{"id":921,"date":"2026-02-09T01:30:17","date_gmt":"2026-02-08T16:30:17","guid":{"rendered":"https:\/\/itexplore.org\/jp\/columns\/ai-research-trends-neural-circuits-medical-diagnosis-cognitive-modeling\/"},"modified":"2026-02-09T01:30:17","modified_gmt":"2026-02-08T16:30:17","slug":"ai-research-trends-neural-circuits-medical-diagnosis-cognitive-modeling","status":"publish","type":"post","link":"https:\/\/itexplore.org\/jp\/columns\/ai-research-trends-neural-circuits-medical-diagnosis-cognitive-modeling\/","title":{"rendered":"\u6700\u65b0AI\u7814\u7a76\u52d5\u5411\uff1a\u795e\u7d4c\u56de\u8def\u3001\u533b\u7642\u8a3a\u65ad\u3001\u8a8d\u77e5\u30e2\u30c7\u30ea\u30f3\u30b0"},"content":{"rendered":"<p>\u672c\u65e5\u306e\u6ce8\u76eeAI\u30fb\u30c6\u30c3\u30af\u30cb\u30e5\u30fc\u30b9\u3092\u3001\u5c02\u9580\u7684\u306a\u5206\u6790\u3068\u5171\u306b\u304a\u5c4a\u3051\u3057\u307e\u3059\u3002<\/p>\n<div class=\"wp-block-vk-blocks-alert vk_alert alert alert-warning has-alert-icon\">\n<div class=\"vk_alert_icon\">\n<div class=\"vk_alert_icon_icon\"><i class=\"fa-solid fa-triangle-exclamation\" aria-hidden=\"true\"><\/i><\/div>\n<div class=\"vk_alert_icon_text\"><span>Warning<\/span><\/div>\n<\/div>\n<div class=\"vk_alert_content\">\n<p>\u3053\u306e\u8a18\u4e8b\u306fAI\u306b\u3088\u3063\u3066\u81ea\u52d5\u751f\u6210\u30fb\u5206\u6790\u3055\u308c\u305f\u3082\u306e\u3067\u3059\u3002AI\u306e\u6027\u8cea\u4e0a\u3001\u4e8b\u5b9f\u8aa4\u8a8d\u304c\u542b\u307e\u308c\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u305f\u3081\u3001\u91cd\u8981\u306a\u5224\u65ad\u3092\u4e0b\u3059\u969b\u306f\u5fc5\u305a\u30ea\u30f3\u30af\u5148\u306e\u4e00\u6b21\u30bd\u30fc\u30b9\u3092\u3054\u78ba\u8a8d\u304f\u3060\u3055\u3044\u3002<\/p>\n<\/div>\n<\/div>\n<div class=\"wp-block-group\" style=\"margin-top:40px;margin-bottom:40px\">\n<h2 class=\"wp-block-heading\">\u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u304a\u3051\u308b\u975e\u7dda\u5f62\u30ce\u30a4\u30ba\u306e\u305f\u3081\u306e\u52d5\u7684\u5e73\u5747\u5834\u7406\u8ad6<\/h2>\n<ul>\n<li><strong>\u539f\u984c:<\/strong> Dynamic Mean Field Theories for Nonlinear Noise in Recurrent Neuronal Networks<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\">\u5c02\u9580\u30a2\u30ca\u30ea\u30b9\u30c8\u306e\u5206\u6790<\/h3>\n<div class=\"ai-summary-content\">\n<p>\u672c\u7814\u7a76\u3067\u306f\u3001\u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u304a\u3051\u308b\u975e\u7dda\u5f62\u30ce\u30a4\u30ba\u306e\u6271\u3044\u306b\u7126\u70b9\u3092\u5f53\u3066\u3066\u3044\u307e\u3059\u3002<strong>\u975e\u7dda\u5f62\u95a2\u6570<\/strong>\u3092\u901a\u904e\u3059\u308b\u76f8\u95a2\u30ce\u30a4\u30ba\u306f\u3001\u904e\u6e21\u73fe\u8c61\u3084\u5206\u5c90\u306a\u3069\u306e\u8907\u96d1\u306a\u73fe\u8c61\u306e\u89e3\u6790\u3092\u56f0\u96e3\u306b\u3057\u307e\u3059\u3002<\/p>\n<p>\u63d0\u6848\u624b\u6cd5\u306f\u3001<strong>\u975e\u7dda\u5f62\u95a2\u6570<\/strong>\u306eOrnstein-Uhlenbeck (OU) \u30ce\u30a4\u30ba\u3092\u3001\u5e73\u5747\u3068\u5171\u5206\u6563\u3092\u5408\u308f\u305b\u305f\u30ac\u30a6\u30b9\u7b49\u4fa1\u30d7\u30ed\u30bb\u30b9\u306b\u7f6e\u304d\u63db\u3048\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u5e83\u7bc4\u306a\u975e\u7dda\u5f62\u6027\u306b\u5bfe\u3057\u3066\u306f\u5bfe\u6570\u6b63\u898f\u30e2\u30fc\u30e1\u30f3\u30c8\u9589\u9396\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001<strong>\u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af<\/strong>\u306e\u9589\u3058\u305f\u52d5\u7684\u5e73\u5747\u5834\u7406\u8ad6\u3092\u5c0e\u51fa\u3057\u307e\u3059\u3002<\/p>\n<p>\u3053\u306e\u7406\u8ad6\u306f\u3001\u30aa\u30fc\u30c0\u30fc1\u306e\u904e\u6e21\u73fe\u8c61\u3001\u4e0d\u52d5\u70b9\u3001\u304a\u3088\u3073\u30ce\u30a4\u30ba\u8a98\u767a\u578b\u306e\u5206\u5c90\u69cb\u9020\u30b7\u30d5\u30c8\u3092\u6349\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u3001\u7279\u306b\u5f37\u3044\u5909\u52d5\u30ec\u30b8\u30fc\u30e0\u306b\u304a\u3044\u3066\u3001\u7dda\u5f62\u5316\u30d9\u30fc\u30b9\u306e\u8fd1\u4f3c\u3092\u4e0a\u56de\u308b\u6027\u80fd\u3092\u793a\u3057\u307e\u3059\u3002\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306f\u3001\u8a08\u7b97\u795e\u7d4c\u79d1\u5b66\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u30ce\u30a4\u30ba\u4f9d\u5b58\u6027\u306e\u76f8\u56f3\u3092\u89e3\u6790\u53ef\u80fd\u306b\u3059\u308b\u305f\u3081\u306e\u3001\u3088\u308a\u5e83\u7bc4\u306a\u5fdc\u7528\u53ef\u80fd\u6027\u3092\u79d8\u3081\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\ud83d\udc49 <strong><a href=\"https:\/\/arxiv.org\/abs\/2601.15462\" target=\"_blank\" rel=\"noopener\">arXiv \u3067\u8a18\u4e8b\u5168\u6587\u3092\u8aad\u3080<\/a><\/strong><\/p>\n<\/div>\n<ul>\n<li><strong>\u8981\u70b9:<\/strong> A novel dynamical mean-field theory is developed for recurrent neuronal networks with nonlinear noise, improving the analysis of complex dynamics and noise-induced phenomena.<\/li>\n<li><strong>\u8457\u8005:<\/strong> Shoshana Chipman, Brent Doiron<\/li>\n<\/ul>\n<blockquote class=\"wp-block-quote\"><p><span>English Summary:<\/span><\/p>\n<p>This research focuses on handling nonlinear noise in <strong>recurrent neuronal networks<\/strong>. Correlated noise passing through nonlinear functions complicates the analysis of complex phenomena such as transients and bifurcations.<\/p>\n<p>The proposed method replaces nonlinear functions of Ornstein-Uhlenbeck (OU) noise with a Gaussian-equivalent process matched in mean and covariance. For expansive nonlinearities, it combines this with a lognormal moment closure to derive a closed <strong>dynamical mean-field theory<\/strong> for recurrent neuronal networks.<\/p>\n<p>The resulting theory captures order-one transients, fixed points, and noise-induced shifts of bifurcation structure, outperforming standard linearization-based approximations in the strong-fluctuation regime. This approach offers a tractable route to noise-dependent phase diagrams in computational neuroscience models.<\/p>\n<\/blockquote>\n<\/div>\n<div class=\"wp-block-group\" style=\"margin-top:40px;margin-bottom:40px\">\n<h2 class=\"wp-block-heading\">MRI\u304a\u3088\u3073\u81e8\u5e8a\u7684\u7279\u5fb4\u304b\u3089\u306e\u6a5f\u68b0\u5b66\u7fd2\u5f37\u5316\u578b\u975e\u8a18\u61b6\u969c\u5bb3\u578b\u30a2\u30eb\u30c4\u30cf\u30a4\u30de\u30fc\u75c5\u8a3a\u65ad<\/h2>\n<ul>\n<li><strong>\u539f\u984c:<\/strong> Machine learning-enhanced non-amnestic Alzheimer's disease diagnosis from MRI and clinical features<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\">\u5c02\u9580\u30a2\u30ca\u30ea\u30b9\u30c8\u306e\u5206\u6790<\/h3>\n<div class=\"ai-summary-content\">\n<p>\u672c\u7814\u7a76\u306f\u3001\u975e\u8a18\u61b6\u969c\u5bb3\u578b\u30a2\u30eb\u30c4\u30cf\u30a4\u30de\u30fc\u75c5\uff08atAD\uff09\u306e\u8a3a\u65ad\u7cbe\u5ea6\u5411\u4e0a\u3092\u76ee\u6307\u3057\u3001<strong>\u6a5f\u68b0\u5b66\u7fd2<\/strong>\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002\u5f93\u6765\u306e\u8a3a\u65ad\u6cd5\u306f\u8a18\u61b6\u969c\u5bb3\u578bAD\uff08tAD\uff09\u306b\u306f\u6709\u52b9\u3067\u3059\u304c\u3001atAD\u306e\u60a3\u8005\u7fa4\u306f\u8aa4\u8a3a\u3055\u308c\u3084\u3059\u3044\u3068\u3044\u3046\u8ab2\u984c\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u63d0\u6848\u624b\u6cd5\u3067\u306f\u3001\u81e8\u5e8a\u691c\u67fb\u30d0\u30c3\u30c6\u30ea\u30fc\u3068<strong>MRI\u30c7\u30fc\u30bf<\/strong>\uff08\u6d77\u99ac\u4f53\u7a4d\u3092\u542b\u3080\u5e83\u7bc4\u306a\u8133\u9818\u57df\u306e\u7279\u5fb4\u91cf\uff09\u3092\u7d44\u307f\u5408\u308f\u305b\u3066\u3001atAD\u3068\u975eAD\u8a8d\u77e5\u969c\u5bb3\u3092\u5206\u985e\u3057\u307e\u3059\u3002<strong>\u6a5f\u68b0\u5b66\u7fd2<\/strong>\u30e2\u30c7\u30eb\u306f\u3001\u6d77\u99ac\u4f53\u7a4d\u306e\u307f\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u3068\u6bd4\u8f03\u3057\u3066\u3001atAD\u75c7\u4f8b\u306e\u691c\u51fa\u7387\uff08\u30ea\u30b3\u30fc\u30eb\u7387\uff09\u3092\u5927\u5e45\u306b\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002<\/p>\n<p>\u5177\u4f53\u7684\u306b\u306f\u3001NACC\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306752%\u304b\u308969%\u3001ADNI\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306734%\u304b\u308977%\u3078\u3068\u6539\u5584\u304c\u898b\u3089\u308c\u307e\u3057\u305f\u3002\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306f\u3001\u81e8\u5e8a\u73fe\u5834\u3067\u6a19\u6e96\u7684\u306a\u691c\u67fb\u3068MRI\u306e\u307f\u3092\u7528\u3044\u3066\u3001\u975e\u8a18\u61b6\u969c\u5bb3\u578batAD\u306e\u8a3a\u65ad\u7cbe\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u308b\u91cd\u8981\u306a\u53ef\u80fd\u6027\u3092\u793a\u5506\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\ud83d\udc49 <strong><a href=\"https:\/\/arxiv.org\/abs\/2601.15530\" target=\"_blank\" rel=\"noopener\">arXiv \u3067\u8a18\u4e8b\u5168\u6587\u3092\u8aad\u3080<\/a><\/strong><\/p>\n<\/div>\n<ul>\n<li><strong>\u8981\u70b9:<\/strong> A machine learning approach using MRI and clinical data significantly improves the diagnosis of non-amnestic Alzheimer's disease, outperforming traditional methods.<\/li>\n<li><strong>\u8457\u8005:<\/strong> Megan A. Witherow, Michael L. Evans, Ahmed Temtam, Hamid Okhravi, Khan M. Iftekharuddin<\/li>\n<\/ul>\n<blockquote class=\"wp-block-quote\"><p><span>English Summary:<\/span><\/p>\n<p>This study proposes a <strong>machine learning<\/strong> approach to improve the diagnosis of non-amnestic Alzheimer's disease (atAD), which is often misdiagnosed compared to typical AD (tAD).<\/p>\n<p>The method utilizes a combination of clinical testing batteries and <strong>MRI data<\/strong>, including hippocampal volume and comprehensive brain-wide features, to classify atAD from non-AD cognitive impairment. The <strong>machine learning<\/strong> model significantly enhances the detection rate (recall) of atAD cases compared to using hippocampal volume alone.<\/p>\n<p>Performance improvements were observed from 52% to 69% for the NACC dataset and 34% to 77% for the ADNI dataset. This approach holds significant implications for improving diagnostic accuracy for non-amnestic atAD in clinical settings using standard clinical tests and MRI.