{"id":2696,"date":"2025-08-12T11:28:30","date_gmt":"2025-08-12T11:28:30","guid":{"rendered":"https:\/\/blog.daiconext.com\/?p=2696"},"modified":"2025-08-12T17:21:19","modified_gmt":"2025-08-12T17:21:19","slug":"la-neurona-de-mcculloch-pitts","status":"publish","type":"post","link":"https:\/\/blog.daiconext.com\/index.php\/2025\/08\/12\/la-neurona-de-mcculloch-pitts\/","title":{"rendered":"La neurona de McCulloch\u2013Pitts"},"content":{"rendered":"\n<h1 class=\"wp-block-heading\">El origen de las neuronas artificiales<\/h1>\n\n\n\n<p>\u00bfSab\u00edas que las bases de la inteligencia artificial actual comenzaron con un modelo matem\u00e1tico muy sencillo en los a\u00f1os 40? Hoy te cuento sobre la <strong>neurona de McCulloch\u2013Pitts<\/strong>, el primer modelo artificial de neurona, que sent\u00f3 las bases para las redes neuronales modernas.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u00bfQu\u00e9 es la neurona de McCulloch\u2013Pitts?<\/strong><\/h2>\n\n\n\n<p>En 1943, los cient\u00edficos <strong>Warren McCulloch<\/strong> y <strong>Walter Pitts<\/strong> propusieron una forma matem\u00e1tica muy simple de representar c\u00f3mo funciona una neurona biol\u00f3gica, pero usando l\u00f3gica binaria.<br>Este modelo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Recibe varias <strong>entradas binarias<\/strong> (0 o 1).<\/li>\n\n\n\n<li>Suma esas entradas.<\/li>\n\n\n\n<li>Si la suma supera un cierto <strong>umbral<\/strong>, la neurona se activa y da salida 1.<\/li>\n\n\n\n<li>Si no, la salida es 0.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u00bfC\u00f3mo funciona?<\/strong><\/h2>\n\n\n\n<p>Imagina que tienes 3 entradas: cada una puede ser 0 (apagada) o 1 (encendida). La neurona suma esas entradas y las compara con un umbral, por ejemplo 2.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Si la suma es igual o mayor que 2, la neurona responde con un 1 (activa).<\/li>\n\n\n\n<li>Si es menor, responde 0 (inactiva).<br><\/li>\n<\/ul>\n\n\n\n<p>Es un poco como decir: \u201cS\u00ed al menos 2 de estas condiciones se cumplen, entonces s\u00ed\u201d.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u00bfPor qu\u00e9 es importante?<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Fue <strong>el primer modelo matem\u00e1tico<\/strong> que intent\u00f3 replicar la funci\u00f3n b\u00e1sica de una neurona real.<br><\/li>\n\n\n\n<li>Demostr\u00f3 que con estas neuronas simples, combinadas en red, se pod\u00edan realizar operaciones l\u00f3gicas complejas (como AND, OR, NOT).<br><\/li>\n<\/ul>\n\n\n\n<p>Sent\u00f3 las bases para el desarrollo del <strong>perceptr\u00f3n<\/strong> y las redes neuronales que hoy impulsan la inteligencia artificial.<\/p>\n\n\n\n<p>\u00bfSab\u00edas que las bases de la inteligencia artificial actual comenzaron con un modelo matem\u00e1tico muy sencillo en los a\u00f1os 40? Hoy te cuento sobre la <strong>neurona de McCulloch\u2013Pitts<\/strong>, el primer modelo artificial de neurona, que sent\u00f3 las bases para las redes neuronales modernas.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u00bfQu\u00e9 es la neurona de McCulloch\u2013Pitts?<\/strong><\/h2>\n\n\n\n<p>En 1943, los cient\u00edficos <strong>Warren McCulloch<\/strong> y <strong>Walter Pitts<\/strong> propusieron una forma matem\u00e1tica muy simple de representar c\u00f3mo funciona una neurona biol\u00f3gica, pero usando l\u00f3gica binaria.<br>Este modelo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Recibe varias <strong>entradas binarias<\/strong> (0 o 1).<\/li>\n\n\n\n<li>Suma esas entradas.<\/li>\n\n\n\n<li>Si la suma supera un cierto <strong>umbral<\/strong>, la neurona se activa y da salida 1.<\/li>\n\n\n\n<li>Si no, la salida es 0.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u00bfC\u00f3mo funciona?<\/strong><\/h2>\n\n\n\n<p>Imagina que tienes 3 entradas: cada una puede ser 0 (apagada) o 1 (encendida). La neurona suma esas entradas y las compara con un umbral, por ejemplo 2.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Si la suma es igual o mayor que 2, la neurona responde con un 1 (activa).<\/li>\n\n\n\n<li>Si es menor, responde 0 (inactiva).<br><\/li>\n<\/ul>\n\n\n\n<p>Es un poco como decir: \u201cS\u00ed al menos 2 de estas condiciones se cumplen, entonces s\u00ed\u201d.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u00bfPor qu\u00e9 es importante?<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Fue <strong>el primer modelo matem\u00e1tico<\/strong> que intent\u00f3 replicar la funci\u00f3n b\u00e1sica de una neurona real.<br><\/li>\n\n\n\n<li>Demostr\u00f3 que con estas neuronas simples, combinadas en red, se pod\u00edan realizar operaciones l\u00f3gicas complejas (como AND, OR, NOT).<br><\/li>\n<\/ul>\n\n\n\n<p>Sent\u00f3 las bases para el desarrollo de las redes neuronales que hoy impulsan la inteligencia artificial.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe title=\"Neurona McCulloch\u2013Pitts\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/qG8qUdyXmxM?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Neurona McCulloch\u2013Pitts (c\u00f3digo)<\/h2>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#2e3440ff\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" style=\"color:#d8dee9ff;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><pre class=\"code-block-pro-copy-button-pre\" aria-hidden=\"true\"><textarea class=\"code-block-pro-copy-button-textarea\" tabindex=\"-1\" aria-hidden=\"true\" readonly>import numpy as np\n\nclass MPNeuron:\n    def _init_(self):\n        self.threshold = None\n        \n    def model(self, x):\n        # input: [1, 0, 1, 0] [x1, x2, xn...]