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and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 634px"/><img alt="" class="bh hf hg c" width="634" height="392" loading="eager" role="presentation"/></picture></div></div></figure><div><h1 id="9ca2" class="pw-post-title hh hi hj bf hk hl hm hn ho hp hq hr hs ht hu hv hw hx hy hz ia ib ic id ie if ig ih ii ij bk" data-testid="storyTitle">NEURON IN NEURAL NETWORK(biological neuron)</h1><div><div class="speechify-ignore ab cp"><div class="speechify-ignore bh l"><div class="ik il im in io ab"><div><div class="ab ip"><div><div class="bm" aria-hidden="false"><a rel="noopener follow" 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aria-label="Listen" data-testid="audioPlayButton" class="af fk ah ai aj ak al mc an ao ap ex md me ls mf mg mh mi mj s mk ml mm mn mo mp mq u mr ms mt"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" fill-rule="evenodd" d="M3 12a9 9 0 1 1 18 0 9 9 0 0 1-18 0m9-10C6.477 2 2 6.477 2 12s4.477 10 10 10 10-4.477 10-10S17.523 2 12 2m3.376 10.416-4.599 3.066a.5.5 0 0 1-.777-.416V8.934a.5.5 0 0 1 .777-.416l4.599 3.066a.5.5 0 0 1 0 .832" clip-rule="evenodd"></path></svg><div class="j i d"><p class="bf b bg z du">Listen</p></div></button></div></div></div></div></div></div></div></div><div class="bm" aria-hidden="false" aria-describedby="postFooterSocialMenu" aria-labelledby="postFooterSocialMenu"><div><div class="bm" aria-hidden="false"><button aria-controls="postFooterSocialMenu" aria-expanded="false" aria-label="Share Post" data-testid="headerSocialShareButton" class="af fk ah ai aj ak al mc an ao ap ex md me ls mf mg mh mi mj s mk ml mm mn mo mp mq u mr ms mt"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" fill="none" viewBox="0 0 24 24"><path fill="currentColor" fill-rule="evenodd" d="M15.218 4.931a.4.4 0 0 1-.118.132l.012.006a.45.45 0 0 1-.292.074.5.5 0 0 1-.3-.13l-2.02-2.02v7.07c0 .28-.23.5-.5.5s-.5-.22-.5-.5v-7.04l-2 2a.45.45 0 0 1-.57.04h-.02a.4.4 0 0 1-.16-.3.4.4 0 0 1 .1-.32l2.8-2.8a.5.5 0 0 1 .7 0l2.8 2.79a.42.42 0 0 1 .068.498m-.106.138.008.004v-.01zM16 7.063h1.5a2 2 0 0 1 2 2v10a2 2 0 0 1-2 2h-11c-1.1 0-2-.9-2-2v-10a2 2 0 0 1 2-2H8a.5.5 0 0 1 .35.15.5.5 0 0 1 .15.35.5.5 0 0 1-.15.35.5.5 0 0 1-.35.15H6.4c-.5 0-.9.4-.9.9v10.2a.9.9 0 0 0 .9.9h11.2c.5 0 .9-.4.9-.9v-10.2c0-.5-.4-.9-.9-.9H16a.5.5 0 0 1 0-1" clip-rule="evenodd"></path></svg><div class="j i d"><p class="bf b bg z du">Share</p></div></button></div></div></div></div></div></div></div></div></div><p id="0ff2" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">neurons are main key to learn any thing whether is related to brain learning and to learn machine learning algorithm</p><p id="c377" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">BRAIN LEARNING</p><ul class=""><li id="6e83" class="mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr ns nt nu bk">> BIOLOGICAL NEURON</li></ul><figure class="nw nx ny nz oa ha gs gt paragraph-image"><div class="gs gt nv"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 1100w, https://miro.medium.com/v2/resize:fit:694/format:webp/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 694w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 347px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 1100w, https://miro.medium.com/v2/resize:fit:694/1*IYAB2bj45GwHlgUOwYlj-Q.jpeg 694w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 347px"/><img alt="" class="bh hf hg c" width="347" height="145" loading="lazy" role="presentation"/></picture></div></figure><p id="49dd" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">A cell body or soma One or more dendrites, whose responsibility is to receive signals from other neurons An axon, which in turn conveys the signals generated by the same neuron to the other connected neurons</p><p id="7a5e" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk"><strong class="mw hk">EXAMPLE OF BRAIN LEARN and MACHINE LEARN IN IMAGE RECOGNIZATION</strong></p><p id="9456" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">The visual cortex, which is intended to solve image recognition problems, shows a sequence of sectors placed in a hierarchy. Each of these areas receives an input representation, by means of flow signals that connect it to other sectors.</p><p id="8873" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">Each level of this hierarchy represents a different level of abstraction, with the most abstract features defined in terms of those of the lower level. At a time when the brain receives an input image, the processing goes through various phases, for example, detection of the edges or the perception of forms (from those primitive to those gradually more and more complex). As the brain learns by trial and activates new neurons by learning from the experience, even in deep learning architectures, the extraction stages or layers are changed based on the information received at the input. The scheme, on the next page shows what has been said in the case of an image classification system, each block gradually extracts the features of the input image, going on to process data already preprocessed from the previous blocks, extracting features of the image that are increasingly abstract, and thus building the hierarchical representation of data that comes with on deep learning based system.</p><p id="fbfe" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">More precisely, it builds the layers as follows along with the figure representation:</p><p id="8807" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">Layer 1: The system starts identifying the dark and light pixels</p><p id="7554" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">Layer 2: The system identifies edges and shapes</p><p id="200a" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">Layer 3: The system learns more complex shapes and objects</p><p id="237f" class="pw-post-body-paragraph mu mv hj mw b mx my mz na nb nc nd ne nf ng nh ni nj nk nl nm nn no np nq nr gn bk">Layer 4: The system learns which objects define a human face</p><figure class="nw nx ny nz oa ha gs gt paragraph-image"><div class="gs gt ob"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 1100w, https://miro.medium.com/v2/resize:fit:640/format:webp/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 640w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 320px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 1100w, https://miro.medium.com/v2/resize:fit:640/1*j6rOXb_OOdwcH3eOwcSEUw.jpeg 640w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 320px"/><img alt="" class="bh hf hg c" width="320" height="320" loading="lazy" role="presentation"/></picture></div></figure></div></div></div></div></section></div></div></article></div><div class="ab 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The scheme, on the next page shows what has been said in the case of an image classification system, each block gradually extracts the features of the input image, going on to process data already preprocessed from the previous blocks, extracting features of the image that are increasingly abstract, and thus building the hierarchical representation of data that comes with on deep learning based system.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:96aa5e19fd7_10":{"__typename":"Paragraph","id":"96aa5e19fd7_10","name":"fbfe","type":"P","href":null,"layout":null,"metadata":null,"text":"More precisely, it builds the layers as follows along with the figure 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