ニューロインテリジェンス国際研究機構(IRCN)連携教員
認知発達ロボティクス研究室 長井 志江
Predictive and Creative Brain Lab Zenas C. Chao
認知発達ロボティクス研究室 スタッフ
| 特任教授 | 長井 志江 |
|---|
研究概要
ヒトはどのように知能を獲得するのでしょうか。この究極の問いに迫る方法の一つに、ヒトのように学習・発達するロボットを創るという手法があります。私たちは、ヒトの脳や身体を模した神経回路モデルやロボットを設計し、それが環境や他者との相互作用を通して知能を獲得する過程を解析することで、知能の発生原理の解明に取り組んでいます。知能の原理として私たちが注目しているのは、脳の予測情報処理です。脳は環境や身体からのボトムアップな感覚信号と、経験や知識に基づく内部モデルからのトップダウンな予測信号を統合し、両信号の誤差（予測誤差）を最小化するように環境認識や行動生成を行います。私たちは、予測情報処理に基づく神経回路モデルを設計することで、感覚運動能力から社会的能力に至る発達の時間的連続性や、発達障害などの個人の多様性が、予測情報処理の獲得と変調に基づいて統一的に説明できるのかを検証しています。また、そこで得られた知見を応用し、発達障害者の支援技術を開発しています。特に、発達障害を抱えた当事者研究者と協働することで、社会的活動における困りごとが、どのような脳・身体機能と環境要因の相互作用として生じるのかを明らかにし、多様性の発生機序の理解に基づいた支援を実現します。
Publication list
- Philippsen A, Tsuji S, and Nagai Y: Simulating Developmental and Individual Differences of Drawing Behavior in Children Using a Predictive Coding Model. Frontiers in Neurorobotics, 16:856184, 2022.
- Seker M Y, Ahmetoglu A, Nagai Y, Asada M, Oztop E, and Ugur E: Imitation and mirror systems in robots through Deep Modality Blending Networks. Neural Networks, 146:22-35, 2022.
- Nagai Y: Predictive learning: its key role in early cognitive development. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1771):20180030, 2019.
- Horii T, Nagai Y, and Asada M: Modeling Development of Multimodal Emotion Perception Guided by Tactile Dominance and Perceptual Improvement. IEEE Transactions on Cognitive and Developmental Systems, 10(3):762-775, 2018.
- Baraglia J, Nagai Y, and Asada M: Emergence of Altruistic Behavior Through the Minimization of Prediction Error. IEEE Transactions on Cognitive and Developmental Systems, 8(3):141-151, 2016.
連絡先
office@developmental-robotics.jp
Predictive and Creative Brain Lab スタッフ
| 准教授 | Zenas C. Chao |
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研究概要
Large-Scale Neuronal Networks in the Predictive and Creative Brain
Creative Brain
Predictive coding theory proposes that the brain actively generates predictions about incoming sensory information to create an understanding of the external world. This is achieved by a hierarchical and bidirectional cascade of large-scale cortical signaling in order to minimize overall prediction errors. This theoretical framework provides a unified model for both perception and action and holds promise for shedding light on psychiatric disorders characterized by disturbances in prediction or error signaling, such as autism, schizophrenia, and ADHD. My current research interests are twofold: (1) validating the theory by pinpointing brain signals that underlie predictions and prediction errors at different hierarchical levels, and (2) extending the theory's applicability beyond perception and action, encompassing domains such as internal thought processes and creativity. Enhancing our understanding of how predictive coding operates across various hierarchies and sensory modalities, as well as how information from diverse spatial and temporal scales merges into a comprehensive thought, holds the potential to catalyze progress in neuromorphic engineering and propel the quest for neural markers crucial in forecasting and diagnosing brain disorders.
研究項目
- Investigation of Predictive Coding Circuits: Our objective is to elucidate the intricate networks of microcircuits and macrocircuits integral to predictive coding across various hierarchies and sensory domains. This work extends across species — from rodents and marmosets to humans — and integrates experimental findings from these diverse subjects with theoretical modeling to forge comprehensive insights.
- Neural Markers for Prediction-Related Psychiatric Disorders: A pivotal aspect of our work involves pinpointing specific neural markers that correlate with psychiatric disorders linked to prediction anomalies, such as autism spectrum disorder and schizophrenia. Identifying these markers is crucial for understanding the neural foundations of these conditions and could lead to more targeted interventions.
- Understanding Predictive Coding in Creativity: Our research also explores the role of predictive coding in the realm of creative potential, specifically its capacity to foster the generation of innovative ideas for future tasks. Through this exploration, we aim to illuminate the cognitive mechanisms that underpin creativity, offering new perspectives on this complex process.
- Development of a Creativity Augmentation System for Real-World Applications: One ambition of our work is to create a closed-loop system designed to augment creative potential in practical, real-world settings. Such a system promises to revolutionize fields that depend heavily on creative problem-solving, by offering new tools and methodologies to enhance innovation.
Publication list(within 5 papers)
- Kern, F. B., & Chao, Z. C. (2023). Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks. PLOS Computational Biology, 19(10), e1011554.
- Chao, Z. C., Huang, Y. T., & Wu, C. T. (2022). A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain. Communications Biology, 5(1), 1076.
- Chao, Z. C., Dillon, D. G., Liu, Y. H., Barrick, E. M., & Wu, C. T. (2022). Altered coordination between frontal delta and parietal alpha networks underlies anhedonia and depressive rumination in major depressive disorder. Journal of Psychiatry and Neuroscience, 47(6), E367-E378.
- Chao, Z. C., Takaura, K., Wang, L., Fujii, N., & Dehaene, S. (2018). Large-scale cortical networks for hierarchical prediction and prediction error in the primate brain. Neuron, 100(5), 1252-1266.
- Chao, Z. C., Nagasaka, Y., & Fujii, N. (2015). Cortical network architecture for context processing in primate brain. Elife, 4, e06121.
連絡先
zenas.c.chao@ircn.jp