\n        z = sum(x)\n        return ( z >= self.threshold)\n    \n    def predict(self, X):\n        # input : [[1, 0, 1, 0], [1, 0, 1, 1]]\n        Y = []\n        for x in X:\n            result = self.model(x)\n            Y.append(result)\n        return np.array(Y)<\/textarea><\/pre><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki nord\" style=\"background-color: #2e3440ff\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #81A1C1\">import<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">numpy<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #81A1C1\">as<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">np<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #8FBCBB\">class<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">MPNeuron<\/span><span style=\"color: #D8DEE9FF\">:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    <\/span><span style=\"color: #8FBCBB\">def<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">_init_<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #8FBCBB\">self<\/span><span style=\"color: #D8DEE9FF\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        <\/span><span style=\"color: #8FBCBB\">self<\/span><span style=\"color: #D8DEE9FF\">.<\/span><span style=\"color: #8FBCBB\">threshold<\/span><span style=\"color: #D8DEE9FF\"> = <\/span><span style=\"color: #8FBCBB\">None<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        <\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    <\/span><span style=\"color: #8FBCBB\">def<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">model<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #8FBCBB\">self<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">x<\/span><span style=\"color: #D8DEE9FF\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        # <\/span><span style=\"color: #8FBCBB\">input<\/span><span style=\"color: #D8DEE9FF\">: [1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 0] [<\/span><span style=\"color: #8FBCBB\">x1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">x2<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">xn<\/span><span style=\"color: #D8DEE9FF\">...]<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        <\/span><span style=\"color: #8FBCBB\">z<\/span><span style=\"color: #D8DEE9FF\"> = <\/span><span style=\"color: #8FBCBB\">sum<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #8FBCBB\">x<\/span><span style=\"color: #D8DEE9FF\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        <\/span><span style=\"color: #8FBCBB\">return<\/span><span style=\"color: #D8DEE9FF\"> ( <\/span><span style=\"color: #8FBCBB\">z<\/span><span style=\"color: #D8DEE9FF\"> &gt;= <\/span><span style=\"color: #8FBCBB\">self<\/span><span style=\"color: #D8DEE9FF\">.<\/span><span style=\"color: #8FBCBB\">threshold<\/span><span style=\"color: #D8DEE9FF\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    <\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    <\/span><span style=\"color: #8FBCBB\">def<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">predict<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #8FBCBB\">self<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">X<\/span><span style=\"color: #D8DEE9FF\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        # <\/span><span style=\"color: #8FBCBB\">input<\/span><span style=\"color: #D8DEE9FF\"> : [[1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 0]<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> [1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> 1]]<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        <\/span><span style=\"color: #8FBCBB\">Y<\/span><span style=\"color: #D8DEE9FF\"> = []<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        <\/span><span style=\"color: #8FBCBB\">for<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">x<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">in<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">X<\/span><span style=\"color: #D8DEE9FF\">:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">            <\/span><span style=\"color: #8FBCBB\">result<\/span><span style=\"color: #D8DEE9FF\"> = <\/span><span style=\"color: #8FBCBB\">self<\/span><span style=\"color: #D8DEE9FF\">.<\/span><span style=\"color: #8FBCBB\">model<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #8FBCBB\">x<\/span><span style=\"color: #D8DEE9FF\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">            <\/span><span style=\"color: #8FBCBB\">Y<\/span><span style=\"color: #D8DEE9FF\">.<\/span><span style=\"color: #8FBCBB\">append<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #8FBCBB\">result<\/span><span style=\"color: #D8DEE9FF\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">        <\/span><span style=\"color: #8FBCBB\">return<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #8FBCBB\">np<\/span><span style=\"color: #D8DEE9FF\">.<\/span><span style=\"color: #8FBCBB\">array<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #8FBCBB\">Y<\/span><span style=\"color: #D8DEE9FF\">)<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#2e3440ff\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" style=\"color:#d8dee9ff;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><pre class=\"code-block-pro-copy-button-pre\" aria-hidden=\"true\"><textarea class=\"code-block-pro-copy-button-textarea\" tabindex=\"-1\" aria-hidden=\"true\" readonly># Creamos una instancia de la neurona\nmp_neuron = MPNeuron()\n\n# Establecemos un umbral\nmp_neuron.threshold = 2\n\n# Evaluamos diferentes casos de uso\nmp_neuron.predict([[0, 1, 0, 0], [1, 1, 0, 1], [1, 1, 1, 1]])<\/textarea><\/pre><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki nord\" style=\"background-color: #2e3440ff\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #D8DEE9FF\"># <\/span><span style=\"color: #D8DEE9\">Creamos<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">una<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">instancia<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">de<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">la<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">neurona<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9\">mp_neuron<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #81A1C1\">=<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #88C0D0\">MPNeuron<\/span><span style=\"color: #D8DEE9FF\">()<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\"># <\/span><span style=\"color: #D8DEE9\">Establecemos<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">un<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">umbral<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9\">mp_neuron<\/span><span style=\"color: #ECEFF4\">.