Reinforcement Learning Algorithm with CTRNN in Continuous Action Space. - Hiroaki Arie; Jun Namikawa; Tetsuya Ogata; Jun Tani; Shigeki Sugano (ICONIP (1), 2006)

Learning Multiple Goal-Directed Actions Through Self-Organization of a Dynamic Neural Network Model: A Humanoid Robot Experiment. - Ryunosuke Nishimoto; Jun Namikawa; Jun Tani (Adapt. Behav., 2008)

A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance. - Jun Namikawa; Jun Tani (Neural Networks, 2008)

Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model. - Jun Tani; Ryunosuke Nishimoto; Jun Namikawa; Masato Ito (IEEE Trans. Syst. Man Cybern. Part B, 2008)

Building Recurrent Neural Networks to Implement Multiple Attractor Dynamics Using the Gradient Descent Method. - Jun Namikawa; Jun Tani (Adv. Artif. Neural Syst., 2009)

Learning to imitate stochastic time series in a compositional way by chaos. - Jun Namikawa; Jun Tani (Neural Networks, 2010)

A Neurodynamic Account of Spontaneous Behaviour. - Jun Namikawa; Ryunosuke Nishimoto; Jun Tani (PLoS Comput. Biol., 2011)

Estimation of functional brain connectivity from electrocorticograms using an artificial network model. - Misako Komatsu; Jun Namikawa; Jun Tani; Zenas C. Chao; Yasuo Nagasaka; Naotaka Fujii; Kiyohiko Nakamura (IJCNN, 2012)

Learning to reproduce fluctuating behavioral sequences using a dynamic neural network model with time-varying variance estimation mechanism. - Shingo Murata; Jun Namikawa; Hiroaki Arie; Jun Tani; Shigeki Sugano (ICDL-EPIROB, 2013)

Development of Proactive and Reactive Behavior via Meta-learning of Prediction Error Variance. - Shingo Murata; Jun Namikawa; Hiroaki Arie; Jun Tani; Shigeki Sugano (ICONIP (1), 2013)

Learning to Reproduce Fluctuating Time Series by Inferring Their Time-Dependent Stochastic Properties: Application in Robot Learning Via Tutoring. - Shingo Murata; Jun Namikawa; Hiroaki Arie; Shigeki Sugano; Jun Tani (IEEE Trans. Auton. Ment. Dev., 2013)