2025-09-12 東京大学
振動子の観測データから振動子同士のつながり（ネットワーク）を推定
＜関連情報＞
- https://www.k.u-tokyo.ac.jp/information/category/press/11742.html
- https://www.k.u-tokyo.ac.jp/information/upload/08440875c7ace024cae217c226dbcb1d5d0345e5.pdf
- https://journals.plos.org/complexsystems/article?id=10.1371/journal.pcsy.0000063
振動信号から同期システムと非同期システムの両方に適用可能なネットワーク推定 Network inference applicable to both synchronous and desynchronous systems from oscillatory signals
Akari Matsuki ,Hiroshi Kori,Ryota Kobayashi
PLOS Complex Systems Published: September 11, 2025
DOI:https://doi.org/10.1371/journal.pcsy.0000063
Abstract
Synchronization is ubiquitous in nature, which is mathematically described by coupled oscillators. Synchronization strongly depends on the interaction network, and the network plays a crucial role in controlling the dynamics. To understand and control synchronization dynamics in the real world, it is essential to identify the network from the observed data. While previous studies have developed the methods for inferring the network of asynchronous systems, it remains challenging to infer the network of well-synchronized oscillators. In this study, we develop a method for inferring the network of synchronized and desynchronized oscillators from time series. Our method expands the applicability of network inference to a wider class of oscillatory systems. The proposed method discards a large part of data used for inference, which may seem counterintuitive. However, the effectiveness of the method is supported by the phase reduction theory, a well-established theory for weakly coupled oscillators. We verify the proposed method by applying it to simulated data of the limit-cycle oscillators. This study provides an important step towards understanding synchronization in real-world systems from a network perspective.
Author summary
Synchronization is an emergent phenomenon observed in populations of dynamically interacting units, which plays a crucial role across various systems, including physical, biological, chemical, engineering, and social domains. The network topology and the strength of coupling between elements significantly influence synchronization properties. To understand synchronization dynamics in real-world systems, it is essential to identify the interaction network from observed data. In this study, we propose a novel method for inferring the interaction network from oscillatory signals, which is based on the phase reduction theory for weakly coupled oscillators. Our method extends the applicability of network inference to a broader class of oscillatory systems. While the proposed method discards a substantial portion of the data, it enables accurate inference even when oscillators are highly synchronized, a situation that poses considerable challenges for existing methods. The effectiveness of the proposed method is demonstrated for a range of synthetic data, from simple phase oscillator models to biologically realistic clock cell models. This study represents an important step towards understanding synchronization mechanisms in real-world systems from a network perspective.
