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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 levelsfrom single-particle movements. Reliable RTS analyses are crucial prerequisiteto identify underlying mechanisms related to performance sensitivity. Whennumerous levels partake, complex patterns of multilevel RTSs occur, makingtheir quantitative analysis exponentially difficult, hereby systematicapproaches are found elusive. Here, we present a three-step analysis protocolvia progressive knowledge-transfer, where the outputs of early step are passedonto a subsequent step. Especially, to quantify complex RTSs, we build threedeep neural network architectures that can process temporal data well anddemonstrate the model accuracy extensively with a large dataset of differentRTS types affected by controlling background noise size. Our protocol offersstructured schemes to quantify complex RTSs from which meaningfulinterpretation and inference can ensue.

Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals