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["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" } array(11) { ["title"]=> string(82) "An Information Theoretic Approach to Reveal the Formation of Shared Representation" ["year"]=> string(4) "2019" ["authors"]=> string(57) "Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M." ["publication"]=> string(39) "Frontiers in Computational Neuroscience" ["volume"]=> string(2) "14" ["issue"]=> string(1) "1" ["pages"]=> string(0) "" ["type"]=> string(5) "Paper" ["doi"]=> string(40) "https://doi.org/10.3389/fncom.2020.00001" ["publication_link"]=> string(66) "https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full" ["number"]=> string(2) "29" }

2024

ポジティブな精神状態と心理社会的資源との関係:文化的修飾因子に焦点を当てた系統的レビューのプロトコル
量子弾性によるグラフェンの形状
低コスト遠隔操作の改善:GELLOを力で補強する
発達認知計算神経科学のための符号化モデル:将来性、課題、可能性
因果認知のための分離された表象
ある脳から別の脳への表現伝達のための教師なし手法
サイバネティック・アバター:体内モニタリングからソーシャルインタラクションまで応用可能な遠隔操作技術
頭蓋内および皮質下脳体積のゲノム解析から、家系間のばらつきを考慮した多遺伝子スコアが得られる
ProcBench:多段階推論と手続き追従のためのベンチマーク
Optical Neuroimage Studio (OptiNiSt): 直感的、拡張可能、拡張可能な光神経画像データ解析フレームワーク
機械学習で脳から教育へ:神経画像データから識字能力と計算能力を予測する
数式の統語論
科学モデルとしての集団予測コーディング:生成的科学に向けた科学活動の形式化
曲線統計多様体における高次相互作用を介した爆発的ニューラルネットワーク
神経データにおけるスケーリング則:175時間の脳波データを用いた非侵襲的音声デコーディング
知覚経験に対する現象学的態度の神経相関
機能的MRI信号から受動的触覚形状を解読する
因果的認知のための分離された表現
意識の普遍的理論に向けて
リアルなマルチモーダル環境に具現化されたグローバルワークスペースエージェントの設計と評価
背側葉セロトニンニューロンを光遺伝学的に活性化すると脳全体が活性化する
脳内双方向ネットワークコアと意識的知覚・認知との関連性
バイオレキシブ
深層模倣学習の実際的考察
知覚者と行為者のための意味的3D画像補強によるゼロショット物体操作
セグメンテーション条件付きパーシーバ・アクタによるきめ細かな物体検出と操作
ヒトにおける脳の構造と機能の世代間伝達:デザイン、方法、所見の叙述的レビュー
脳波に基づく音声解読における神経の寄与の解明
人間とロボットのインタフェースのための拡張可能なマルチロボット・マルチゴール・マニピュレーション・ベンチマーク
ロボットの多段階タスク制御のための視覚的および聴覚的EEGインターフェースの比較
学習し続けるための記憶変換器
オープンループVLMロボットプランニング:微調整と迅速なエンジニアリング戦略の検討
二次元ディラック物質からの創発弾性表面
多様なタスクの皮質表現幾何学から、被験者固有および被験者不変の認知構造が明らかになる
BRAIN2MUSIC:人間の脳活動から音楽を再構築する
機械学習で脳から教育へ:神経画像データから識字能力と計算能力を予測する
プロセスの表現としてのメタ表現
脳と神経の刺激技術の現在と未来
分散型科学が拓く新たなエコシステム:DeSci.Tokyoが果たす役割
短歌読解におけるヒト脳の情報処理メカニズムの解明
認知ダイナミクスの推定:認知過程の時間的ダイナミクスを抽出するための全脳空間回帰パラダイム
抽象画像のメタファー生成における形状特徴の役割について
ディープラーニングを用いた指先の血流脈波から健康状態を認識する新しい感情認識アプローチ
時空間刺激のためのfMRI適合空気圧触覚アレイの設計
時間知覚の主観性が、前頭葉-線条体間の価値シグナル動態を通じて、将来の報酬に対する選択選好を変化させる
将来の報酬を待つという継続的な決断は、海馬前頭葉の予期的ダイナミクスによって導かれる
海馬体積は幼少期の親からの拒絶と健常成人の社会的認知との関係を媒介する
ヒトの脳と人工ニューラルネットワークの階層的対応性の評価
意識の普遍的理論に向けて
連想変換器はスパース表現学習器である
研究開発における新たな科学運営モデル
数式の統語論
電磁気学と結合したベリーに触発された動的ゲージ場からの創発時空
美学の客観性に曇り?
臨床応用に向けたコミュニケーションおよび運動制御における非侵襲的ブレイン・コンピュータ・インターフェースの性能向上に関するレビュー
人工知能における意識:意識の科学からの洞察
ディープラーニングによる皮質領域の時間的特徴の検出
自律的、入札、信頼性、分散化、倫理的、資金提供(ABCDEF)出版 [バージョン1; 査読: 査読待ち]。
ディープカナルズ精神病理学のカナル理論を洗練させるためのディープラーニングアプローチ
幅広い年齢層のマーモセットを対象としたマルチモーダル脳磁気共鳴画像データベース
Legendre変換の変形が引き起こす幾何学的構造
ヒューマンファクターの神経科学的解析から提案するヒューマンマシンインタフェース
指数関数的なコルモゴロフ・ナグモの平均の熱力学
