{"id":34634,"date":"2025-11-26T13:20:25","date_gmt":"2025-11-26T12:20:25","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=34634"},"modified":"2025-11-26T13:20:27","modified_gmt":"2025-11-26T12:20:27","slug":"rete-neurale-lispirazione-biologica","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/","title":{"rendered":"Rete Neurale: l&#8217;ispirazione biologica"},"content":{"rendered":"\n<p>Spicca il volo verso il futuro dell&#8217;Intelligenza Artificiale! Preparati a capire da zero come le reti neurali artificiali (RNA) siano il cuore dell&#8217;intelligenza artificiale moderna e come queste strutture ispirate al cervello umano stiano rivoluzionando il mondo, permettendo alle macchine di apprendere pattern complessi a partire dai dati. Dal riconoscimento di immagini ai modelli di linguaggio come Transformers, tutto fa parte di questo concetto rivoluzionario.<\/p>\n\n\n\n<p><strong>Che cos&#8217;\u00e8 una rete neurale artificiale e come funziona?<\/strong><\/p>\n\n\n\n<p>Immagina il cervello umano con i suoi miliardi di neuroni interconnessi, che lavorano in parallelo per elaborare informazioni, apprendere e prendere decisioni. Una Rete Neurale Artificiale (RNA) \u00e8 un modello computazionale che imita questa architettura biologica.<\/p>\n\n\n\n<p>Non \u00e8 un cervello, ma un algoritmo di Machine Learning (Apprendimento Automatico) che impara a eseguire compiti (come riconoscere immagini o tradurre lingue) analizzando grandi quantit\u00e0 di dati e trovando pattern da s\u00e9, senza la necessit\u00e0 di essere programmato esplicitamente per ogni regola.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Una rete neurale \u00e8 un modello matematico composto da strati di nodi (<a href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/el-perceptron-redes-neuronales-la-primera-piedra\/\">neuroni artificiali<\/a>).<\/li>\n\n\n\n<li>Ogni neurone riceve input, li moltiplica per dei pesi, somma un bias (polarizzazione) e applica una funzione di attivazione.<\/li>\n\n\n\n<li>L&#8217;obiettivo: approssimare funzioni che trasformino input in output utili.<\/li>\n<\/ul>\n\n\n\n<p>Formula base di un neurone<br><\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"198\" height=\"115\" src=\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/image-6.png\" alt=\"\" class=\"wp-image-34637\" style=\"width:245px;height:auto\"\/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Componenti chiave di una rete neurale artificiale<\/strong><\/p>\n\n\n\n<p>Una RNA \u00e8 costruita con tre elementi fondamentali:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Neuroni (o nodi):<\/strong> Sono le unit\u00e0 di elaborazione di base, organizzate in strati. Ogni neurone riceve input, esegue un semplice calcolo e produce un output.<\/li>\n\n\n\n<li><strong>Connessioni e pesi:<\/strong> Ogni connessione tra neuroni ha un valore numerico chiamato peso. Questo peso determina la forza o l&#8217;importanza dell&#8217;input. Un neurone somma i suoi input moltiplicati per i rispettivi pesi, e a tale somma aggiunge un valore costante chiamato bias (polarizzazione).<\/li>\n\n\n\n<li><strong>Funzione di attivazione:<\/strong> Il risultato della somma ponderata passa attraverso una funzione matematica che decide se il neurone debba &#8220;attivarsi&#8221; (trasmettere un segnale allo strato successivo) e con quale intensit\u00e0. Ci\u00f2 introduce la non linearit\u00e0 essenziale per apprendere pattern complessi.<\/li>\n<\/ol>\n\n\n\n<p><strong>La struttura a strati e il flusso di informazioni<\/strong><\/p>\n\n\n\n<p>Una rete neurale tipica \u00e8 organizzata in almeno tre strati:<\/p>\n\n\n\n<p><em>1. Strato di Input (Input Layer):<\/em> Riceve l&#8217;informazione iniziale. Ad esempio, se la rete deve riconoscere un&#8217;immagine, lo strato di input avr\u00e0 un nodo per ogni pixel dell&#8217;immagine.<\/p>\n\n\n\n<p><em>2. Strati Nascosti (Hidden Layers):<\/em> Qui avviene la magia dell&#8217;elaborazione. Gli strati nascosti eseguono tutti i calcoli intermedi, estraendo caratteristiche e pattern complessi dai dati.