Yoko's Research Topics

Synchronization in Coupled Oscillators

Coupled chaotic circuits can be realized using electronic circuits and various interesting phenomena can be observed in these circuits. In recent years, many studies have reported on the application of clustering and synchronization phenomena that can be observed in coupled chaotic circuits to natural sciences. The reason for this interest is that the characteristics of the chaos phenomena observed in coupled chaotic circuits also exist in real life, in phenomena such as human behavior, emotions and heartbeats. At the same time, synchronization and clustering phenomena have been studied associated with the chaos phenomena.

Coupled chaotic circuits thus have the potential to be applied to a variety of different fields. We believe that we can apply the synchronization phenomena of coupled chaotic circuits to social networks in real life if we can clear up the chaos phenomena. Therefore, our study considers a new approach to investigation of the synchronization and clustering phenomena that occur in coupled chaotic circuits.

Latest papers

IEICE NOLTA 2023

Yoko UWATE, Kenta AGO and Yoshifumi NISHIO
"Dynamics of Chaotic Circuit Betworks with Local Bridgess"
Nonlinear Theory and Its Applications, IEICE, Vol.14, No.2, 534-546, 2023.

IEEE JETCAS 2023

Sohei SHIMA, Tsuyoshi ISOZAKI, Yoko UWATE and Yoshifumi NISHIO
"Investigating Packet Transmission From the Perspective of Complex Networks Composed of Chaotic Circuits"
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 13, no. 4, pp. 658 - 668, Sep. 2023.

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Nonlinear Time Series Analysis for Biological Neurons

Understanding how brain circuits develop and operate is a major goal for many neuroscience projects. Burst patterns in neuronal networks may have an important role in information processing in the brain. Therefore, detecting and analyzing burst patterns are investigated in various fields. Although it is important to study burst patterns in order to understand the correlation and communication processes of neurons, unveiling a structure of the whole neuronal network is also required.

Nonlinear time-series analysis is a useful tool for characterizing the dynamics behind the observed time-series data. The neuronal data obtained from living neurons should be high-dimensional and of dynamic nature. In such a case, nonlinear time-series analysis can be used to characterize the neuronal data.

Latest papers (International Conference Proceedings)

MxW Summit 2024

Yoko UWATE, Marie Engelene J. OBIEN, Urs FREY and Yoshifumi NISHIO
"Visualization of Neuronal Activitiy Using Attractor Reconstruction"
MxW Summit 2024, Zurich, Switzerland, Apr. 2024 (Poster presentation).

ISCAS 2024

Yoko UWATE, Marie Engelene J. OBIEN, Urs FREY and Yoshifumi NISHIO
"Feature Extraction of Neuronal Activityby Attractor Reconstruction in Neural Networks with Delayed Couplings"
Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS'24), May 2024.