Machine Learning Link Inference of Noisy Delay-Coupled Networks with Optoelectronic Experimental Tests
Machine Learning Link Inference of Noisy Delay-Coupled Networks with Optoelectronic Experimental Tests
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DOI：
10.1103/physrevx.11.031014
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发表时间：
2021-07-20
影响因子：
12.5
通讯作者：
Ott, Edward
中科院分区：
文献类型：
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作者：
Banerjee, Amitava;Hart, Joseph D.;Ott, Edward
我们设计了一个机器学习技术来解决一般的问题，推断网络链路的时间延迟，仅使用时间序列数据的网络节点状态。该任务在许多领域中具有应用，例如，从应用物理学、数据科学和工程学到神经科学和生物学。我们的方法是首先训练一种称为水库计算的机器学习系统，以模拟未知网络的动态。然后，我们使用水库系统输出层的训练参数来推导未知网络结构的估计。我们的技术本质上是非侵入性的，但受到广泛使用的侵入性网络推理方法的激励，由此观察并采用对应用于网络的主动扰动的响应来推断网络链路（例如，敲除基因以推断基因调控网络）。我们测试这种技术的实验和模拟数据从延迟耦合光电振荡器网络，具有相同的和异构的延迟沿着的链接。我们表明，该技术往往会产生非常好的结果，特别是如果系统不表现出同步。我们还发现，动态噪声的存在可以显着提高我们的技术的准确性和能力，特别是在网络表现出同步。
We devise a machine learning technique to solve the general problem of inferring network links that have time delays using only time series data of the network nodal states. This task has applications in many fields, e.g., from applied physics, data science, and engineering to neuroscience and biology. Our approach is to first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network. We then use the trained parameters of the reservoir system output layer to deduce an estimate of the unknown network structure. Our technique, by its nature, is noninvasive but is motivated by the widely used invasive network inference method, whereby the responses to active perturbations applied to the network are observed and employed to infer network links (e.g., knocking down genes to infer gene regulatory networks). We test this technique on experimental and simulated data from delay-coupled optoelectronic oscillator networks, with both identical and heterogeneous delays along the links. We show that the technique often yields very good results, particularly if the system does not exhibit synchrony. We also find that the presence of dynamical noise can strikingly enhance the accuracy and ability of our technique, especially in networks that exhibit synchrony.