A novel tuning method for neural oscillators with a ladder-like structure based on oscillation analysis

Authors:
Yuya Hattori;Michiyo Suzuki;Zu Soh;Yasuhiko Kobayashi;Toshio Tsuji
Affiliations:
Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Hiroshima, Japan and Japan Atomic Energy Agency, Takasaki, Gunma, Japan;Japan Atomic Energy Agency, Takasaki, Gunma, Japan;Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Hiroshima, Japan;Japan Atomic Energy Agency, Takasaki, Gunma, Japan;Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Hiroshima, Japan
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

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Abstract

Neural oscillators with a ladder-like structure is one of the central pattern generator (CPG) model that is used to simulate rhythmic movements in living organisms. However, it is not easy to realize rhythmical cycles by tuning many parameters of neural oscillators. In this study, we propose an automatic tuning method. We derive the tuning rules for both the time constants and the coefficients of amplitude by linearizing the nonlinear equations of the neural oscillators. Other parameters such as neural connection weights are tuned using a genetic algorithm (GA). Through numerical experiments, we confirmed that the proposed tuning method can successfully tune all parameters.