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Una rete \u00e8 considerata di Apprendimento Profondo (Deep Learning) quando ha almeno due o pi\u00f9 strati nascosti, consentendole di gestire problemi di complessit\u00e0 estrema.<\/li>\n<\/ul>\n\n\n\n<p><em>3. Strato di Output (Output Layer):<\/em> Produce il risultato finale della rete. Se il compito \u00e8 classificare un&#8217;immagine come &#8220;cane&#8221; o &#8220;gatto&#8221;, lo strato di output avr\u00e0 due nodi, e quello con il valore pi\u00f9 alto sar\u00e0 la previsione.<\/p>\n\n\n\n<p><strong>Il segreto dell&#8217;apprendimento: propagazione e aggiustamento<\/strong><\/p>\n\n\n\n<p>Come impara la rete neurale? Attraverso un processo iterativo di tentativi ed errori in due fasi principali, utilizzando dati di addestramento etichettati:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em>Propagazione in avanti (forward propagation)<\/em><\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li>I dati di addestramento entrano attraverso lo strato di input.<\/li>\n\n\n\n<li>L&#8217;informazione fluisce attraverso gli strati, nodo per nodo, fino allo strato di output.<\/li>\n\n\n\n<li>La rete produce una previsione.<\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li><em>Retropropagazione (backpropagation) e ottimizzazione<\/em><\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Calcolo dell&#8217;Errore:<\/strong> Si utilizza una funzione di perdita (o costo) per misurare la differenza o l&#8217;errore tra la previsione della rete e il risultato reale atteso (l&#8217;etichetta corretta).<\/li>\n\n\n\n<li><strong>Retropropagazione:<\/strong> L&#8217;errore si propaga all&#8217;indietro attraverso la rete (dallo strato di output agli strati nascosti e di input).<\/li>\n\n\n\n<li><strong>Aggiustamento dei Pesi:<\/strong> Utilizzando il calcolo dell&#8217;errore (attraverso un processo chiamato Discesa del Gradiente), la rete aggiusta i pesi e i bias di tutte le connessioni in modo che, nell&#8217;iterazione successiva, l&#8217;errore sia minore.<\/li>\n<\/ol>\n\n\n\n<p>Questo ciclo di Propagazione \u2192 Errore \u2192 Aggiustamento dei Pesi si ripete migliaia o milioni di volte (chiamate epoche) fino a quando l&#8217;errore \u00e8 minimo e la rete ha <em>imparato<\/em> la relazione tra input e output con alta precisione.<\/p>\n\n\n\n<p><strong>Tipi chiave di reti e le loro applicazioni pi\u00f9 importanti<\/strong><\/p>\n\n\n\n<p>Non tutte le<a href=\"https:\/\/www.codemotion.com\/magazine\/es\/inteligencia-artificial\/deep-learning-y-redes-neuronales-una-guia-completa\/\"> reti neurali<\/a> sono uguali. Le loro architetture variano per adattarsi meglio a diversi tipi di dati e problemi.<\/p>\n\n\n\n<p><em>Esempio pratico da zero (Python + NumPy)<\/em><\/p>\n\n\n\n<p>Per prima cosa, costruiamo una rete neurale manuale per classificare dati semplici:<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php\">import numpy <span class=\"hljs-keyword\">as<\/span> np\n\n<span class=\"hljs-comment\"># Dati di esempio (XOR problem)<\/span>\nX = np.<span class=\"hljs-keyword\">array<\/span>(&#91;&#91;<span class=\"hljs-number\">0<\/span>,<span class=\"hljs-number\">0<\/span>],&#91;<span class=\"hljs-number\">0<\/span>,<span class=\"hljs-number\">1<\/span>],&#91;<span class=\"hljs-number\">1<\/span>,<span class=\"hljs-number\">0<\/span>],&#91;<span class=\"hljs-number\">1<\/span>,<span class=\"hljs-number\">1<\/span>]])\ny = np.<span class=\"hljs-keyword\">array<\/span>(&#91;&#91;<span class=\"hljs-number\">0<\/span>],&#91;<span class=\"hljs-number\">1<\/span>],&#91;<span class=\"hljs-number\">1<\/span>],&#91;<span class=\"hljs-number\">0<\/span>]])\n\n<span class=\"hljs-comment\"># Inizializzazione dei pesi<\/span>\nnp.random.seed(<span class=\"hljs-number\">42<\/span>)\nW1 = np.random.randn(<span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">2<\/span>)   <span class=\"hljs-comment\"># pesi strato nascosto<\/span>\nb1 = np.zeros((<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>))\nW2 = np.random.randn(<span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">1<\/span>)   <span class=\"hljs-comment\"># pesi strato di output<\/span>\nb2 = np.zeros((<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>))\n\n<span