研究者業績

矢入 郁子

ヤイリ イクコ  (Yairi Ikuko)

基本情報

所属
上智大学 理工学部情報理工学科 教授
学位
博士(工学)(1999年3月 東京大学)

通称等の別名
矢入(江口)郁子
研究者番号
10358880
ORCID ID
 https://orcid.org/0000-0001-7522-0663
J-GLOBAL ID
200901082419968115
researchmap会員ID
6000011105

外部リンク

1994年東京大学工学部卒業,1996年同大学院工学系研究科修士課程修了,1999年同博士課程修了,博士 (工学). 同年、郵政省通信総合研究所 (現 :国立研究開発法人情報通信研究機構)研究官,2008年 り上智大学准教授.ユビキタス歩行者ITSのための時空間情報処理や高齢者・障害者向けインタフエース,Future lnternet,人間行動データ分析への深層学習応用,脳情報処理などの研究開発に従事.元人工知能学会理事,元ヒューマ ンインタフエース学会理事 .

 


論文

 134
  • Hiroshi Yamakawa, Ayako Fukawa, Ikuko Eguchi Yairi, Yutaka Matsuo
    Frontiers in Systems Neuroscience 18 2024年8月20日  査読有り
    Background Imagination represents a pivotal capability of human intelligence. To develop human-like artificial intelligence, uncovering the computational architecture pertinent to imaginative capabilities through reverse engineering the brain's computational functions is essential. The existing Structure-Constrained Interface Decomposition (SCID) method, leverages the anatomical structure of the brain to extract computational architecture. However, its efficacy is limited to narrow brain regions, making it unsuitable for realizing the function of imagination, which involves diverse brain areas such as the neocortex, basal ganglia, thalamus, and hippocampus. Objective In this study, we proposed the Function-Oriented SCID method, an advancement over the existing SCID method, comprising four steps designed for reverse engineering broader brain areas. This method was applied to the brain's imaginative capabilities to design a hypothetical computational architecture. The implementation began with defining the human imaginative ability that we aspire to simulate. Subsequently, six critical requirements necessary for actualizing the defined imagination were identified. Constraints were established considering the unique representational capacity and the singularity of the neocortex's modes, a distributed memory structure responsible for executing imaginative functions. In line with these constraints, we developed five distinct functions to fulfill the requirements. We allocated specific components for each function, followed by an architectural proposal aligning each component with a corresponding brain organ. Results In the proposed architecture, the distributed memory component, associated with the neocortex, realizes the representation and execution function; the imaginary zone maker component, associated with the claustrum, accomplishes the dynamic-zone partitioning function; the routing conductor component, linked with the complex of thalamus and basal ganglia, performs the manipulation function; the mode memory component, related to the specific agranular neocortical area executes the mode maintenance function; and the recorder component, affiliated with the hippocampal formation, handles the history management function. Thus, we have provided a fundamental cognitive architecture of the brain that comprehensively covers the brain's imaginative capacities.
  • Tianchen Zhou, Yutaka Yakuwa, Natsuki Okamura, Takayuki Kuroda, Ikuko Eguchi Yairi
    IEICE Communications Express 12(11) 575-578 2023年11月  査読有り最終著者責任著者
  • Nagisa Masuda, Ikuko Eguchi Yairi
    Frontiers in Psychology 14 2023年6月1日  査読有り最終著者責任著者
    Objective and accurate classification of fear levels is a socially important task that contributes to developing treatments for Anxiety Disorder, Obsessive–compulsive Disorder, Post-Traumatic Stress Disorder (PTSD), and Phobia. This study examines a deep learning model to automatically estimate human fear levels with high accuracy using multichannel EEG signals and multimodal peripheral physiological signals in the DEAP dataset. The Multi-Input CNN-LSTM classification model combining Convolutional Neural Network (CNN) and Long Sort-Term Memory (LSTM) estimated four fear levels with an accuracy of 98.79% and an F1 score of 99.01% in a 10-fold cross-validation. This study contributes to the following; (1) to present the possibility of recognizing fear emotion with high accuracy using a deep learning model from physiological signals without arbitrary feature extraction or feature selection, (2) to investigate effective deep learning model structures for high-accuracy fear recognition and to propose Multi-Input CNN-LSTM, and (3) to examine the model’s tolerance to individual differences in physiological signals and the possibility of improving accuracy through additional learning.
  • Natsuki Okamura, Yutaka Yakuwa, Takayuki Kuroda, Ikuko E. Yairi
    IEICE Communications Express 11(10) 667-672 2022年10月1日  査読有り最終著者責任著者
  • Eichi Takaya, Masaki Haraoka, Hiroki Takahashi, Ikuko Eguchi Yairi, Yasuyuki Kobayashi
    Journal of St. Marianna University 13(2) 95-100 2022年  査読有り

MISC

 121

講演・口頭発表等

 202

共同研究・競争的資金等の研究課題

 15

学術貢献活動

 1

社会貢献活動

 22