和我们的自相关方法在一系列模型系统中的等效性。我们还展示了如何通过反馈环，即使对于高度变化的、未知的输入信号，也可以连续自适应地添加噪声。最后，我们提出的证据表明，基于传感器输出自相关的自适应随机共振可能是神经元系统的一个基本原理。