class=\"hljs-comment\"># Funzione di attivazione (sigmoid)<\/span>\ndef sigmoid(z):\n    <span class=\"hljs-keyword\">return<\/span> <span class=\"hljs-number\">1<\/span> \/ (<span class=\"hljs-number\">1<\/span> + np.exp(-z))\n\n<span class=\"hljs-comment\"># Addestramento semplice<\/span>\nlr = <span class=\"hljs-number\">0.1<\/span>\n<span class=\"hljs-keyword\">for<\/span> epoch in range(<span class=\"hljs-number\">10000<\/span>):\n    <span class=\"hljs-comment\"># Forward<\/span>\n    z1 = X.dot(W1) + b1\n    a1 = sigmoid(z1)\n    z2 = a1.dot(W2) + b2\n    a2 = sigmoid(z2)\n\n    <span class=\"hljs-comment\"># Backpropagation<\/span>\n    error = y - a2\n    d2 = error * a2 * (<span class=\"hljs-number\">1<\/span> - a2)\n    d1 = d2.dot(W2.T) * a1 * (<span class=\"hljs-number\">1<\/span> - a1)\n\n    <span class=\"hljs-comment\"># Aggiornamento dei pesi<\/span>\n    W2 += a1.T.dot(d2) * lr\n    b2 += np.sum(d2, axis=<span class=\"hljs-number\">0<\/span>, keepdims=<span class=\"hljs-keyword\">True<\/span>) * lr\n    W1 += X.T.dot(d1) * lr\n    b1 += np.sum(d1, axis=<span class=\"hljs-number\">0<\/span>, keepdims=<span class=\"hljs-keyword\">True<\/span>) * lr\n\n<span class=\"hljs-comment\"># Previsioni<\/span>\n<span class=\"hljs-keyword\">print<\/span>(<span class=\"hljs-string\">\"Previsioni finali:\"<\/span>)\n<span class=\"hljs-keyword\">print<\/span>(a2.round())<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p>Questo codice addestra una rete neurale da zero per risolvere il classico problema <em>XOR<\/em>, che \u00e8 un esempio fondamentale nell&#8217;apprendimento automatico e nell&#8217;intelligenza artificiale che mette in evidenza le limitazioni dei modelli lineari.<\/p>\n\n\n\n<p><em>Esempio con librerie moderne (PyTorch)<\/em><\/p>\n\n\n\n<p>Ora, la stessa idea ma con PyTorch, molto pi\u00f9 pratico:<\/p>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs\">import torch\n\nimport torch.nn as nn\n\nimport torch.optim as optim\n\n# Dati XOR\n\nX = torch.tensor(&#91;&#91;0,0],&#91;0,1],&#91;1,0],&#91;1,1]], dtype=torch.float32)\n\ny = torch.tensor(&#91;&#91;0],&#91;1],&#91;1],&#91;0]], dtype=torch.float32)\n\n# Definizione della rete\n\nclass XORNet(nn.Module):\n\n    def __init__(self):\n\n        super(XORNet, self).__init__()\n\n        self.hidden = nn.Linear(2, 2)\n\n        self.output = nn.Linear(2, 1)\n\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n\n        x = self.sigmoid(self.hidden(x))\n\n        x = self.sigmoid(self.output(x))\n\n        return x\n\n# Addestramento\n\nmodel = XORNet()\n\ncriterion = nn.MSELoss()\n\noptimizer = optim.SGD(model.parameters(), lr=0.1)\n\nfor epoch in range(10000):\n\n    optimizer.zero_grad()\n\n    outputs = model(X)\n\n    loss = criterion(outputs, y)\n\n    loss.backward()\n\n    optimizer.step()\n\nprint(\"Previsioni finali:\")\n\nprint(model(X).round().detach())\n<\/code><\/span><\/pre>\n\n\n<p><strong>Applicazioni reali delle reti neurali artificiali<\/strong><\/p>\n\n\n\n<p>Le RNA sono il motore dell&#8217;attuale rivoluzione dell&#8217;IA, guidando innovazioni in quasi tutti i settori:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Medicina:<\/strong> Rilevazione precoce di malattie (tumori, retinopatie) analizzando immagini mediche con maggiore velocit\u00e0 e precisione rispetto a un occhio umano.<\/li>\n\n\n\n<li><strong>Finanza:<\/strong> Rilevazione di frodi in tempo reale analizzando pattern di transazioni atipiche e modelli di rischio predittivo.<\/li>\n\n\n\n<li><strong>Tecnologia:<\/strong> Motori di raccomandazione di Netflix o Amazon, assistenti virtuali come Siri e Alexa e sistemi di traduzione automatica come Google Translate.<\/li>\n\n\n\n<li><strong>Robotica e Automazione:<\/strong> Controllo di robot industriali e la presa di decisioni in veicoli autonomi.<\/li>\n<\/ul>\n\n\n\n<p>La Rete Neurale non \u00e8 magia, \u00e8 matematica!<\/p>\n\n\n\n<p>\u00c8 il modo in cui riusciamo a far s\u00ec che una macchina impari dall&#8217;esperienza a un livello di complessit\u00e0 senza precedenti, avvicinandoci sempre di pi\u00f9 a replicare l&#8217;incredibile capacit\u00e0 dell&#8217;intelligenza naturale. Comprendere i suoi fondamenti \u00e8 comprendere il pilastro dell&#8217;IA moderna. Il futuro \u00e8 qui, ed \u00e8 connesso!