Kanamaru Takashi

カナマルタカシ | Kanamaru Takashi

Affiliation and department：
Job title： Professor
Homepage URL (1)： http://brain.cc.kogakuin.ac.jp/~kanamaru/index-j.html

Research field (2)： Sensitivity (kansei) informatics , Soft computing

Research theme for competitive and other funds (6)：

2020 - 2025 Inducing lifelong plasticity (iPlasticity) by brain rejuvenation: elucidation and manipulation of critical period mechanisms
2020 - 2025 臨界期の回路網形成に関する注意下の擬アトラクター理論による基礎理論構築
2011 - 2013 基礎研究と応用研究の融合による複雑系数理モデル学の体系化(研究分担者)
2008 - 2011 Constructing theory of dynamical neural networks and its application to dynamic control
2005 - 2007 動的なニューラルネットワークによる情報統合モデルとそのハードウェア化

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Papers (31)：

Yudai Ebato, Sou Nobukawa, Yusuke Sakemi, Haruhiko Nishimura, Takashi Kanamaru, Nina Sviridova, Kazuyuki Aihara. Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks. Scientific Reports. 2024. 14. 8631
Takashi Kanamaru, Takao K. Hensch, Kazuyuki Aihara. Maximal Memory Capacity Near the Edge of Chaos in Balanced Cortical E-I Networks. Neural Computation. 2023. 35. 8. 1430-1462
Takashi Kanamaru, Kazuyuki Aihara. Acetylcholine-mediated top-down attention improves the response to bottom-up inputs by deformation of the attractor landscape. PLOS ONE. 2019. 14. 10. e0223592
Takashi Kanamaru. Quantifying strength of chaos in the population firing rate of neurons. Neural Computation. 2018. 30. 3. 792-819
Takashi Kanamaru. Chaotic Pattern Alternations Can Reproduce Properties of Dominance Durations in Multistable Perception. NEURAL COMPUTATION. 2017. 29. 6. 1696-1720

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Books (9)：

ラズパイ4対応カラー図解最新 Raspberry Piで学ぶ電子工作
講談社 2020 ISBN:9784065193396
高校数学からはじめるディープラーニング
講談社 2020 ISBN:9784065194034
カラー図解 Raspberry Piではじめる機械学習
講談社 2018 ISBN:9784065020524
カラー図解最新 Raspberry Piで学ぶ電子工作
講談社 2016 ISBN:9784062579773
実例で学ぶRaspberry Pi電子工作
講談社 2015 ISBN:9784062579506

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Lectures and oral presentations (83)：

Azure Kinectを用いた骨格推定に基づく姿勢評価方法の提案
(電子情報通信学会;画像工学研究会 2023)
Azure Kinectを用いた骨格角度に基づく姿勢評価方法の提案
(FIT2023 第22回情報科学技術フォーラム講演論文集 2023)
Optimal Excitatory and Inhibitory Balance for High Learning Performance in Spiking Neural Networks with Long-Tailed Synaptic Weight Distributions
(2023)
Innate trainingによるカオスニューロンモデルによって構成されたEcho State Networkの学習法
(電子情報通信学会非線形問題研究会 2022)
臨界期のE-I balance下ネットワークにおける情報処理
(「次世代脳」プロジェクト冬のシンポジウム2021 「グリアとニューロン:機能調節とその可塑性」 2021)

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Education (3)：

1998 - 2001 The University of Tokyo The Graduate School of Engineering Department of Advanced Interdisciplinary Studies
1996 - 1998 The University of Tokyo The Graduate School of Engineering
1994 - 1996 The University of Tokyo The Faculty of Engineering Department of Applied Physics

Professional career (1)：

Doctor of Engineering (The University of Tokyo)

Work history (8)：

2020/04 - 現在 The University of Tokyo International Research Center for Neurointelligence, Institutes for Advanced Study Affiliated Faculty
2019/04 - 現在 Kogakuin University School of Advanced Engineering, Department of Mechanical Science and Engineering
2022/04 - 2022/09 Osaka Metropolitan University
2015/04 - 2019/03 Kogakuin University School of Advanced Engineering, Department of Mechanical Science and Engineering
2010/04 - 2019/03 Kogakuin University Faculty of Global Engineering, Department of Innovative Mechanical Engineering

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Awards (1)：

2006 - 平成18年度日本神経回路学会奨励賞

Association Membership(s) (4)：

THE JAPAN SOCIETY OF MECHANICAL ENGINEERS , Japanese Neural Network Society , THE JAPAN NEUROSCIENCE SOCIETY , THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS

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