Namiko Saito, Tetsuya Ogata, Hiroki Mori, Shingo Murata, and Shigeki Sugano, “Tool-use Model to Reproduce the Goal Situations Considering Relationship among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks,” Accepted, Frontiers in Robotics and AI, 2021.
Yuta Takahashi, Shingo Murata, Hayato Idei, Hiroaki Tomita, and Yuichi Yamashita, “Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework,” Scientific Reports, Vol. 11, Article number: 14684, pp. 1–14, 2021. DOI: 10.1038/s41598-021-94067-x / PsyArXiv
Press Release: Keio Univ. (in Japanese) / Tohoku Univ. (in English)
Hayato Idei, Shingo Murata, Yuichi Yamashita, and Tetsuya Ogata, “Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder,” Neural Networks, Vol. 138, pp. 150–163, 2021. DOI: 10.1016/j.neunet.2021.01.033
Hayato Idei, Shingo Murata, Yuichi Yamashita, and Tetsuya Ogata, “Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder,” Frontiers in Psychiatry, Vol. 11, Article 762, pp. 1–15, 2020. DOI: 10.3389/fpsyt.2020.00762
Press Release (in Japanese): NCNP / Waseda Univ.
出井勇人, 村田真悟, 尾形哲也, 山下祐一, “不確実性の推定と自閉スペクトラム症－神経ロボティクス実験による症状シミュレーション,” 精神医学, Vol. 62, No. 2, pp. 219–229, 2020. DOI: 10.11477/mf.1405206009
Hayato Idei, Shingo Murata, Yiwen Chen, Yuichi Yamashita, Jun Tani, and Tetsuya Ogata, “A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision,” Computational Psychiatry, Vol. 2, pp. 164–182, 2018. DOI: 10.1162/cpsy_a_00019
Ryoichi Nakajo, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Acquisition of Viewpoint Transformation and Action Mappings via Sequence to Sequence Imitative Learning by Deep Neural Networks,” Frontiers in Neurorobotics, Vol. 12, Article 46, pp. 1–14, 2018. DOI: 10.3389/fnbot.2018.00046
Shingo Murata, Yuxi Li, Hiroaki Arie, Tetsuya Ogata, and Shigeki Sugano, “Learning to Achieve Different Levels of Adaptability for Human–Robot Collaboration Utilizing a Neuro-dynamical System,” IEEE Transactions on Cognitive and Developmental Systems, Vol. 10, Issue 3, pp. 712–725, 2018. DOI: 10.1109/TCDS.2018.2797260
Tatsuro Yamada, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions,” Frontiers in Neurorobotics, Vol. 11, Article 70, pp. 1–18, 2017. DOI: 10.3389/fnbot.2017.00070
Shingo Murata, Yuichi Yamashita, Hiroaki Arie, Tetsuya Ogata, Shigeki Sugano, and Jun Tani, “Learning to Perceive the World as Probabilistic or Deterministic via Interaction with Others: A Neuro-Robotics Experiment,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, Issue 4, pp. 830–846, 2017. DOI: 10.1109/TNNLS.2015.2492140
Tatsuro Yamada, Shingo Murata, Hiroaki Arie, and Tetsuya Ogata, “Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction,” Frontiers in Neurorobotics, Vol. 10, Article 5, pp. 1–17, 2016. DOI: 10.3389/fnbot.2016.00005
Shingo Murata, Hiroaki Arie, Tetsuya Ogata, Shigeki Sugano, and Jun Tani, “Learning to Generate Proactive and Reactive Behavior Using a Dynamic Neural Network Model with Time-Varying Variance Prediction Mechanism,” Advanced Robotics, Vol. 28, Issue 17, pp. 1189–1203, 2014. DOI: 10.1080/01691864.2014.916628
Shingo Murata, Jun Namikawa, Hiroaki Arie, Shigeki Sugano, and Jun Tani, “Learning to Reproduce Fluctuating Time Series by Inferring Their Time-Dependent Stochastic Properties: Application in Robot Learning via Tutoring,” IEEE Transactions on Autonomous Mental Development, Vol. 5, Issue 4, pp. 298–310, 2013. DOI: 10.1109/TAMD.2013.2258019