Universidad Nacional Autónoma de México, Instituto de Ciencias Físicas, Cuernavaca, Mexico.
Indiana University, Department of Psychological and Brain Sciences, Bloomington IN, USA.
Sci Rep. 2017 Oct 12;7(1):13020. doi: 10.1038/s41598-017-13400-5.
Stochastic resonance is a phenomenon in which noise enhances the response of a system to an input signal. The brain is an example of a system that has to detect and transmit signals in a noisy environment, suggesting that it is a good candidate to take advantage of stochastic resonance. In this work, we aim to identify the optimal levels of noise that promote signal transmission through a simple network model of the human brain. Specifically, using a dynamic model implemented on an anatomical brain network (connectome), we investigate the similarity between an input signal and a signal that has traveled across the network while the system is subject to different noise levels. We find that non-zero levels of noise enhance the similarity between the input signal and the signal that has traveled through the system. The optimal noise level is not unique; rather, there is a set of parameter values at which the information is transmitted with greater precision, this set corresponds to the parameter values that place the system in a critical regime. The multiplicity of critical points in our model allows it to adapt to different noise situations and remain at criticality.
随机共振是一种噪声增强系统对输入信号响应的现象。大脑是一个在嘈杂环境中必须检测和传输信号的系统的例子，这表明它是利用随机共振的一个很好的候选者。在这项工作中，我们旨在通过一个简单的人类大脑网络模型来确定促进信号传输的最佳噪声水平。具体来说，我们使用基于解剖学脑网络（连接组）的动态模型来研究输入信号和在系统受到不同噪声水平影响时穿过网络传播的信号之间的相似性。我们发现，非零水平的噪声增强了输入信号和在系统中传播的信号之间的相似性。最佳噪声水平不是唯一的；而是存在一组参数值，在这些参数值下，信息以更高的精度传输，这组参数值对应于将系统置于临界状态的参数值。我们模型中的多个临界点使其能够适应不同的噪声情况并保持在临界状态。
