MURATA Shingo

写真a

Affiliation

Faculty of Science and Technology, Department of Electronics and Electrical Engineering (Yagami)

Position

Associate Professor

Related Websites
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Career 【 Display / hide 】

  • 2016.04
    -
    2018.03

    Waseda University, Department of Modern Mechanical Engineering, School of Creative Science and Engineering, Research Associate

  • 2018.04
    -
    2020.03

    National Institute of Informatics, Principles of Informatics Research Division, Assistant Professor

  • 2018.04
    -
    2020.03

    The Graduate University for Advanced Studies, Department of Informatics, School of Multidisciplinary Science, Assistant Professor

  • 2020.04
    -
    2024.03

    Keio University, Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Assistant Professor

  • 2024.04
    -
    Present

    Keio University, Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Associate Professor

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Academic Background 【 Display / hide 】

  • 2007.04
    -
    2011.03

    Waseda University, School of Creative Science and Engineering, Department of Modern Mechanical Engineering

    University, Graduated

  • 2011.04
    -
    2013.03

    Waseda University, Graduate School of Creative Science and Engineering, Department of Modern Mechanical Engineering

    Graduate School, Completed, Master's course

  • 2013.04
    -
    2016.03

    Waseda University, Graduate School of Creative Science and Engineering, Department of Modern Mechanical Engineering

    Graduate School, Completed, Doctoral course

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Academic Degrees 【 Display / hide 】

  • Doctor of Engineering, Waseda University, Coursework, 2016.03

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Research Areas 【 Display / hide 】

  • Cognitive Robotics

  • Robot Learning

  • Computational Psychiatry

  • Informatics / Intelligent informatics

  • Informatics / Intelligent robotics

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Research Keywords 【 Display / hide 】

  • Deep Learning

  • Cognitive Robotics

  • ロボット学習

  • Computational Psychiatry

  • 予測符号化

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Books 【 Display / hide 】

  • Cognitive Robotics

    Angelo Cangelosi, Minoru Asada, The MIT Press, 2022.04

    Scope: Machine Learning for Cognitive Robotics

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Papers 【 Display / hide 】

  • World models and predictive coding for cognitive and developmental robotics: frontiers and challenges

    Tadahiro Taniguchi, Shingo Murata, Masahiro Suzuki, Dimitri Ognibene, Pablo Lanillos, Emre Ugur, Lorenzo Jamone, Tomoaki Nakamura, Alejandra Ciria, Bruno Lara, Giovanni Pezzulo

    Advanced Robotics (Informa UK Limited)  37 ( 13 ) 780 - 806 2023.06

    Accepted,  ISSN  0169-1864

    　View Summary

    Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Importantly, if the aim is to create robots that can continuously develop through interactions with their environment, their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e. controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future.

  • Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data

    Yuta Takahashi, Shingo Murata, Masao Ueki, Hiroaki Tomita, Yuichi Yamashita

    Computational Psychiatry (Ubiquity Press, Ltd.)  7 ( 1 ) 14 - 29 2023.01

    Accepted

    　View Summary

    Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (fMRI). A hierarchical recurrent neural network embodying predictive processing theory was subjected to a facial emotion recognition task. Neural network simulations examined the effects of FC and neural excitability on changes in neural representations by developmental learning, and eventually on ASD-like performance. Next, by mapping each neural network condition to subject subgroups on the basis of fMRI parameters, the association between ASD-like performance in the simulation and ASD diagnosis in the corresponding subject subgroup was examined. In the neural network simulation, the more homogeneous the neural excitability of the lower-level network, the more ASD-like the performance (reduced generalization and emotion recognition capability). In addition, in homogeneous networks, the higher the FC, the more ASD-like performance, while in heterogeneous networks, the higher the FC, the less ASD-like performance, demonstrating that FC and neural excitability interact. As an underlying mechanism, neural excitability determines the generalization capability of top-down prediction, and FC determines whether the model’s information processing will be top-down prediction-dependent or bottom-up sensory-input dependent. In fMRI datasets, ASD was actually more prevalent in subject subgroups corresponding to the network condition showing ASD-like performance. The current study suggests an interaction between FC and neural excitability, and presents a novel framework for computational modeling and biological application of a developmental learning process underlying cognitive alterations in ASD.