<\/p>\n<\/blockquote>\n<\/div>\n<div class=\"wp-block-group\" style=\"margin-top:40px;margin-bottom:40px\">\n<h2 class=\"wp-block-heading\">\u30b9\u30ad\u30fc\u30de\u30d9\u30fc\u30b9\u306e\u80fd\u52d5\u63a8\u8ad6\u306f\u7d4c\u9a13\u306e\u6025\u901f\u306a\u4e00\u822c\u5316\u3068\u62bd\u8c61\u69cb\u9020\u306e\u524d\u982d\u76ae\u8cea\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u3092\u652f\u6301\u3059\u308b<\/h2>\n<ul>\n<li><strong>\u539f\u984c:<\/strong> Schema-based active inference supports rapid generalization of experience and frontal cortical coding of abstract structure<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\">\u5c02\u9580\u30a2\u30ca\u30ea\u30b9\u30c8\u306e\u5206\u6790<\/h3>\n<div class=\"ai-summary-content\">\n<p>\u672c\u7814\u7a76\u306f\u3001\u7d4c\u9a13\u306e\u4e00\u822c\u5316\u3084\u65b0\u3057\u3044\u72b6\u6cc1\u3078\u306e\u9069\u5fdc\u3092\u53ef\u80fd\u306b\u3059\u308b<strong>\u30b9\u30ad\u30fc\u30de<\/strong>\uff08\u7d4c\u9a13\u9593\u306e\u5171\u901a\u6027\u3092\u6349\u3048\u308b\u62bd\u8c61\u7684\u69cb\u9020\uff09\u306e\u5f62\u6210\u3068\u5229\u7528\u306b\u95a2\u3059\u308b\u8a08\u7b97\u8ad6\u7684\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3092\u63d0\u6848\u3057\u3066\u3044\u307e\u3059\u3002<strong>\u30b9\u30ad\u30fc\u30de\u30d9\u30fc\u30b9\u306e\u968e\u5c64\u7684\u80fd\u52d5\u63a8\u8ad6\uff08S-HAI\uff09<\/strong>\u306f\u3001\u4e88\u6e2c\u51e6\u7406\u3068\u80fd\u52d5\u63a8\u8ad6\u3092\u30b9\u30ad\u30fc\u30de\u30d9\u30fc\u30b9\u306e\u30e1\u30ab\u30cb\u30ba\u30e0\u3068\u7d71\u5408\u3057\u305f\u65b0\u3057\u3044\u67a0\u7d44\u307f\u3067\u3059\u3002<\/p>\n<p>S-HAI\u3067\u306f\u3001\u9ad8\u30ec\u30d9\u30eb\u306e\u751f\u6210\u30e2\u30c7\u30eb\u304c\u62bd\u8c61\u7684\u306a\u30bf\u30b9\u30af\u69cb\u9020\u3092\u30a8\u30f3\u30b3\u30fc\u30c9\u3057\u3001\u4f4e\u30ec\u30d9\u30eb\u30e2\u30c7\u30eb\u304c\u7a7a\u9593\u30ca\u30d3\u30b2\u30fc\u30b7\u30e7\u30f3\u3092\u30a8\u30f3\u30b3\u30fc\u30c9\u3057\u307e\u3059\u3002\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3092\u901a\u3058\u3066\u3001S-HAI\u306f\u7a7a\u9593\u30ca\u30d3\u30b2\u30fc\u30b7\u30e7\u30f3\u30bf\u30b9\u30af\u306b\u304a\u3051\u308b<strong>\u30b9\u30ad\u30fc\u30de\u30d9\u30fc\u30b9\u306e\u6025\u901f\u306a\u4e00\u822c\u5316<\/strong>\u306e\u4e3b\u8981\u306a\u884c\u52d5\u7684\u7279\u5fb4\u3092\u518d\u73fe\u3059\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306b\u306f\u3001\u62bd\u8c61\u30b9\u30ad\u30fc\u30de\u3092\u65b0\u3057\u3044\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u306b\u67d4\u8edf\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3059\u308b\u80fd\u529b\u306a\u3069\u304c\u542b\u307e\u308c\u307e\u3059\u3002<\/p>\n<p>\u3055\u3089\u306b\u3001S-HAI\u306f\u3001\u3052\u3063\u6b6f\u985e\u306e<strong>\u524d\u982d\u524d\u91ce<\/strong>\u3067\u5831\u544a\u3055\u308c\u3066\u3044\u308b\u795e\u7d4c\u30b3\u30fc\u30c9\uff08\u30bf\u30b9\u30af\u4e0d\u5909\u306e\u76ee\u6a19\u9032\u6357\u30bb\u30eb\u3001\u76ee\u6a19\u540c\u4e00\u6027\u30bb\u30eb\u3001\u76ee\u6a19\u3068\u7a7a\u9593\u306e\u5171\u8d77\u30bb\u30eb\u306a\u3069\uff09\u3082\u518d\u73fe\u3057\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u7d50\u679c\u306f\u3001\u30b9\u30ad\u30fc\u30de\u5f62\u6210\u3068\u4e00\u822c\u5316\u304c\u3001\u76ae\u8cea\u304a\u3088\u3073\u6d77\u99ac\u56de\u8def\u5168\u4f53\u306b\u968e\u5c64\u7684\u306b\u5b9f\u88c5\u3055\u308c\u305f\u4e88\u6e2c\u51e6\u7406\u539f\u7406\u304b\u3089\u751f\u3058\u308b\u53ef\u80fd\u6027\u3092\u793a\u5506\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\ud83d\udc49 <strong><a href=\"https:\/\/arxiv.org\/abs\/2601.18946\" target=\"_blank\" rel=\"noopener\">arXiv \u3067\u8a18\u4e8b\u5168\u6587\u3092\u8aad\u3080<\/a><\/strong><\/p>\n<\/div>\n<ul>\n<li><strong>\u8981\u70b9:<\/strong> Schema-based hierarchical active inference (S-HAI) provides a mechanistic account for rapid generalization and frontal cortical coding of abstract structure, bridging behavior, neural data, and theory.<\/li>\n<li><strong>\u8457\u8005:<\/strong> Toon Van de Maele, Tim Verbelen, Dileep George, Giovanni Pezzulo<\/li>\n<\/ul>\n<blockquote class=\"wp-block-quote\"><p><span>English Summary:<\/span><\/p>\n<p>This research introduces a computational framework for <strong>schema<\/strong> formation and utilization, which are crucial for generalizing experience and adapting to new situations. <strong>Schema-based hierarchical active inference (S-HAI)<\/strong> integrates predictive processing and active inference with schema-based mechanisms.<\/p>\n<p>In S-HAI, a higher-level generative model encodes abstract task structure, while a lower-level model encodes spatial navigation. Simulations demonstrate that S-HAI reproduces key behavioral signatures of rapid <strong>schema-based generalization<\/strong> in spatial navigation tasks, including the flexible remapping of abstract schemas onto novel contexts.<\/p>\n<p>Furthermore, S-HAI replicates prominent neural codes reported in rodent <strong>medial prefrontal cortex<\/strong>, such as task-invariant goal-progress cells and goal-identity cells. These findings suggest that schema formation and generalization may arise from hierarchical predictive processing principles implemented across cortical and hippocampal circuits.<\/p>\n<\/blockquote>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u6700\u65b0\u306eAI\u7814\u7a76\u8ad6\u65873\u5831\u3092\u8981\u7d04\u3002\u795e\u7d4c\u56de\u8def\u306e\u975e\u7dda\u5f62\u30ce\u30a4\u30ba\u3001\u30a2\u30eb\u30c4\u30cf\u30a4\u30de\u30fc\u75c5\u306eMRI\u8a3a\u65ad\u652f\u63f4\u3001\u30b9\u30ad\u30fc\u30de\u30d9\u30fc\u30b9\u306e\u80fd\u52d5\u63a8\u8ad6\u306b\u3088\u308b\u8a8d\u77e5\u30e2\u30c7\u30ea\u30f3\u30b0\u306b\u3064\u3044\u3066\u89e3\u8aac\u3002<\/p>\n","protected":false},"author":1,"featured_media":849,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"vkexunit_cta_each_option":"","footnotes":""},"categories":[3],"tags":[8,152,34,47,15,35,63],"class_list":{"0":"post-921","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-columns","8":"tag-ai","10":"tag-34","11":"tag-47","13":"tag-35","14":"tag-63"},"_links":{"self":[{"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/posts\/921","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/comments?post=921"}],"version-history":[{"count":0,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/posts\/921\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/media\/849"}],"wp:attachment":[{"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/media?parent=921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/categories?post=921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/itexplore.org\/jp\/wp-json\/wp\/v2\/tags?post=921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}