Researchers

SHINOZAKI Takashi

SHINOZAKI Takashi
Associate Professor
Faculty Department of Informatics / Graduate School of Science and Engineering / Cyber Informatics Research Institute
Researchmap https://researchmap.jp/tshino

Education and Career

Education

  • 1996/04 - 2000/03 , Tokyo University of Science, Faculty of Science,
  • 2000/04 - 2006/03 , University of Tokyo, Graduate School of Frontier Sciences,

Academic & Professional Experience

  • Apr. 2022 - Today , Kindai University Faculty of Infomatics Associate Professor
  • Dec. 2010 - Mar. 2022 , National Institute of Information and Communications Technology Center for Information and Neural Networks Researcher
  • Aug. 2010 - Nov. 2010 , RIKEN BSI Temporal Researcher
  • Apr. 2009 - Jul. 2010 , Center for Neural Science, New York University Postdoctral Fellow
  • Apr. 2006 - Mar. 2009 , RIKEN BSI Special Postdoctoral Research Fellow

Research Activities

Research Areas

  • Informatics, Intelligent informatics
  • Humanities & social sciences, Cognitive sciences
  • Life sciences, Neuroscience - general

Research Interests

Neural Network, Artificial Intelligence, Deep Learning, Computational Neuroscience, Vision

Published Papers

  1. Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression
    Chanseok Lim; Mikio Inagaki; Takashi Shinozaki; Ichiro Fujita
    Scientific Reports  13  (1)  5, Jul. 2023  , Refereed
  2. Convolutional neural networks reveal differences in action units of facial expressions between face image databases developed in different countries
    Mikio Inagaki; Tatsuro Ito; Takashi Shinozaki; Ichiro Fujita
    Frontiers in Psychology  13  3, Nov. 2022  , Refereed
  3. Biologically motivated learning method for deep neural networks using hierarchical competitive learning
    Takashi Shinozaki
    Neural Networks  144  , 271-278, Dec. 2021  , Refereed

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Conference Activities & Talks

  1. 農作物の画像を対象としたディープラーニング入門 , 篠崎 隆志 , 農林水産省 次世代施設園芸地域展開促進事業 植物工場人材育成プログラム , 30, Nov. 2020
  2. 脳のしくみと人工知能 , 篠崎 隆志 , 和歌山大学 世界の情報通信研究を知る , 11, Nov. 2020
  3. 脳に学ぶ次世代 AI 技術 , 篠崎隆志 , 大阪国際サイエンスクラブ 金曜サイエンスサロン , 7, Feb. 2020

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MISC

  1. 国内大規模画像コホートを用いた人工知能によるGrade II‐III神経膠腫の画像分子診断 , 木下学; 木下学; 福間良平; 柳澤琢史; 柳澤琢史; 篠崎隆志; 貴島晴彦; 高橋雅道; 成田善孝; 有田英之; 有田英之; 藤本康倫; 藤本康倫; 寺川雄三; 露口尚弘; 深井順也; 沖田典子; 高垣匡寿; 石橋謙一; 児玉良典; 埜中正博; 森内秀祐; 泉本修一; 中島義和; 森鑑二; 正札智子; 正札智子; 市村幸一; 金村米博; 金村米博 , 日本脳腫瘍学会プログラム・抄録集 , 35th , 83 , 2017
  2. Feedforward supervised learning for deep neural networks with local competitiveness information , 篠崎 隆志 , 電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 , 116 , 120 , 229 , 234 , 4, Jul. 2016
  3. Portable BMI System using Phase Template Matching Analysis of Steady State Visual Evoked Potential , SHINOZAKI Takashi; YOKOTA Yusuke; NARUSE Yasushi , IEICE technical report. Neurocomputing , 114 , 515 , 211 , 216 , 16, Mar. 2015
    Summary:This study proposes a novel analysis method for steady-state visual-evoked responses (SSVEP), named phase template matching analysis, and develops portable BMI systems using the method. The method focused the phase information of SSVEP which is more robust than the amplitude information, and accompanied with 'phase tagging' method. The developed BMI system is powered by a portable battery, and enables a BMI control of a small humanoid robot with a portable system size. Moreover, the system is extended to a immersive BMI system using head mounted display (HMD) for the presentation of visual stimulus, resulting good accuracy in a discrimination task under daily life environment.

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Patents

  1. 脳波計測用ヘッドギア , 成瀬康, 横田悠右, 篠崎隆志, 宮本章尋, 佐野太一, 大西智樹, 田中真悟 , 特許6435587
  2. 階層型ニューラルネットワークの学習システム及び方法 , 篠崎隆志 , 特許6327926
  3. 脳波計測用電極、脳波計測用電極を備える脳波計測用電極付キャップ , 成瀬康, 篠崎隆志, 梅原広明 , 特許6112534

Awards & Honors

  1. Jul. 2022, Japanese Neural Network Society, Best paper award
  2. Jul. 2022, Japanese Neural Network Society, Excellent research award
  3. Sep. 2019, 日本神経回路学会, 最優秀研究賞

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Research Grants & Projects

  1. Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Research (Exploratory), Study on the mechanisms of #TheDress phenomenon by using DNN , Tohoku University
  2. Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), 脳における情報の高効率なゲーティング機構の解明 , National Institute of Information and Communications Technology
  3. Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Exploratory Research, Measurement of spatial distribution of visual attention by using SSVEP evoked by dichoptic stimulation , Tohoku University

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