  • Application of Robotic Predicitve Learning to Computational Psychiatry

    Shingo Murata

    Journal of the Robotics Society of Japan (The Robotics Society of Japan)  40 ( 9 ) 796 - 801 2022.11

    Lead author, Corresponding author,  ISSN  0289-1824

  • Latent Representation in Human-Robot Interaction With Explicit Consideration of Periodic Dynamics

    Taisuke Kobayashi, Shingo Murata, Tetsunari Inamura

    IEEE Transactions on Human-Machine Systems (IEEE Transactions on Human-Machine Systems)  52 ( 5 ) 928 - 940 2022.10

    Accepted,  ISSN  2168-2291

    　View Summary

    This article presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. The model representing pHRI is critical for elaborating human understanding and/or robot control during pHRI. Recent advancements in deep learning technology would allow us to train such a model on a dataset collected from the actual pHRI. Our framework is based on a variational recurrent neural network (VRNN), which can process time-series data generated by a pHRI. This study modifies VRNN to explicitly integrate the latent dynamics from robot to human and to distinguish it from a human state estimate module. Furthermore, to analyze periodic motions, such as walking, we integrate VRNN with a new recurrent network based on reservoir computing (RC), which has random and fixed connections between numerous neurons. By boosting RC into a complex domain, periodic behavior can be represented as phase rotation in the complex domain without decaying the amplitude. A rope rotation/swinging experiment was used to validate the proposed framework. The proposed framework, trained on the collected experiment dataset, achieved the latent state space in which variation in periodic motions can be distinguished. The best prediction accuracy of the human observations and robot actions was obtained in such a well-distinguished space.

  • Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework

    Yuta Takahashi, Shingo Murata, Hayato Idei, Hiroaki Tomita, Yuichi Yamashita

    Scientific Reports (Springer Science and Business Media LLC)  11 ( 1 )  2021.12

    Accepted

    　View Summary

    <title>Abstract</title>The mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown. Therefore, this study proposes a system-level explanation for understanding the facial emotion recognition process and its alteration in autism spectrum disorder (ASD) from the perspective of predictive processing theory. Predictive processing for facial emotion recognition was implemented as a hierarchical recurrent neural network (RNN). The RNNs were trained to predict the dynamic changes of facial expression movies for six basic emotions without explicit emotion labels as a developmental learning process, and were evaluated by the performance of recognizing unseen facial expressions for the test phase. In addition, the causal relationship between the network characteristics assumed in ASD and ASD-like cognition was investigated. After the developmental learning process, emotional clusters emerged in the natural course of self-organization in higher-level neurons, even though emotional labels were not explicitly instructed. In addition, the network successfully recognized unseen test facial sequences by adjusting higher-level activity through the process of minimizing precision-weighted prediction error. In contrast, the network simulating altered intrinsic neural excitability demonstrated reduced generalization capability and impaired emotional clustering in higher-level neurons. Consistent with previous findings from human behavioral studies, an excessive precision estimation of noisy details underlies this ASD-like cognition. These results support the idea that impaired facial emotion recognition in ASD can be explained by altered predictive processing, and provide possible insight for investigating the neurophysiological basis of affective contact.

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Papers, etc., Registered in KOARA 【 Display / hide 】

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Reviews, Commentaries, etc. 【 Display / hide 】

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Presentations 【 Display / hide 】

  • Real-World Robot Control Based on Contrastive Active Inference with Learning from Demonstration

    Kentaro Fujii, Takuya Isomura, Shingo Murata

    The 4th International Workshop on Active Inference, 

    2023.09
    , 

    Poster presentation

  • A Deep Generative Model for Extracting Shared and Private Latent Representations from Multimodal Data

    Kaito Kusumoto, Shingo Murata

    International Symposium on Predictive Brain and Cognitive Feelings,, 

    2023.07
    , 

    Poster presentation

  • Multiple Timescale Recurrent State-Space Model for Learning Long-Horizon Tasks

    Kentaro Fujii, Shingo Murata

    International Symposium on Predictive Brain and Cognitive Feelings, 

    2023.07
    , 

    Poster presentation

  • Action Modification Based on Real-time Amortized Inference of Others’ Intentions Using Backward RNN

    Yukiko Orui, Shingo Murata

    The 54th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS '22), 

    2022.10
    , 

    Oral presentation (general)

  • Analysis of Imitative Interactions between Typically Developed or Autistic Participants and a Robot with a Recurrent Neural Network

    Shingo Murata, Kai Hirano, Naoto Higashi, Shin-ichiro Kumagaya, Yuichi Yamashita, Tetsuya Ogata

    The Ninth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2019), 

    2019.08
    , 

    Other

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • ロボットのための自由エネルギー原理に基づくインタラクション基盤技術