<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"800\" src=\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png\" alt=\"\" class=\"wp-image-34429\" srcset=\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png 800w, https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-300x300.png 300w, https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-150x150.png 150w, https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-768x768.png 768w, https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-100x100.png 100w, https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-600x600.png 600w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Spicca il volo verso il futuro dell&#8217;Intelligenza Artificiale! Preparati a capire da zero come le reti neurali artificiali (RNA) siano il cuore dell&#8217;intelligenza artificiale moderna e come queste strutture ispirate al cervello umano stiano rivoluzionando il mondo, permettendo alle macchine di apprendere pattern complessi a partire dai dati. Dal riconoscimento di immagini ai modelli di&#8230; <a class=\"more-link\" href=\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/\">Read more<\/a><\/p>\n","protected":false},"author":238,"featured_media":34429,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[10281],"tags":[10317],"collections":[],"class_list":{"0":"post-34634","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-deep-learning-it","8":"tag-ai-it","9":"entry"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v26.9) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Rete neurale: l&#039;ispizarione biologica<\/title>\n<meta name=\"description\" content=\"Scopri come funziona una rete neurale artificiale partendo da zero, con esempi di codice e applicazioni reali nell\u2019IA moderna.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Rete Neurale: l&#039;ispirazione biologica\" \/>\n<meta property=\"og:description\" content=\"Scopri come funziona una rete neurale artificiale partendo da zero, con esempi di codice e applicazioni reali nell\u2019IA moderna.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/\" \/>\n<meta property=\"og:site_name\" content=\"Codemotion Magazine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/Codemotion.Italy\/\" \/>\n<meta property=\"article:published_time\" content=\"2025-11-26T12:20:25+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-26T12:20:27+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"800\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Arnaldo Morena\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@CodemotionIT\" \/>\n<meta name=\"twitter:site\" content=\"@CodemotionIT\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Arnaldo Morena\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/\"},\"author\":{\"name\":\"Arnaldo Morena\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#\/schema\/person\/72209dcaf2205f28968d38489892bd17\"},\"headline\":\"Rete Neurale: l&#8217;ispirazione biologica\",\"datePublished\":\"2025-11-26T12:20:25+00:00\",\"dateModified\":\"2025-11-26T12:20:27+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/\"},\"wordCount\":874,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png\",\"keywords\":[\"AI\"],\"articleSection\":[\"deep learning\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/\",\"url\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/\",\"name\":\"Rete neurale: l'ispizarione biologica\",\"isPartOf\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png\",\"datePublished\":\"2025-11-26T12:20:25+00:00\",\"dateModified\":\"2025-11-26T12:20:27+00:00\",\"description\":\"Scopri come funziona una rete neurale artificiale partendo da zero, con esempi di codice e applicazioni reali nell\u2019IA moderna.