Receptive Field\uff08\u53d7\u5bb9\u91ce\uff09\u306f\u3001\u795e\u7d4c\u79d1\u5b66\u3084\u611f\u899a\u79d1\u5b66\u306b\u304a\u3044\u3066\u9577\u304f\u7814\u7a76\u3055\u308c\u3066\u304d\u305f\u6982\u5ff5\u3067\u3059\u3002<\/p>\n\n\n\n
\u751f\u7269\u5b66\u7684\u306b\u306f\u3001\u3042\u308b\u611f\u899a\u795e\u7d4c\u7d30\u80de\u306e\u5fdc\u7b54\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u523a\u6fc0\u9818\u57df\u3092\u6307\u3057\u307e\u3059\u3002\u8996\u899a\u7cfb\u3067\u306f\u3001\u7db2\u819c\u3084\u8996\u91ce\u306e\u7279\u5b9a\u306e\u9818\u57df\u304b\u3089\u5165\u3063\u305f\u60c5\u5831\u304c\u3001\u7279\u5b9a\u306e\u795e\u7d4c\u7d30\u80de\u306e\u6d3b\u52d5\u306b\u5f71\u97ff\u3059\u308b\u95a2\u4fc2\u3068\u3057\u3066\u7814\u7a76\u3055\u308c\u3066\u304d\u307e\u3057\u305f\u3002\u7c73\u56fd\u56fd\u7acb\u533b\u5b66\u56f3\u66f8\u9928\u306eNeuroscience\u3067\u306f\u3001receptive field\u3092\u3001\u53d7\u5bb9\u5668\u8868\u9762\u306e\u3046\u3061\u611f\u899a\u795e\u7d4c\u7d30\u80de\u306a\u3069\u306e\u5fdc\u7b54\u3092\u5f15\u304d\u8d77\u3053\u3059\u9818\u57df\u3068\u3057\u3066\u5b9a\u7fa9\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u3053\u306e\u6982\u5ff5\u306f\u3001\u751f\u7269\u5b66\u3084\u795e\u7d4c\u79d1\u5b66\u3060\u3051\u306b\u3068\u3069\u307e\u308a\u307e\u305b\u3093\u3002<\/p>\n\n\n\n
\u4eba\u5de5\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3001\u3068\u308a\u308f\u3051\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30d3\u30b8\u30e7\u30f3\u306e\u767a\u5c55\u3068\u3068\u3082\u306b\u3001Receptive Field\u306fAI\u304c\u5165\u529b\u60c5\u5831\u3092\u3069\u306e\u7bc4\u56f2\u304b\u3089\u53d7\u3051\u53d6\u308a\u3001\u305d\u308c\u3092\u3069\u306e\u3088\u3046\u306b\u5185\u90e8\u8868\u73fe\u3078\u5909\u63db\u3059\u308b\u304b\u3092\u8003\u3048\u308b\u305f\u3081\u306e\u91cd\u8981\u306a\u6280\u8853\u6982\u5ff5\u3068\u306a\u3063\u3066\u304d\u307e\u3057\u305f\u3002<\/p>\n\n\n\n
\u7573\u307f\u8fbc\u307f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08CNN\uff09\u3067\u306f\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u5185\u306e\u3042\u308b\u30e6\u30cb\u30c3\u30c8\u304c\u5165\u529b\u753b\u50cf\u306e\u3069\u306e\u7bc4\u56f2\u304b\u3089\u5f71\u97ff\u3092\u53d7\u3051\u308b\u304b\u3092Receptive Field\u3068\u3057\u3066\u8003\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n
\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u304c\u6df1\u304f\u306a\u308b\u306b\u3064\u308c\u3066\u3001\u500b\u3005\u306e\u7279\u5fb4\u8868\u73fe\u304c\u53c2\u7167\u3067\u304d\u308b\u5165\u529b\u9818\u57df\u306f\u5e83\u304c\u308a\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001Receptive Field\u306e\u8a2d\u8a08\u3084\u5927\u304d\u3055\u306f\u3001\u7269\u4f53\u8a8d\u8b58\u3001\u753b\u50cf\u5206\u985e\u3001\u7269\u4f53\u691c\u51fa\u3001\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u306a\u3069\u3067\u3001\u5c40\u6240\u7684\u306a\u7279\u5fb4\u3068\u3088\u308a\u5e83\u3044\u6587\u8108\u3092\u3069\u306e\u3088\u3046\u306b\u7d71\u5408\u3059\u308b\u304b\u306b\u95a2\u4fc2\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\u3055\u3089\u306b\u7814\u7a76\u3067\u306f\u3001\u7406\u8ad6\u4e0a\u8a08\u7b97\u3055\u308c\u308bReceptive Field\u306e\u3059\u3079\u3066\u304c\u5b9f\u969b\u306b\u540c\u3058\u5f37\u3055\u3067\u5229\u7528\u3055\u308c\u3066\u3044\u308b\u308f\u3051\u3067\u306f\u306a\u3044\u3053\u3068\u304c\u793a\u3055\u308c\u3001\u300cEffective Receptive Field\u300d\u3068\u3044\u3046\u8003\u3048\u65b9\u3082\u63d0\u6848\u3055\u308c\u3066\u3044\u307e\u3059\u3002Luo\u3089\u306e\u7814\u7a76\u3067\u306f\u3001\u5b9f\u969b\u306b\u5f37\u304f\u5f71\u97ff\u3059\u308b\u9818\u57df\u304c\u7406\u8ad6\u4e0a\u306eReceptive Field\u306e\u4e00\u90e8\u306b\u96c6\u4e2d\u3059\u308b\u3053\u3068\u304c\u793a\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n
\u3064\u307e\u308aReceptive Field\u306f\u3001\u5358\u306b\u300cAI\u304c\u3069\u3053\u3092\u898b\u308b\u304b\u300d\u3060\u3051\u3067\u306f\u306a\u304f\u3001<\/p>\n\n\n\n
\u3069\u306e\u7bc4\u56f2\u306e\u60c5\u5831\u3092\u53d7\u3051\u53d6\u308a\u3001\u3069\u306e\u60c5\u5831\u3092\u5f37\u304f\u5229\u7528\u3057\u3001\u305d\u308c\u3089\u3092\u3069\u306e\u3088\u3046\u306b\u7d71\u5408\u3057\u3066\u8a8d\u8b58\u3078\u5909\u3048\u308b\u306e\u304b<\/strong><\/p>\n\n\n\n \u3068\u3044\u3046\u554f\u984c\u306b\u3064\u306a\u304c\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u73fe\u5728\u306eAI\u3067\u306f\u3001\u753b\u50cf\u8a8d\u8b58\u30e2\u30c7\u30eb\u306e\u69cb\u9020\u305d\u306e\u3082\u306e\u3082\u6025\u901f\u306b\u5909\u5316\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n CNN\u3060\u3051\u3067\u306a\u304f\u3001Vision Transformer\u3084Attention\u3092\u5229\u7528\u3057\u305f\u30e2\u30c7\u30eb\u3067\u306f\u3001\u5c40\u6240\u7684\u306a\u60c5\u5831\u3060\u3051\u3067\u306f\u306a\u304f\u3001\u753b\u50cf\u5185\u306e\u96e2\u308c\u305f\u9818\u57df\u540c\u58eb\u306e\u95a2\u4fc2\u3092\u6271\u3048\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002Transformer\u7cfb\u306e\u8996\u899a\u30e2\u30c7\u30eb\u306b\u3064\u3044\u3066\u3082\u3001\u7814\u7a76\u8ad6\u6587\u3067\u306f\u5c40\u6240\u7684\u30fb\u5e83\u57df\u7684\u3042\u308b\u3044\u306fglobal\u306aReceptive Field\u3068\u3044\u3046\u89b3\u70b9\u304b\u3089\u3001\u305d\u306e\u60c5\u5831\u53d6\u5f97\u7bc4\u56f2\u304c\u5206\u6790\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u3053\u306e\u3053\u3068\u306f\u3001Receptive Field\u3068\u3044\u3046\u6982\u5ff5\u304c\u7279\u5b9a\u306eCNN\u69cb\u9020\u3060\u3051\u306b\u9589\u3058\u305f\u3082\u306e\u3067\u306f\u306a\u304f\u3001<\/p>\n\n\n\n AI\u304c\u300c\u3069\u3053\u307e\u3067\u306e\u5165\u529b\u3092\u77e5\u899a\u5bfe\u8c61\u3068\u3057\u3066\u6271\u3046\u304b\u300d<\/strong><\/p>\n\n\n\n \u3068\u3044\u3046\u3001\u3088\u308a\u4e00\u822c\u7684\u306a\u554f\u984c\u3078\u5e83\u304c\u308a\u5f97\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u30b0\u30e9\u30d5\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08GNN\uff09\u3067\u3082\u3001\u3042\u308b\u30ce\u30fc\u30c9\u304c\u60c5\u5831\u3092\u53d7\u3051\u53d6\u308b\u5468\u8fba\u9818\u57df\u3092Receptive Field\u3068\u3057\u3066\u6349\u3048\u308b\u7814\u7a76\u304c\u3042\u308a\u307e\u3059\u3002Geometric Deep Learning\u306e\u4f53\u7cfb\u3067\u3082\u3001\u4e00\u5b9a\u8ddd\u96e2\u5185\u306e\u8fd1\u508d\u60c5\u5831\u3092\u5165\u529b\u3068\u3057\u3066\u5229\u7528\u3059\u308b\u9818\u57df\u304creceptive field\u3068\u3057\u3066\u8aac\u660e\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u3057\u305f\u304c\u3063\u3066\u3001\u753b\u50cf\u3068\u3044\u3046\u4e8c\u6b21\u5143\u7a7a\u9593\u3092\u8d8a\u3048\u3066\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3001\u95a2\u4fc2\u69cb\u9020\u3001\u7a7a\u9593\u60c5\u5831\u306a\u3069\u3092\u51e6\u7406\u3059\u308bAI\u306b\u307e\u3067\u6982\u5ff5\u304c\u62e1\u5f35\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n Receptive Field\u3068\u306e\u63a5\u7d9a\u306f\u3001\u8996\u899a\u3060\u3051\u306b\u9650\u5b9a\u3055\u308c\u307e\u305b\u3093\u3002<\/p>\n\n\n\n \u97f3\u58f0\u30fb\u97f3\u97ff\u51e6\u7406\u3067\u306f\u3001Google DeepMind\u304c\u958b\u767a\u3057\u305fWaveNet\u306e\u3088\u3046\u306a\u7573\u307f\u8fbc\u307f\u578b\u30e2\u30c7\u30eb\u3067\u3001\u6642\u9593\u65b9\u5411\u306b\u3069\u306e\u7bc4\u56f2\u306e\u97f3\u58f0\u4fe1\u53f7\u3092\u53c2\u7167\u3059\u308b\u304b\u3092Receptive Field\u3068\u3057\u3066\u6271\u3044\u307e\u3059\u3002WaveNet\u3067\u306fdilated convolution\u306b\u3088\u3063\u3066Receptive Field\u3092\u5927\u304d\u304f\u3057\u3001\u6570\u5343\u306e\u6642\u9593\u30b9\u30c6\u30c3\u30d7\u306b\u308f\u305f\u308b\u97f3\u97ff\u60c5\u5831\u3092\u6271\u3046\u69cb\u9020\u304c\u63a1\u7528\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n\n\n\n \u3053\u3053\u3067\u306fReceptive Field\u306f\u300c\u7a7a\u9593\u300d\u3067\u306f\u306a\u304f\u300c\u6642\u9593\u300d\u306b\u5e83\u304c\u308a\u307e\u3059\u3002<\/p>\n\n\n\n \u3053\u306e\u8003\u3048\u65b9\u306f\u3001AI\u304c\u753b\u50cf\u3001\u6620\u50cf\u3001\u97f3\u3001\u30bb\u30f3\u30b5\u30fc\u60c5\u5831\u306a\u3069\u7570\u306a\u308b\u5f62\u5f0f\u306e\u5165\u529b\u3092\u540c\u6642\u306b\u6271\u3046\u3088\u3046\u306b\u306a\u308b\u306b\u3064\u308c\u3066\u3001\u3055\u3089\u306b\u91cd\u8981\u306a\u7814\u7a76\u30c6\u30fc\u30de\u306b\u3064\u306a\u304c\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n Receptive Field\u3068AI\u306e\u63a5\u7d9a\u3092\u8003\u3048\u308b\u4e0a\u3067\u7279\u306b\u6ce8\u76ee\u3055\u308c\u308b\u306e\u304c\u3001Neuromorphic Computing\u3068Event-based Vision\u3067\u3059\u3002<\/p>\n\n\n\n 2025\u5e74\u306bNature Communications\u3067\u767a\u8868\u3055\u308c\u305f\u7814\u7a76\u3067\u306f\u3001Spiking Neural Network\u306b\u5bfe\u3057\u3066\u300cspatio-temporal receptive fields\uff08\u6642\u7a7a\u9593\u53d7\u5bb9\u91ce\uff09\u300d\u3092\u5c0e\u5165\u3057\u3001\u751f\u7269\u306e\u8996\u899a\u51e6\u7406\u306b\u8fd1\u3044\u69cb\u9020\u3092\u5229\u7528\u3057\u3066\u30a4\u30d9\u30f3\u30c8\u30d9\u30fc\u30b9\u8996\u899a\u51e6\u7406\u3092\u6539\u5584\u3059\u308b\u65b9\u6cd5\u304c\u7814\u7a76\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u7814\u7a76\u8005\u3089\u306f\u3001\u3053\u306e\u67a0\u7d44\u307f\u304csignal processing\u3084event-based vision\u3060\u3051\u3067\u306a\u304f\u3001\u5c06\u6765\u7684\u306b\u306fmemory\u3084control\u306a\u3069\u306e\u6642\u7a7a\u9593\u51e6\u7406\u306b\u3082\u62e1\u5f35\u53ef\u80fd\u3067\u3042\u308b\u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u3055\u3089\u306b2026\u5e74\u306b\u306f\u3001\u4e21\u751f\u985e\u306e\u7db2\u819c\u6a5f\u80fd\u306b\u7740\u60f3\u3092\u5f97\u305f\u30cb\u30e5\u30fc\u30ed\u30e2\u30fc\u30d5\u30a3\u30c3\u30af\u8996\u899a\u30b7\u30b9\u30c6\u30e0\u306b\u304a\u3044\u3066\u3001center-surround receptive-field-like processing\u3092\u96fb\u5b50\u30c7\u30d0\u30a4\u30b9\u4e0a\u3067\u518d\u73fe\u3057\u3001\u5f31\u3044\u5149\u74b0\u5883\u3067\u7a7a\u9593\u30b3\u30f3\u30c8\u30e9\u30b9\u30c8\u3092\u5f37\u8abf\u3059\u308b\u7814\u7a76\u3082Nature Communications\u3067\u5831\u544a\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u3053\u3053\u3067\u306f\u300c\u53d7\u5bb9\u91ce\u300d\u306f\u3001\u4eba\u5de5\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u4e0a\u3060\u3051\u306e\u62bd\u8c61\u6982\u5ff5\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n\n\n\n \u30bb\u30f3\u30b5\u30fc\u3001\u96fb\u5b50\u30c7\u30d0\u30a4\u30b9\u3001\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3001\u77e5\u899a\u51e6\u7406\u304c\u4e00\u4f53\u5316\u3057\u305f\u4eba\u5de5\u7684\u306a\u611f\u899a\u30b7\u30b9\u30c6\u30e0<\/strong><\/p>\n\n\n\n \u3078\u63a5\u7d9a\u3057\u59cb\u3081\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u533b\u7642\u753b\u50cfAI\u3067\u3082Receptive Field\u306f\u73fe\u5728\u3082\u5b9f\u969b\u306e\u8a2d\u8a08\u8ab2\u984c\u3067\u3059\u3002<\/p>\n\n\n\n 2026\u5e74\u306e\u533b\u7642\u753b\u50cf\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u7814\u7a76\u3067\u306f\u3001CNN\u306e\u6709\u9650\u306aReceptive Field\u306b\u3088\u3063\u3066\u9060\u8ddd\u96e2\u306e\u60c5\u5831\u95a2\u4fc2\u3092\u5341\u5206\u306b\u6271\u3048\u306a\u3044\u3053\u3068\u3092\u8ab2\u984c\u3068\u3057\u3066\u6319\u3052\u3001Transformer\u3084Attention\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001\u5c40\u6240\u60c5\u5831\u3068global context\u306e\u53cc\u65b9\u3092\u5229\u7528\u3059\u308b\u30e2\u30c7\u30eb\u304c\u7814\u7a76\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u3053\u308c\u306fReceptive Field\u304c\u904e\u53bb\u306eCNN\u7528\u8a9e\u3068\u3057\u3066\u6b8b\u3063\u3066\u3044\u308b\u3060\u3051\u3067\u306f\u306a\u304f\u3001\u73fe\u5728\u306eAI\u30e2\u30c7\u30eb\u306b\u304a\u3044\u3066\u3082\u3001<\/p>\n\n\n\n \u300c\u5c40\u6240\u3092\u898b\u308b\u306e\u304b\u300d \u3068\u3044\u3046\u30e2\u30c7\u30eb\u8a2d\u8a08\u4e0a\u306e\u554f\u984c\u3068\u76f4\u63a5\u95a2\u4fc2\u3057\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n \u4eca\u5f8c\u306eAI\u306f\u3001\u30c6\u30ad\u30b9\u30c8\u3084\u9759\u6b62\u753b\u50cf\u3060\u3051\u3092\u51e6\u7406\u3059\u308b\u30b7\u30b9\u30c6\u30e0\u304b\u3089\u3001\u6620\u50cf\u3001\u97f3\u58f0\u3001\u4f4d\u7f6e\u3001\u8ddd\u96e2\u3001\u52d5\u304d\u3001\u74b0\u5883\u30bb\u30f3\u30b5\u30fc\u306a\u3069\u3001\u591a\u6570\u306e\u60c5\u5831\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u30b7\u30b9\u30c6\u30e0\u3078\u5e83\u304c\u3063\u3066\u3044\u304f\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/p>\n\n\n\n \u30ed\u30dc\u30c6\u30a3\u30af\u30b9\u3001\u81ea\u5f8b\u30b7\u30b9\u30c6\u30e0\u3001\u30a6\u30a7\u30a2\u30e9\u30d6\u30ebAI\u3001\u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30ebAI\u3001Neuromorphic Computing\u3001Event-based Vision\u306a\u3069\u3067\u306f\u3001AI\u304c\u73fe\u5b9f\u4e16\u754c\u304b\u3089\u5927\u91cf\u306e\u9023\u7d9a\u3057\u305f\u60c5\u5831\u3092\u53d7\u3051\u53d6\u308a\u3001\u305d\u306e\u4e2d\u304b\u3089\u5fc5\u8981\u306a\u60c5\u5831\u3092\u9078\u629e\u3057\u3001\u7d71\u5408\u3057\u3001\u884c\u52d5\u3078\u5909\u63db\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n \u3053\u306e\u9818\u57df\u3067Receptive