    2024.04
    -
    2027.03

    日本学術振興会, Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), Principal investigator

  • 脳の計算原理とプレイデータに基づく実世界ロボット学習

    2022.10
    -
    2026.03

    科学技術振興機構, 戦略的創造研究推進事業（さきがけ）, Principal investigator

  • Understanding hierarchical statistical learning of sequential perception with dynamic multiple timescale models

    2021.10
    -
    2025.03

    日本学術振興会, Grants-in-Aid for Scientific Research, Fund for the Promotion of Joint International Research (Fostering Joint International Research (B)), Coinvestigator(s)

  • Computational nosology based on artificial intelligence and dimensional approach for psychiatric disorders

    2020.04
    -
    2025.03

    National Center of Neurology and Psychiatry, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (A), Grant-in-Aid for Scientific Research (A), Coinvestigator(s)

    　View Summary

    生物学的知見の蓄積により、現行の精神障害カテゴリーは生物学的妥当性を欠くことが明らかになり、研究方略の抜本的方向修正の必要性が指摘されている。本研究は、精神障害に関する症状・神経生理・認知行動のビッグデータに対して、既存の精神障害カテゴリーにとらわれない疾患横断的・次元的アプローチに基づき、機械学習・人工知能（AI）技術を含む計算論的精神医学の手法を適用することで、新しい精神障害の表現型：症候学的タイプ、バイオタイプ、計算論的表現型を明らかにする。さらに、深層学習技術を用いて、見出された各水準の表現型相互の媒介メカニズムを明らかにすることにより、精神障害の統合的理解と新しい診断体系を創出する。

  • 二個体間における協調の形成と崩壊の予測符号化に基づくロボット構成論的理解

    2019.04
    -
    2022.03

    National Institute of Informatics, Grant-in-Aid for Early-Career Scientists, Principal investigator

    　View Summary

    本研究は他者との協調を支える認知情報処理機構の理解を目的とし，構成論的手法により取り組む．特に，（i）環境変化や他者のふるまいといった外的要因と（ii）自己の将来の行動に関する計画や意図といった内的要因により生じる協調の形成とその崩壊に関する動的過程に着目する．具体的には，脳の情報処理の仕組みとして提案されている予測符号化を再帰型神経回路モデルにより具現化し，二台のロボットそれぞれに実装する．そして，実環境における二台のロボット間の相互作用学習実験を行う．他者（ロボット）との相互作用の結果生じる互いの意図の動的な収斂と発散により，協調の形成・崩壊に関する動的過程が観察可能であると期待される．

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Awards 【 Display / hide 】

  • JSAI Annual Conference Award

    2020.07, The Japanese Society for Artificial Intelligence (JSAI 2020)

    Type of Award： Award from Japanese society, conference, symposium, etc.

  • Best Paper Award

    Tatsuro Yamada, Shingo Murata, Hiroaki Arie, Tetsuya Ogata, 2016.09, The 25th International Conference on Artificial Neural Networks (ICANN 2016)

    Type of Award： Award from international society, conference, symposium, etc.

  • Best Paper Award for Young Researcher of IPSJ National Convention

    2014.03, Information Processing Society of Japan (IPSJ 2014)

    Type of Award： Award from Japanese society, conference, symposium, etc.

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Courses Taught 【 Display / hide 】

  • SEMINOR IN ELECTRONICS AND INFOTMATION ENGINEERING(2)

    2024

  • RECITATION IN ELECTRONICS AND INFORMATION ENGINEERING

    2024

  • LABORATORY IN SCIENCE

    2024

  • LABORATORIES IN SCIENCE AND TECHNOLOGY

    2024

  • INTRODUCTION TO MACHINE LEARNING

    2024

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Memberships in Academic Societies 【 Display / hide 】

  • IEEE, 

    2016
    -
    Present

  • The Japanese Society for Artificial Intelligence, 

    2014
    -
    Present

  • The Robotics Society of Japan, 

    2012
    -
    Present
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Committee Experiences 【 Display / hide 】

  • 2024
    -
    Present

    Section Editor, Special Section on "Cognitive Development and Symbol Emergence" in Advanced Robotics

  • 2021.11
    -
    Present

    Associate Editor, IEEE Transactions on Cognitive and Developmental Systems

  • 2021

    Associate Editor, IEEE/RSJ IROS 2021

  • 2021

    Associate Editor, IEEE ICDL 2021

  • 2020

    Associate Editor, IEEE/RSJ IROS 2020

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