Parameter inference based on phase oscillator models from oscillatory or spike data (Online)

Abstract

ynchronization of rhythmic units is essential for various biological functions. The synchronization mechanism is often governed by neural networks. For example, the circadian rhythm in mammals functions by synchronizing the gene expression rhythms of individual neurons within a neural tissue called the suprachiasmatic nucleus. Various movement patterns observed in animal locomotion, such as walking and swimming, are generated by neural networks known as central pattern generators. The synchronization dynamics of oscillator groups strongly depend on the heterogeneity of intrinsic frequencies, noise intensity, and the interaction network. If these factors can be estimated from observations, it can aid in understanding, predicting, and controlling the system, and contribute to elucidating the design principles of robust systems. However, estimation involves many challenges. Among them, estimation becomes exponentially difficult as the model's dimensions and the number of parameters increase. Therefore, it is desirable to assume a model with as low dimensions and as few parameters as possible. In the synchronization phenomena of oscillator groups, the phase oscillator model is expected to be useful. This presentation introduces research on estimation using the phase oscillator model [1,2]. [1]A Matsuki, H Kori, R Kobayashi: Network inference from oscillatory signals based on circle map, arXiv:2407.07445 (2024) [2]F Mori, H Kori: Noninvasive inference methods for interaction and noise intensities of coupled oscillators using only spike time data, Proceedings of the National Academy of Sciences 119, e2113620119 (2022)