<\/span><span style=\"color: #D8DEE9\">threshold<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #81A1C1\">=<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">2<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\"># <\/span><span style=\"color: #D8DEE9\">Evaluamos<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">diferentes<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">casos<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">de<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">uso<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9\">mp_neuron<\/span><span style=\"color: #ECEFF4\">.<\/span><span style=\"color: #88C0D0\">predict<\/span><span style=\"color: #D8DEE9FF\">([[<\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #D8DEE9FF\">]<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> [<\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #D8DEE9FF\">]<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> [<\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #D8DEE9FF\">]])<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Ejemplo practico encuesta de clientes:<\/h2>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#2e3440ff\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" style=\"color:#d8dee9ff;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><pre class=\"code-block-pro-copy-button-pre\" aria-hidden=\"true\"><textarea class=\"code-block-pro-copy-button-textarea\" tabindex=\"-1\" aria-hidden=\"true\" readonly>def mcCulloch_pitts(inputs, threshold):\n    return 1 if sum(inputs) >= threshold else 0<\/textarea><\/pre><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki nord\" style=\"background-color: #2e3440ff\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #D8DEE9\">def<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #88C0D0\">mcCulloch_pitts<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #D8DEE9\">inputs<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">threshold<\/span><span style=\"color: #D8DEE9FF\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    <\/span><span style=\"color: #81A1C1\">return<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">if<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #88C0D0\">sum<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #D8DEE9\">inputs<\/span><span style=\"color: #D8DEE9FF\">) <\/span><span style=\"color: #81A1C1\">&gt;=<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">threshold<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">else<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">0<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#2e3440ff\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" style=\"color:#d8dee9ff;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><pre class=\"code-block-pro-copy-button-pre\" aria-hidden=\"true\"><textarea class=\"code-block-pro-copy-button-textarea\" tabindex=\"-1\" aria-hidden=\"true\" readonly># Datos de la encuesta\nencuestas = [\n    [1, 1, 0],  # Cliente 1\n    [0, 1, 0],  # Cliente 2\n    [1, 1, 1],  # Cliente 3\n    [0, 0, 1],  # Cliente 4\n]<\/textarea><\/pre><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki nord\" style=\"background-color: #2e3440ff\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #D8DEE9FF\"># <\/span><span style=\"color: #D8DEE9\">Datos<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">de<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">la<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">encuesta<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9\">encuestas<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #81A1C1\">=<\/span><span style=\"color: #D8DEE9FF\"> [<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    [<\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #D8DEE9FF\">]<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\">  # <\/span><span style=\"color: #D8DEE9\">Cliente<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    [<\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #D8DEE9FF\">]<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\">  # <\/span><span style=\"color: #D8DEE9\">Cliente<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">2<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    [<\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #D8DEE9FF\">]<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\">  # <\/span><span style=\"color: #D8DEE9\">Cliente<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">3<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    [<\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">0<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #D8DEE9FF\">]<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\">  # <\/span><span style=\"color: #D8DEE9\">Cliente<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">4<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">]<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:.875rem;font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:1.25rem;--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#2e3440ff\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" style=\"color:#d8dee9ff;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><pre class=\"code-block-pro-copy-button-pre\" aria-hidden=\"true\"><textarea class=\"code-block-pro-copy-button-textarea\" tabindex=\"-1\" aria-hidden=\"true\" readonly># Para considerar que el cliente est\u00e1 satisfecho, al menos 2 respuestas deben ser 'S\u00ed' (1).