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#primaryimage\",\"url\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png\",\"contentUrl\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png\",\"width\":800,\"height\":800},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.codemotion.com\/magazine\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Intelligenza artificiale\",\"item\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"deep learning\",\"item\":\"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Rete Neurale: l&#8217;ispirazione biologica\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#website\",\"url\":\"https:\/\/www.codemotion.com\/magazine\/\",\"name\":\"Codemotion Magazine\",\"description\":\"We code the future. Together\",\"publisher\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.codemotion.com\/magazine\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#organization\",\"name\":\"Codemotion\",\"url\":\"https:\/\/www.codemotion.com\/magazine\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2019\/11\/codemotionlogo.png\",\"contentUrl\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2019\/11\/codemotionlogo.png\",\"width\":225,\"height\":225,\"caption\":\"Codemotion\"},\"image\":{\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/Codemotion.Italy\/\",\"https:\/\/x.com\/CodemotionIT\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#\/schema\/person\/72209dcaf2205f28968d38489892bd17\",\"name\":\"Arnaldo Morena\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.codemotion.com\/magazine\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2024\/01\/whatsapp-image-100x100.jpg\",\"contentUrl\":\"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2024\/01\/whatsapp-image-100x100.jpg\",\"caption\":\"Arnaldo Morena\"},\"description\":\"First steps i moved into computers world were my beloved basic programs I wrote on a Zx Spectrum in early 80s. In 90s , while i was studing economic , i was often asked to help people on using personal computer for every day business : It's been a one way ticket. First and lasting love was for managing data , so i have started using msaccess and SqlServer to build databases , elaborate information and reports using tons and tons of Visual Basic code . My web career started developing in Asp and Asp.net , then I began to use php . I like to have an administrative approach ,too .In fact i have earned many certifications on database administration . Mixing up this two factors i developed many programs for data collecting and analyzing, being involved on publishing reports and articles based on elaborated information , in scenarios as Public Administration training , collaboration project between universities all over the world or survey on genetic structure and their relative kind of analysis. Actually i am involved in collecting data by using automated sensor IoT, that lead me on joining Arduino community in Rome, and integrating my application with more instruments , working in fields like Open and Big data , and using data mining software .\",\"sameAs\":[\"https:\/\/www.linkedin.com\/in\/arnymore\/\"],\"url\":\"https:\/\/www.codemotion.com\/magazine\/author\/arnaldo-morena\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Rete neurale: l'ispizarione biologica","description":"Scopri come funziona una rete neurale artificiale partendo da zero, con esempi di codice e applicazioni reali nell\u2019IA moderna.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/","og_locale":"en_US","og_type":"article","og_title":"Rete Neurale: l'ispirazione biologica","og_description":"Scopri come funziona una rete neurale artificiale partendo da zero, con esempi di codice e applicazioni reali nell\u2019IA moderna.","og_url":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/","og_site_name":"Codemotion Magazine","article_publisher":"https:\/\/www.facebook.com\/Codemotion.Italy\/","article_published_time":"2025-11-26T12:20:25+00:00","article_modified_time":"2025-11-26T12:20:27+00:00","og_image":[{"width":800,"height":800,"url":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png","type":"image\/png"}],"author":"Arnaldo Morena","twitter_card":"summary_large_image","twitter_creator":"@CodemotionIT","twitter_site":"@CodemotionIT","twitter_misc":{"Written by":"Arnaldo Morena","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#article","isPartOf":{"@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/"},"author":{"name":"Arnaldo