Field\u3068\u3044\u3046\u8a00\u8449\u304c\u5c06\u6765\u3069\u3053\u307e\u3067\u5171\u901a\u6982\u5ff5\u3068\u3057\u3066\u4f7f\u7528\u3055\u308c\u308b\u304b\u306f\u3001\u73fe\u6642\u70b9\u3067\u306f\u78ba\u5b9a\u3057\u3066\u3044\u307e\u305b\u3093\u3002<\/p>\n\n\n\n \u3057\u304b\u3057\u3001<\/p>\n\n\n\n AI\u304c\u3069\u306e\u5165\u529b\u3092\u53d7\u3051\u53d6\u308a\u3001\u3069\u306e\u7bc4\u56f2\u3092\u8a8d\u8b58\u3057\u3001\u3069\u306e\u60c5\u5831\u3092\u7d71\u5408\u3057\u3066\u4e16\u754c\u3092\u7406\u89e3\u3059\u308b\u306e\u304b<\/strong><\/p>\n\n\n\n \u3068\u3044\u3046\u554f\u984c\u305d\u306e\u3082\u306e\u306f\u3001AI\u304c\u30c7\u30b8\u30bf\u30eb\u7a7a\u9593\u304b\u3089\u7269\u7406\u4e16\u754c\u3078\u5e83\u304c\u308b\u307b\u3069\u91cd\u8981\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n Receptive Field\u306f\u3001\u305d\u306e\u554f\u984c\u3092\u795e\u7d4c\u79d1\u5b66\u3068\u4eba\u5de5\u77e5\u80fd\u306e\u53cc\u65b9\u304b\u3089\u8003\u3048\u308b\u3053\u3068\u306e\u3067\u304d\u308b\u6982\u5ff5\u306e\u4e00\u3064\u3067\u3059\u3002<\/p>\n\n\n\n 22degrees\u306f2026\u5e748\u670830\u65e5\u3001 Receptive Field\u306f\u3001\u795e\u7d4c\u79d1\u5b66\u304b\u3089\u4eba\u5de5\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3078\u53d7\u3051\u7d99\u304c\u308c\u3001Computer Vision\u3001CNN\u3001Transformer\u3001Graph Neural Network\u3001\u97f3\u97ff\u51e6\u7406\u3001\u533b\u7642\u753b\u50cf\u3001Neuromorphic Computing\u3001Event-based Vision\u306a\u3069\u3001\u7570\u306a\u308b\u6280\u8853\u9818\u57df\u306e\u4e2d\u3067\u305d\u306e\u610f\u5473\u3092\u5e83\u3052\u3066\u304d\u307e\u3057\u305f\u3002<\/p>\n\n\n\n \u73fe\u5728\u78ba\u7acb\u3057\u3066\u3044\u308b\u6280\u8853\u7684\u7528\u9014\u3068\u3001\u3053\u308c\u304b\u3089\u751f\u307e\u308c\u308b\u53ef\u80fd\u6027\u306e\u3042\u308b\u7528\u9014\u3092\u533a\u5225\u3057\u306a\u304c\u3089\u300122degrees\u306f\u4eca\u5f8c\u3082Receptive Field\u3068\u4eba\u5de5\u77e5\u80fd\u3068\u306e\u63a5\u7d9a\u3001\u305d\u306e\u6280\u8853\u7684\u5909\u5316\u3068\u62e1\u5f35\u53ef\u80fd\u6027\u3092\u7d99\u7d9a\u3057\u3066\u7814\u7a76\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n\n\n\n \u00a9 2026 22degrees. All Rights Reserved.<\/p>\n\n\n\n A receptive field is a concept that has long been studied in neuroscience and sensory science.<\/p>\n\n\n\n In biology, it generally refers to the region of sensory space in which a stimulus can influence the response of a particular sensory neuron. In visual systems, receptive fields have been studied as the relationship between specific regions of the retina or visual field and the activity of particular neurons.<\/p>\n\n\n\n The concept, however, is not limited to biology or neuroscience.<\/p>\n\n\n\n With the development of artificial neural networks, particularly in computer vision, the receptive field has also become an important technical concept for understanding the range of input information that can influence a unit within an AI model and how that information is transformed into internal representations.<\/p>\n\n\n\n In convolutional neural networks, or CNNs, the receptive field of a unit can be understood as the region of the original input image that can influence that unit.<\/p>\n\n\n\n As neural networks become deeper, individual feature representations can receive information from progressively larger portions of the input.