\nthreshold = 2\nfor i, respuestas in enumerate(encuestas, start=1):\n    resultado = mcCulloch_pitts(respuestas, threshold)\n    print(f\"Cliente {i}: {'Satisfecho' if resultado == 1 else 'No satisfecho'}\")<\/textarea><\/pre><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki nord\" style=\"background-color: #2e3440ff\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #D8DEE9FF\"># <\/span><span style=\"color: #D8DEE9\">Para<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">considerar<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">que<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">el<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">cliente<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">est\u00e1<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">satisfecho<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">al<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">menos<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">2<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">respuestas<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">deben<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">ser<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #ECEFF4\">&#39;<\/span><span style=\"color: #A3BE8C\">S\u00ed<\/span><span style=\"color: #ECEFF4\">&#39;<\/span><span style=\"color: #D8DEE9FF\"> (<\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #D8DEE9FF\">)<\/span><span style=\"color: #ECEFF4\">.<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9\">threshold<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #81A1C1\">=<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #B48EAD\">2<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9\">for<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">i<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">respuestas<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #81A1C1\">in<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #88C0D0\">enumerate<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #D8DEE9\">encuestas<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">start<\/span><span style=\"color: #81A1C1\">=<\/span><span style=\"color: #B48EAD\">1<\/span><span style=\"color: #D8DEE9FF\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    <\/span><span style=\"color: #D8DEE9\">resultado<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #81A1C1\">=<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #88C0D0\">mcCulloch_pitts<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #D8DEE9\">respuestas<\/span><span style=\"color: #ECEFF4\">,<\/span><span style=\"color: #D8DEE9FF\"> <\/span><span style=\"color: #D8DEE9\">threshold<\/span><span style=\"color: #D8DEE9FF\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #D8DEE9FF\">    <\/span><span style=\"color: #88C0D0\">print<\/span><span style=\"color: #D8DEE9FF\">(<\/span><span style=\"color: #D8DEE9\">f<\/span><span style=\"color: #ECEFF4\">&quot;<\/span><span style=\"color: #A3BE8C\">Cliente {i}: {&#39;Satisfecho&#39; if resultado == 1 else &#39;No satisfecho&#39;}<\/span><span style=\"color: #ECEFF4\">&quot;<\/span><span style=\"color: #D8DEE9FF\">)<\/span><\/span><\/code><\/pre><\/div>\n\n\n\n<p>La neurona de McCulloch\u2013Pitts es una pieza clave en la historia de la inteligencia artificial. Aunque simple, su concepto de activaci\u00f3n basada en un umbral y entradas binarias sigue siendo la base de modelos neuronales m\u00e1s avanzados que usamos hoy en d\u00eda.<\/p>\n\n\n\n<p><strong>Te dejo el codigo para descargar el notebook:<\/strong><\/p>\n\n\n\n<div class=\"wp-block-file\"><a id=\"wp-block-file--media-08c7a280-8812-4afa-bfb8-f9d36f1d48c5\" href=\"https:\/\/blog.daiconext.com\/wp-content\/uploads\/2025\/08\/Neurona-McCulloch\u2013Pitts.html\">Neurona McCulloch\u2013Pitts<\/a><a href=\"https:\/\/blog.daiconext.com\/wp-content\/uploads\/2025\/08\/Neurona-McCulloch\u2013Pitts.html\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-08c7a280-8812-4afa-bfb8-f9d36f1d48c5\">Descarga<\/a><\/div>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>El origen de las neuronas artificiales \u00bfSab\u00edas que las bases de la inteligencia artificial actual comenzaron con un modelo matem\u00e1tico muy sencillo en los a\u00f1os 40? Hoy te cuento sobre la neurona de McCulloch\u2013Pitts, el primer modelo artificial de neurona, que sent\u00f3 las bases para las redes neuronales modernas. \u00bfQu\u00e9 es la neurona de McCulloch\u2013Pitts? [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2586,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[62],"tags":[],"class_list":["post-2696","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-inteligencia-artificial-ia"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/posts\/2696","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/comments?post=2696"}],"version-history":[{"count":9,"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/posts\/2696\/revisions"}],"predecessor-version":[{"id":2718,"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/posts\/2696\/revisions\/2718"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/media\/2586"}],"wp:attachment":[{"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/media?parent=2696"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/categories?post=2696"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.daiconext.com\/index.php\/wp-json\/wp\/v2\/tags?post=2696"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}