Morena","@id":"https:\/\/www.codemotion.com\/magazine\/#\/schema\/person\/72209dcaf2205f28968d38489892bd17"},"headline":"Rete Neurale: l&#8217;ispirazione biologica","datePublished":"2025-11-26T12:20:25+00:00","dateModified":"2025-11-26T12:20:27+00:00","mainEntityOfPage":{"@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/"},"wordCount":874,"commentCount":0,"publisher":{"@id":"https:\/\/www.codemotion.com\/magazine\/#organization"},"image":{"@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#primaryimage"},"thumbnailUrl":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png","keywords":["AI"],"articleSection":["deep learning"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/","url":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/","name":"Rete neurale: l'ispizarione biologica","isPartOf":{"@id":"https:\/\/www.codemotion.com\/magazine\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#primaryimage"},"image":{"@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#primaryimage"},"thumbnailUrl":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png","datePublished":"2025-11-26T12:20:25+00:00","dateModified":"2025-11-26T12:20:27+00:00","description":"Scopri come funziona una rete neurale artificiale partendo da zero, con esempi di codice e applicazioni reali nell\u2019IA moderna.","breadcrumb":{"@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#primaryimage","url":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png","contentUrl":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png","width":800,"height":800},{"@type":"BreadcrumbList","@id":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/rete-neurale-lispirazione-biologica\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.codemotion.com\/magazine\/"},{"@type":"ListItem","position":2,"name":"Intelligenza artificiale","item":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/"},{"@type":"ListItem","position":3,"name":"deep learning","item":"https:\/\/www.codemotion.com\/magazine\/it\/intelligenza-artificiale\/deep-learning-it\/"},{"@type":"ListItem","position":4,"name":"Rete Neurale: l&#8217;ispirazione biologica"}]},{"@type":"WebSite","@id":"https:\/\/www.codemotion.com\/magazine\/#website","url":"https:\/\/www.codemotion.com\/magazine\/","name":"Codemotion Magazine","description":"We code the future. Together","publisher":{"@id":"https:\/\/www.codemotion.com\/magazine\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.codemotion.com\/magazine\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.codemotion.com\/magazine\/#organization","name":"Codemotion","url":"https:\/\/www.codemotion.com\/magazine\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.codemotion.com\/magazine\/#\/schema\/logo\/image\/","url":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2019\/11\/codemotionlogo.png","contentUrl":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2019\/11\/codemotionlogo.png","width":225,"height":225,"caption":"Codemotion"},"image":{"@id":"https:\/\/www.codemotion.com\/magazine\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/Codemotion.Italy\/","https:\/\/x.com\/CodemotionIT"]},{"@type":"Person","@id":"https:\/\/www.codemotion.com\/magazine\/#\/schema\/person\/72209dcaf2205f28968d38489892bd17","name":"Arnaldo Morena","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.codemotion.com\/magazine\/#\/schema\/person\/image\/","url":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2024\/01\/whatsapp-image-100x100.jpg","contentUrl":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2024\/01\/whatsapp-image-100x100.jpg","caption":"Arnaldo Morena"},"description":"First steps i moved into computers world were my beloved basic programs I wrote on a Zx Spectrum in early 80s. In 90s , while i was studing economic , i was often asked to help people on using personal computer for every day business : It's been a one way ticket. First and lasting love was for managing data , so i have started using msaccess and SqlServer to build databases , elaborate information and reports using tons and tons of Visual Basic