The human nervous system is one of the most complicated systems in nature. The complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input-output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a small amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances on nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. They also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention.

中文翻译：

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神经生理信号对神经系统的非线性系统识别

人的神经系统是自然界中最复杂的系统之一。从单个神经元级别到系统级别，已经显示出复杂的非线性行为。数十年来，线性连通性分析方法（例如相关性，相干性和Granger因果关系）已被广泛用于评估神经系统中的神经连通性和输入输出互连。最近的研究表明，这些线性方法只能捕获少量的神经活动和功能关系，因此无法以精确或完整的方式描述神经行为。在这篇综述中，我们重点介绍了神经系统的非线性系统识别，相应的时域和频域分析以及基于非线性系统识别技术的新型神经连通性度量的最新进展。我们认为非线性建模和分析对于定量研究神经系统中的神经元处理和信号传递是必要的。这些方法有望提供新的见识，以增进我们对神经功能基础的神经生理机制的理解。它们还可能产生敏感的生物标志物，以促进精密诊断工具的开发，以评估神经系统疾病和靶向干预的效果。