论文标题
对复杂随机电报信号的多个深神经网络的广泛研究
Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals
论文作者
论文摘要
在许多物理,化学和生物系统中,时间允许信号无处不在,多样性,其中随机电报信号(RTSS)是指单粒子运动中两个离散水平之间的一系列瞬时切换事件。可靠的RTS分析是识别与性能敏感性相关的基本机制的关键先决条件。当大量级别参与其中时,发生多级RTS的复杂模式,使其定量分析成倍困难,从而发现系统的方法难以捉摸。在这里,我们通过渐进知识转移提出了三步分析协议,其中早期步骤的输出将传递到后续步骤中。特别是,为了量化复杂的RTS,我们构建了三个深神经网络体系结构,可以很好地处理时间数据,并使用由控制背景噪声大小影响的不同RTS类型的大型数据集广泛地演示模型精度。我们的协议提供结构化方案来量化复杂的RTSS,从中可以从中进行有意义的解释和推理。
Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. Reliable RTS analyses are crucial prerequisite to identify underlying mechanisms related to performance sensitivity. When numerous levels partake, complex patterns of multilevel RTSs occur, making their quantitative analysis exponentially difficult, hereby systematic approaches are found elusive. Here, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we build three deep neural network architectures that can process temporal data well and demonstrate the model accuracy extensively with a large dataset of different RTS types affected by controlling background noise size. Our protocol offers structured schemes to quantify complex RTSs from which meaningful interpretation and inference can ensue.