<\/p>\n\n\n\n For this reason, the size and structure of receptive fields are closely related to how AI systems combine local features with broader contextual information in tasks such as image classification, object recognition, object detection, and image segmentation.<\/p>\n\n\n\n Research has also shown that the entire theoretically calculated receptive field does not necessarily contribute equally to the actual response of a neural network.<\/p>\n\n\n\n This distinction led to the concept of the Effective Receptive Field, which describes the region of the theoretical receptive field that has the strongest practical influence on a unit’s output.<\/p>\n\n\n\n Receptive fields therefore relate not only to the question of where an AI system looks, but also to a broader problem:<\/p>\n\n\n\n What range of information does an AI system receive, which parts of that information exert the greatest influence, and how are those inputs integrated into recognition?<\/strong><\/p>\n\n\n\n The architecture of visual AI systems continues to evolve.<\/p>\n\n\n\n In addition to CNNs, Vision Transformers and attention-based models can process relationships between spatially distant regions of an image.<\/p>\n\n\n\n Researchers have therefore continued to examine these systems through concepts related to local, global, and effective receptive fields.<\/p>\n\n\n\n This suggests that the receptive field is not necessarily limited to a specific CNN architecture.<\/p>\n\n\n\n It can also be understood more broadly in relation to another fundamental question:<\/p>\n\n\n\n How much of an input environment can an AI system use when constructing an internal representation?<\/strong><\/p>\n\n\n\n Similar terminology also appears in research on Graph Neural Networks.<\/p>\n\n\n\n In graph-based models, information is propagated across neighboring nodes, and the region of the graph capable of influencing a particular node representation can likewise be described in terms of a receptive field.<\/p>\n\n\n\n The underlying idea therefore extends beyond two-dimensional images and can be applied to structured relationships, networks, spatial systems, and other forms of machine-readable environments.<\/p>\n\n\n\n Receptive fields are not limited to visual information.<\/p>\n\n\n\n In speech and audio processing, convolutional architectures can use receptive fields to describe the temporal range of an audio signal that influences a particular output.<\/p>\n\n\n\n Models such as WaveNet demonstrated how dilated convolutions can greatly expand the temporal receptive field, allowing a network to use information across long sequences of audio samples.<\/p>\n\n\n\n In this context, the receptive field expands from space into time.<\/p>\n\n\n\n This becomes increasingly relevant as AI systems begin to combine images, video, audio, motion, sensor measurements, and other forms of input.<\/p>\n\n\n\n One area of particular interest is the connection between receptive fields, neuromorphic computing, and event-based vision.<\/p>\n\n\n\n Recent research has explored spatio-temporal receptive fields in spiking neural networks, using structures inspired by biological sensory processing to improve the handling of dynamic visual information.