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

IJBC 2019

Yoko UWATE, Yuji TAKAMARU, Thomas OTT and Yoshifumi NISHIO
"Clustering Using Chaotic Circuit Networks with Weighted Couplings"
International Journal of Bifurcation and Chaos, vol. 29, no. 4, pp. 1950053_1-19, Apr. 2019.

Chaos 2019

Yoko UWATE and Yoshifumi NISHIO
"Competitive Networks Using Chaotic Circuits with Hierarchical Structure"
Chaos, vol. 29, no. 8, pp. 083115_1-9, Aug. 2019.

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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)

NCSP 2019

Yoko UWATE, Marie Engelene J. OBIEN, Urs FREY and Yoshifumi NISHIO
"Time Series Analysis of Neurons and Visualization of Network Characteristics"
Proceedings of RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'19), pp. 450-453, Mar. 2019.

NCSP 2020

Yoko UWATE, Marie Engelene J. OBIEN, Urs FREY and Yoshifumi NISHIO
"Modeling of Bursting Neurons and Its Characteristic using Nonlinear Time Series Analysis"
Proceedings of RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'20), pp. 233-236, Mar. 2020.