Murata, Shingo

写真a

Affiliation

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

Position

Assistant Professor/Senior Assistant Professor

E-mail Address

E-mail address

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

  • 2020.04
    -
    Present

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

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

Academic Degrees 【 Display / hide

  • Doctor of Engineering, Waseda University, Coursework, 2016.03

 

Research Areas 【 Display / hide

  • Cognitive Robotics

  • Robot Learning

  • Computational Psychiatry

 

Papers 【 Display / hide

  • Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder

    Hayato Idei, Shingo Murata, Yuichi Yamashita, Tetsuya Ogata

    Frontiers in Psychiatry (Frontiers Media SA)  11 ( 762 ) 1 - 15 2020.08

    Research paper (scientific journal), Joint Work, Accepted

     View Summary

    Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. In particular, developmental learning aspects of symptoms and interactions with the physical environment remain largely unexplored in computational modeling studies, although a leading computational theory has posited associations between psychiatric symptoms and an unusual estimation of information uncertainty (precision), which is an essential aspect of the real world and is estimated through learning processes. Here, we propose a mechanistic explanation that unifies the disparate observations via a hierarchical predictive coding and developmental learning framework, which is demonstrated in experiments using a neural network-controlled robot. The results show that, through the developmental learning process, homogeneous intrinsic neuronal excitability at the neural level induced via self-organization changes at the information processing level, such as hyper sensory precision and overfitting to sensory noise. These changes led to multifaceted alterations at the behavioral level, such as inflexibility, reduced generalization, and motor clumsiness. In addition, these behavioral alterations were accompanied by fluctuating neural activity and excessive development of synaptic connections. These findings might bridge various levels of understandings in autism spectrum and other neurodevelopmental disorders and provide insights into the disease processes underlying observed behaviors and brain activities in individual patients. This study shows the potential of neurorobotics frameworks for modeling how psychiatric disorders arise from dynamic interactions among the brain, body, and uncertain environments.

  • Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder

    Hayato Idei, Shingo Murata, Yuichi Yamashita, Tetsuya Ogata

    Neural Networks (Center for Open Science)  138   150 - 163 2020.06

    Joint Work, Accepted,  ISSN  08936080

     View Summary

    <p>Hyper- and hyporeactivity to sensory stimuli is a diagnostic feature of autism spectrum disorder and has been reported in many neurodevelopmental disorders. However, the computational mechanisms underlying such paradoxical responses remain unclear. Here, using a robot controlled by a hierarchical recurrent neural network model with predictive processing and a learning mechanism, we simulated how functional disconnection alters the learning process and affects subsequent behavioral reactivity to environmental change. The results show that, through the learning process, functional disconnection between distinct network levels simultaneously lowered the precision of sensory information and higher-level prediction. These changes caused the robot to exhibit sensory-dominated and sensory-ignoring behaviors ascribed to sensory hyperreactivity and hyporeactivity, respectively. Furthermore, local functional disconnection at the sensory processing level similarly induced hyporeactivity due to low sensory precision. These findings suggest a computational explanation for co-existing sensory hyper- and hyporeactivity and insights at various levels of understanding in neurodevelopmental disorders.</p>

  • Sensory Uncertainty and Autism Spectrum Disorder: A Neurorobotics Simulation of Symptoms

    Hayato Idei, Shingo Murata, Tetsuya Ogata, Yuichi Yamashita

    Seishin Igaku 62 ( 2 ) 219 - 229 2020.02

    Research paper (scientific journal), Joint Work, Accepted

  • Achieving Human–Robot Collaboration with Dynamic Goal Inference by Gradient Descent

    Shingo Murata, Wataru Masuda, Jiayi Chen, Hiroaki Arie, Tetsuya Ogata, Shigeki Sugano

    In Proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))  11954 LNCS   579 - 590 2019.12

    Research paper (international conference proceedings), Joint Work, Accepted,  ISSN  9783030367107

     View Summary

    Collaboration with a human partner is a challenging task expected of intelligent robots. To realize this, robots need the ability to share a particular goal with a human and dynamically infer whether the goal state is changed by the human. In this paper, we propose a neural network-based computational framework with a gradient-based optimization of the goal state that enables robots to achieve this ability. The proposed framework consists of convolutional variational autoencoders (ConvVAEs) and a recurrent neural network (RNN) with a long short-term memory (LSTM) architecture that learns to map a given goal image for collaboration to visuomotor predictions. More specifically, visual and goal feature states are first extracted by the encoder of the respective ConvVAEs. Visual feature and motor predictions are then generated by the LSTM based on their current state and are conditioned according to the extracted goal feature state. During collaboration after the learning process, the goal feature state is optimized by gradient descent to minimize errors between the predicted and actual visual feature states. This enables the robot to dynamically infer situational (goal) changes of the human partner from visual observations alone. The proposed framework is evaluated by conducting experiments on a human–robot collaboration task involving object assembly. Experimental results demonstrate that a robot equipped with the proposed framework can collaborate with a human partner through dynamic goal inference even when the situation is ambiguous.

