Details of a Researcher - Murata, Shingo

Murata, Shingo

写真a

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

Position: Associate Professor

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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

-

Present

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

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Research Keywords 【 Display / hide 】

Neural Network
Recurrent Neural Network
Deep Learning
Computational Psychiatry
Cognitive Robotics

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Papers 【 Display / hide 】

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.
- Access to Document (DOI)
Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks

Namiko Saito, Tetsuya Ogata, Hiroki Mori, Shingo Murata, Shigeki Sugano

Frontiers in Robotics and AI （Frontiers Media SA) 8 2021.09

Accepted

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We propose a tool-use model that enables a robot to act toward a provided goal. It is important to consider features of the four factors; tools, objects actions, and effects at the same time because they are related to each other and one factor can influence the others. The tool-use model is constructed with deep neural networks (DNNs) using multimodal sensorimotor data; image, force, and joint angle information. To allow the robot to learn tool-use, we collect training data by controlling the robot to perform various object operations using several tools with multiple actions that leads different effects. Then the tool-use model is thereby trained and learns sensorimotor coordination and acquires relationships among tools, objects, actions and effects in its latent space. We can give the robot a task goal by providing an image showing the target placement and orientation of the object. Using the goal image with the tool-use model, the robot detects the features of tools and objects, and determines how to act to reproduce the target effects automatically. Then the robot generates actions adjusting to the real time situations even though the tools and objects are unknown and more complicated than trained ones.
- Access to Document (DOI)
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

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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.
- Access to Document (DOI)
Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder

Hayato Idei, Shingo Murata, Yuichi Yamashita, Tetsuya Ogata

Neural Networks （Elsevier BV) 138 150 - 163 2020.06

Joint Work, Accepted, ISSN 08936080

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Neurodevelopmental disorders are characterized by heterogeneous and non-specific nature of their clinical symptoms. In particular, hyper- and hypo-reactivity to sensory stimuli are diagnostic features of autism spectrum disorder and are reported across many neurodevelopmental disorders. However, computational mechanisms underlying the unusual paradoxical behaviors remain unclear. In this study, using a robot controlled by a hierarchical recurrent neural network model with predictive processing and learning mechanism, we simulated how functional disconnection altered the learning process and subsequent behavioral reactivity to environmental change. The results show that, through the learning process, long-range functional disconnection between distinct network levels could simultaneously lower the precision of sensory information and higher-level prediction. The alteration caused a robot to exhibit sensory-dominated and sensory-ignoring behaviors ascribed to sensory hyper- and hypo-reactivity, respectively. As long-range functional disconnection became more severe, a frequency shift from hyporeactivity to hyperreactivity was observed, paralleling an early sign of autism spectrum disorder. Furthermore, local functional disconnection at the level of sensory processing similarly induced hyporeactivity due to low sensory precision. These findings suggest a computational explanation for paradoxical sensory behaviors in neurodevelopmental disorders, such as coexisting hyper- and hypo-reactivity to sensory stimulus. A neurorobotics approach may be useful for bridging various levels of understanding in neurodevelopmental disorders and providing insights into mechanisms underlying complex clinical symptoms.
- Access to Document (DOI)
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

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

Large-scale web-based experiments on the interaction between human and drawing AI agent

Murata, Shingo

福澤諭吉記念慶應義塾学事振興基金事業報告集（福澤基金運営委員会) 2022
Inheritance and modification of unfinished tasks by a collaborative robot via sub-goal generation using deep learning

Murata, Shingo

学事振興資金研究成果実績報告書（慶應義塾大学) 2021
Large-scale web-based experiments on the interaction between human and drawing AI agent

Murata, Shingo

福澤諭吉記念慶應義塾学事振興基金事業報告集（福澤基金運営委員会) 2021
Self-supervised learning method to realize skillful object manipulation by a robot

Murata, Shingo

学事振興資金研究成果実績報告書（慶應義塾大学) 2020

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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, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (A), Grant-in-Aid for Scientific Research (A), No Setting

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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

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2022.03

National Institute of Informatics, Grant-in-Aid for Early-Career Scientists, No Setting

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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

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2020.03

Waseda University, Grants-in-Aid for Scientific Research Grant-in-Aid for Young Scientists (B), MURATA Shingo, Grant-in-Aid for Young Scientists (B), No Setting

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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.

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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.