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

TL;DR

This paper introduces a three-step deep learning-based analysis protocol for complex random telegraph signals, enabling accurate quantification and interpretation of multilevel RTS patterns in noisy environments.

Contribution

It develops three novel deep neural network architectures and a systematic analysis protocol for quantifying complex RTSs, addressing challenges in multilevel signal analysis.

Findings

High model accuracy demonstrated on large RTS datasets

Effective quantification of multilevel RTS patterns

Structured analysis scheme enables meaningful interpretation

Abstract

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.