code . My web career started developing in Asp and Asp.net , then I began to use php . I like to have an administrative approach ,too .In fact i have earned many certifications on database administration . Mixing up this two factors i developed many programs for data collecting and analyzing, being involved on publishing reports and articles based on elaborated information , in scenarios as Public Administration training , collaboration project between universities all over the world or survey on genetic structure and their relative kind of analysis. Actually i am involved in collecting data by using automated sensor IoT, that lead me on joining Arduino community in Rome, and integrating my application with more instruments , working in fields like Open and Big data , and using data mining software .","sameAs":["https:\/\/www.linkedin.com\/in\/arnymore\/"],"url":"https:\/\/www.codemotion.com\/magazine\/author\/arnaldo-morena\/"}]}},"featured_image_src":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-600x400.png","featured_image_src_square":"https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-600x600.png","author_info":{"display_name":"Arnaldo Morena","author_link":"https:\/\/www.codemotion.com\/magazine\/author\/arnaldo-morena\/"},"uagb_featured_image_src":{"full":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png",800,800,false],"thumbnail":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-150x150.png",150,150,true],"medium":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-300x300.png",300,300,true],"medium_large":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-768x768.png",768,768,true],"large":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png",800,800,false],"1536x1536":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png",800,800,false],"2048x2048":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw.png",800,800,false],"small-home-featured":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-100x100.png",100,100,true],"sidebar-featured":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-180x128.png",180,128,true],"genesis-singular-images":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-800x504.png",800,504,true],"archive-featured":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-400x225.png",400,225,true],"gb-block-post-grid-landscape":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-600x400.png",600,400,true],"gb-block-post-grid-square":["https:\/\/www.codemotion.com\/magazine\/wp-content\/uploads\/2025\/11\/1CjrjrAcJUT8Ux31Fd4qvcw-600x600.png",600,600,true]},"uagb_author_info":{"display_name":"Arnaldo Morena","author_link":"https:\/\/www.codemotion.com\/magazine\/author\/arnaldo-morena\/"},"uagb_comment_info":0,"uagb_excerpt":"Spicca il volo verso il futuro dell&#8217;Intelligenza Artificiale! Preparati a capire da zero come le reti neurali artificiali (RNA) siano il cuore dell&#8217;intelligenza artificiale moderna e come queste strutture ispirate al cervello umano stiano rivoluzionando il mondo, permettendo alle macchine di apprendere pattern complessi a partire dai dati. Dal riconoscimento di immagini ai modelli di&#8230;&hellip;","lang":"it","_links":{"self":[{"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/posts\/34634","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/users\/238"}],"replies":[{"embeddable":true,"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/comments?post=34634"}],"version-history":[{"count":2,"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/posts\/34634\/revisions"}],"predecessor-version":[{"id":34651,"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/posts\/34634\/revisions\/34651"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/media\/34429"}],"wp:attachment":[{"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/media?parent=34634"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/categories?post=34634"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/tags?post=34634"},{"taxonomy":"collections","embeddable":true,"href":"https:\/\/www.codemotion.com\/magazine\/wp-json\/wp\/v2\/collections?post=34634"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}