  • Large-scale Data Collection for Goal-directed Drawing Task with Self-report Psychiatric Symptom Questionnaires via Crowdsourcing

    Shingo Murata, Hikaru Yanagida, Kentaro Katahira, Shinsuke Suzuki, Tetsuya Ogata, Yuichi Yamashita

    In Proceedings of the 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2019) (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics)  2019-October   3839 - 3845 2019.10

    Research paper (international conference proceedings), Joint Work, Accepted,  ISSN  9781728145693

     View Summary

    Drawing is a representative human cognitive ability and may mirror cognitive characteristics including those associated with psychiatric symptoms. Therefore, analysis of drawing data collected from various populations such as healthy people and psychiatric patients may be beneficial for better understanding human cognition. However, collecting such large-scale data about the relationship between drawing and cognitive/personality traits offline-in a laboratory-is a difficult issue. To overcome this issue, we devised a novel experimental paradigm involving a goal-directed drawing task conducted online-on the eb-with participants recruited via a crowdsourcing platform. With the assistance of 1155 participants with differing levels of psychiatric symptoms, we collected a total of 194, 040 trajectory data and answers to seven different self-report psychiatric symptom questionnaires comprising 181 items. We visualized the collected trajectory data and performed an exploratory factor analysis on the correlation matrix of the psychiatric symptom questionnaire items. Our results suggest that there were associations between psychiatric symptoms represented by specific psychiatric factors and atypical behavior observed while performing the goal-directed drawing task. This indicates the efficacy of a dimensional approach to large-scale online experiments with respect to clinical psychiatry.

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Presentations 【 Display / hide

  • 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

  • Altered Sense of Self Induced by Functional Disconnection in a Hierarchical Neural Network: A Neuro-Robotics Study

    Hayato Idei, Shingo Murata, Yuichi Yamashita, Tetsuya Ogata

    International Consortium on Hallucination Research and Related Symptoms Kyoto Satellite Meeting (ICHR 2018 KYOTO), 2018.10, Other

  • Altered Behavioral Flexibility and Generalization Induced by Reduced Heterogeneity of Intrinsic Neuronal Excitability: A Neurorobotics Study

    Hayato Idei, Shingo Murata, Yuichi Yamashita, Tetsuya Ogata

    WPA XVII World Congress of Psychiatry, 2017.10, Other

  • Representation Learning of Logical Words via Seq2seq Learning from Linguistic Instructions to Robot Actions

    Tatsuro Yamada, Shingo Murata, Hiroaki Arie, Tetsuya Ogata

    Workshop on Representation Learning for Human and Robot Cognition, The 5th International Conference on Human–Agent Interaction (HAI 2017), 2017.10, Other

  • Logically Complex Symbol Grounding for Interactive Robots by Seq2seq Learning with an LSTM-RNN

    Tatsuro Yamada, Shingo Murata, Hiroaki Arie, Tetsuya Ogata

    The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS 2016), 2016.12, Other

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

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

    2020.04
    -
    2025.03

    National Center of Neurology and Psychiatry, 山下 祐一, 片平 健太郎, 国里 愛彦, 村田 真悟, 杉原 玄一, 高村 真広, Grant-in-Aid for Scientific Research (A)

     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, 村田 真悟, Grant-in-Aid for Early-Career Scientists

     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.

  • Integrative understanding of various symptoms of psychiatric disorders by constructive approach using robots

    2017.04
    -
    2020.03

    Waseda University, MURATA Shingo, Grant-in-Aid for Young Scientists (B)

     View Summary

    This research project aims at integrative and system-level understanding of the brain and neural mechanisms that produce various symptoms of psychiatric disorders by a constructive approach using robots. We hypothesize that the prediction error minimization considering uncertainty estimation is the fundamental computational principle in the neural networks of the brain. We developed a hierarchical recurrent neural network model based on the principle and evaluated the hypothesis by implementing the model into a robot. Experimental results on robot behavior learning demonstrate that aberrant uncertainty estimation decreases or increases prediction error signals, resulting in abnormalities in perception and action. Furthermore, the results also suggest that the aberrant uncertainty estimation is derived from disconnection between hierarchies and homogenization of neural activities.

Awards 【 Display / hide

  • JSAI Annual Conference Award

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

    Type of Award: Awards of National Conference, Council and Symposium

  • Best Paper Award

    2016.09, The 25th International Conference on Artificial Neural Networks (ICANN 2016)

    Type of Award: Awards of International Conference, Council and Symposium

  • Best Paper Award for Young Researcher of IPSJ National Convention

    2014.03, Information Processing Society of Japan (IPSJ 2014)

    Type of Award: Awards of National Conference, Council and Symposium

 

Courses Taught 【 Display / hide

  • RECITATION IN ELECTRONICS AND INFORMATION ENGINEERING

    2021

  • LABORATORY IN SCIENCE

    2021

  • LABORATORIES IN SCIENCE AND TECHNOLOGY

    2021

  • INTRODUCTION TO MACHINE LEARNING

    2021

  • INDEPENDENT STUDY ON INTEGRATED DESIGN ENGINEERING

    2021

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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

Committee Experiences 【 Display / hide

  • 2020

    Associate Editor, IEEE/RSJ IROS 2020

  • 2019.06
    -
    Present

    Member , JSAI Editorial Committee

  • 2019

    Associate Editor, IEEE ICDL-EpiRob 2019

  • 2019

    Associate Editor, IEEE/RSJ IROS 2019

  • 2018

    Local Chair, IEEE ICDL-EpiRob 2018

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