<\/p>\n\n\n\n Other neuromorphic research has investigated electronic systems that reproduce receptive-field-like processing found in biological vision.<\/p>\n\n\n\n In such systems, concepts originally associated with biological neurons are increasingly connected with artificial sensors, electronic devices, and computational architectures.<\/p>\n\n\n\n The receptive field is therefore no longer only an abstract concept inside a neural network.<\/p>\n\n\n\n It can also become part of research into systems in which sensing, signal processing, neural computation, and environmental perception are more closely integrated.<\/p>\n\n\n\n Receptive fields also remain relevant in medical imaging.<\/p>\n\n\n\n One of the challenges of conventional convolutional architectures is that a limited receptive field can make it difficult to capture relationships between spatially distant structures.<\/p>\n\n\n\n For this reason, modern medical image segmentation systems increasingly combine convolutional networks with Transformer and attention mechanisms in order to integrate local detail with broader contextual information.<\/p>\n\n\n\n The continuing presence of receptive-field-related problems in these systems demonstrates that the concept is not merely a historical term inherited from early CNN research.<\/p>\n\n\n\n It remains connected to fundamental questions of model architecture:<\/p>\n\n\n\n How much local information should be used?<\/p>\n\n\n\n How much global information should be used?<\/p>\n\n\n\n At what spatial scales should information be integrated?<\/p>\n\n\n\n AI is increasingly moving beyond systems that process only text or static images.<\/p>\n\n\n\n Future systems are expected to combine information from video, audio, location, distance, motion, cameras, environmental sensors, and other continuous streams of physical-world data.<\/p>\n\n\n\n Robotics, autonomous systems, wearable AI, multimodal AI, neuromorphic computing, and event-based vision all require machines to receive large amounts of information from their environments, identify what is relevant, integrate multiple inputs, and convert perception into decisions or actions.<\/p>\n\n\n\n It is not yet possible to determine how broadly the specific term \u201creceptive field\u201d will ultimately be used across all of these future technologies.<\/p>\n\n\n\n However, the underlying problem is likely to become increasingly important:<\/p>\n\n\n\n What information can an AI system receive, what portion of its environment can it perceive, and how can those signals be integrated into an understanding of the world?<\/strong><\/p>\n\n\n\n Receptive Field is one of the concepts through which this question can be examined from both neuroscience and artificial intelligence.<\/p>\n\n\n\n On August 30, 2026, 22degrees acquired The concept of the receptive field has moved from neuroscience into artificial neural networks and now appears across multiple areas of AI research, including computer vision, convolutional neural networks, Transformer architectures, graph neural networks, audio processing, medical imaging, neuromorphic computing, and event-based vision.