グラフェンにおける時間依存性幾何学から曲率誘起された擬ゲージ場
統合情報理論(IIT)4.現象的存在の特性を物理的な言葉で定式化する
システム統合情報
線形確率系における脳内状態遷移の最適制御コスト
ハイブリッドライフ:生物・人工・認知システムの融合
ディープラーニングを用いた指先の血流脈波から健康状態を認識する新しい感情認識アプローチ
混合イニシアチブの品質多様性アルゴリズムのためのプリファレンス学習型エミッター
Logical Tasks for Measuring Extrapolation and Rule Comprehension
時空間刺激のためのfMRI適合空気圧触覚アレイの設計
社会的信頼の神経解剖学的特性はうつ病の脆弱性を予測する
天皇は裸だ。対象記事へのコメントへの返信
Web3技術による科学の再構築
グラフェンにおける時間依存性幾何学から得られる曲率誘起擬似ゲージ場
背側皮質セロトニンニューロンの光遺伝学的活性化は、報酬ネットワークにおいて脳全体の反応を誘発する
ベイズ推論としての力学系の解釈
ヒトの一次報酬の採食は、海馬前部の予期動態によって導かれる
情報理論的アプローチによる共有表象の形成の解き明かし
質問に対する答えに興味を持つこと:学習された注意による新規性探索
エントロピー緩和輸送問題によるWasserstein距離とKullback-Leiblerダイバージェンスを結ぶ情報幾何学
恒常性制御を伴う好奇心駆動型強化学習
情報損失を最小限に抑えながら、パーティションを高速かつ正確に検索することが可能
ヒト前頭頂皮質における時間表現
ホメオヘテロバリュー勾配による退屈駆動型好奇心学習
分類器生成のための学習
好奇心とエンパワーメントに基づく強化学習導入のための統一的な戦略
脳内ネットワークの時間的変動が双安定知覚の個人差を予測する
フリストンの能動的推論の幾何学的特徴
ヒト補足運動野の時空間マップ
意識の機能的基盤としての情報生成
38,851人における皮質下脳構造の遺伝的アーキテクチャー
移動ロボットのカメラを用いたDeep Reinforcement Learningによる人物行動認識
ヒト大脳皮質の遺伝的構造に関する研究
現象学的短期研修プログラムの開発。方法論的教訓の報告。
意識に関する情報閉鎖理論
複雑系における情報的コアの効率的な探索。脳内ネットワークへの応用
連続アトラクタダイナミクスにおける情報の統合と次元性
Neuroprofile: 解剖学的脳スキャンからパーソナライズされた神経予測を行うウェブベースのサービス
右下前頭葉の予測形状信頼度
デコーディングの観点から統合情報を測定する
時間的知覚と予測における周波数同調
ニューラル・コース・グレーニング。ニューラルネットワークによる緩やかに変化する潜在的自由度の抽出
並べ換え等変量ニューラルネットワークの力学予測への応用
均一性錯視。中心的な刺激が周辺的な知覚を決定することがある
ベイジアンネットワークにおけるパターンの特異的かつ完全な局所的統合
時空間パターンに対する作用と知覚
精神神経疾患マウスモデルにおける拡散機能MRIによる局所的な活動によって引き起こされるグローバルな脳内ネットワーク機能異常の発見
連続的な動作を伴う制御のための内発的動機付け強化学習
ヒト脳皮質脳波における統合された情報パターンとしての意識的知覚
行動空間をシンプルにするボディ・アフォーダンスの学習
生成モデルによる無償の反実仮想制御
k-meansのクラスタ数を決定するための記述長アプローチ
統合情報理論における最小情報区画の探索のための効率的なアルゴリズム
大域的に結合したカオス系における情報統合
能動的推論を拡張する。知覚-行動ループにおけるより多くの内発的動機づけ
逆向き強化学習による相対的な返品制度の学習
Generative Adversarial Networkによる画像間転送を用いた脳活動分類器の反実仮想的説明
皇帝の新しいマルコフ毛布
安静時脳活動の共活性化パターン解析と非定常性について
ヒト眼窩前頭皮質における感覚的特質と価値表現の共存
社会的相互作用とウェルビーイングを促進するためのAIエージェント
Legendreへの頌歌:曲がった統計多様体における幾何学的・熱力学的意味合い
ミニマルクライテリオン・アーティスト・コレクティブ
ハイブリッドL系制約条件付き進化型宇宙船 最適化 進化論的アルゴリズム
知覚の不確実性が、反応抑制時にトップダウンとボトムアップの前頭葉-側頭葉ネットワークのシグナル伝達を交互に変化させる
必要なのは教師あり学習。模倣学習から逆向きのRLを用いたメタRLまで
意識の機能:概念の明確化
シュレーディンガー橋による脳の状態遷移コストの定量化
人間と機械における意識機能と一般知能の関連について
手続き的コンテンツ生成のための代理非実現適合性獲得FI-2Pop
TOBは海馬を介した急性ストレス応答におけるエフェクターである
ネガティブを一般と具体に絞り込む
パーシーバーアーキテクチャーは機能的なグローバルワークスペースである
予測符号化に基づく変分リカレントニューラルネットワークによる少量学習サンプルからの目標指向型プランニング
心理現象の主観的特徴を探索する新しい実験現象学的手法:両眼ライバル視への応用
ベイズ型ハイパーパラメータの非自明な情報的閉鎖性
マルコフ連鎖におけるベイズ型ハイパーパラメータのダイナミックス
コーザル・ブランケット理論とアルゴリズムの枠組み
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バイオレキシブ
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BETHデータセット。異常検知研究のための実際のサイバーセキュリティデータ
局所活動の蒸留により、視覚処理における神経情報の隠れた内容が明らかになる
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脳と機械におけるメタ学習、社会的認知、意識
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2022

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曲がった統計多様体に対する最大エントロピー原理の一般化