<\/p>\n\n\n\n 22degrees will continue to study the relationship between Receptive Field and artificial intelligence, while distinguishing between applications that are already technically established and those that remain future possibilities.<\/p>\n\n\n\n The research will focus on how the concept evolves as AI systems expand from digital information processing toward increasingly complex forms of perception, sensing, and interaction with the physical world.<\/p>\n\n\n\n \u00a9 2026 22degrees. All Rights Reserved.<\/p>\n","protected":false},"excerpt":{"rendered":" receptivefield.ai \u2014 AI\u306e\u77e5\u899a\u3068\u5165\u529b\u8a8d\u8b58\u3092\u3081\u3050\u308b\u7814\u7a76 Receptive Field Receptive Field\uff08\u53d7\u5bb9\u91ce\uff09\u306f\u3001\u795e\u7d4c\u79d1\u5b66\u3084\u611f\u899a\u79d1\u5b66\u306b\u304a\u3044\u3066\u9577\u304f\u7814\u7a76\u3055\u308c\u3066\u304d\u305f\u6982\u5ff5\u3067\u3059\u3002 \u751f\u7269\u5b66\u7684\u306b\u306f\u3001 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-3510","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/ruma22degrees.com\/index.php?rest_route=\/wp\/v2\/pages\/3510","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ruma22degrees.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ruma22degrees.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ruma22degrees.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ruma22degrees.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3510"}],"version-history":[{"count":2,"href":"https:\/\/ruma22degrees.com\/index.php?rest_route=\/wp\/v2\/pages\/3510\/revisions"}],"predecessor-version":[{"id":3515,"href":"https:\/\/ruma22degrees.com\/index.php?rest_route=\/wp\/v2\/pages\/3510\/revisions\/3515"}],"wp:attachment":[{"href":"https:\/\/ruma22degrees.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3510"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}CNN\u304b\u3089\u3001\u3088\u308a\u5e83\u3044AI\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3078<\/h2>\n\n\n\n
\u300c\u898b\u308b\u300d\u3060\u3051\u3067\u306f\u306a\u3044Receptive Field<\/h2>\n\n\n\n
2026\u5e74\u6642\u70b9\u3067\u898b\u3048\u59cb\u3081\u3066\u3044\u308b\u6b21\u306e\u9818\u57df<\/h2>\n\n\n\n
\u533b\u7642\u753b\u50cfAI\u306b\u304a\u3051\u308b\u73fe\u5728\u306e\u610f\u5473<\/h2>\n\n\n\n
\u300c\u5168\u4f53\u3092\u898b\u308b\u306e\u304b\u300d
\u300c\u3069\u306e\u30b9\u30b1\u30fc\u30eb\u306e\u60c5\u5831\u3092\u7d71\u5408\u3059\u308b\u306e\u304b\u300d<\/p>\n\n\n\nAI\u304c\u73fe\u5b9f\u4e16\u754c\u3092\u77e5\u899a\u3059\u308b\u6642\u4ee3\u3078<\/h2>\n\n\n\n
receptivefield.ai<\/h2>\n\n\n\n
receptivefield.ai<\/code> \u3092\u53d6\u5f97\u3057\u3001Domain Asset 003\u3068\u3057\u3066\u7814\u7a76\u3092\u958b\u59cb\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\nreceptivefield.ai \u2014 Research on AI Perception and Input Recognition<\/h1>\n\n\n\n
Receptive Field<\/h2>\n\n\n\n
Receptive Fields in Artificial Intelligence<\/h2>\n\n\n\n
From CNNs to Broader AI Architectures<\/h2>\n\n\n\n
Beyond Vision<\/h2>\n\n\n\n
Emerging Connections in Neuromorphic and Event-Based Systems<\/h2>\n\n\n\n
Receptive Fields in Medical Imaging AI<\/h2>\n\n\n\n
Toward AI Systems That Perceive the Physical World<\/h2>\n\n\n\n
receptivefield.ai<\/h2>\n\n\n\n
receptivefield.ai<\/code> and began researching it as Domain Asset 003.<\/p>\n\n\n\n