{"id":510,"date":"2019-02-20T20:55:00","date_gmt":"2019-02-20T20:55:00","guid":{"rendered":"http:\/\/54.157.48.44\/2019\/02\/20\/que-son-las-redes-neuronales-biologicas-y-redes-neuronales-artificiales-unidad-logica-de-umbral-perceptron-redes-de-propagacion-y-aplicaciones-de-las-redes-neuronales\/"},"modified":"2019-02-20T20:55:00","modified_gmt":"2019-02-20T20:55:00","slug":"que-son-las-redes-neuronales-biologicas-y-redes-neuronales-artificiales-unidad-logica-de-umbral-perceptron-redes-de-propagacion-y-aplicaciones-de-las-redes-neuronales","status":"publish","type":"post","link":"http:\/\/34.227.63.65\/en\/technology\/what-are-biological-neuronal-networks-and-artificial-neuronal-networks-logical-threshold-unit-perceptron-propagation-networks-and-applications-of-neuronal-networks\/","title":{"rendered":"What are biological neural networks and artificial neural networks? Threshold logic unit, perceptron, propagation networks and applications of neural networks."},"content":{"rendered":"
Antes de comenzar la lectura del blog quiero decirles que gran parte del conocimiento aplicado, incluso las im\u00e1genes, son sacadas de este libro, por si lo quieren adquirir:<\/div>\n
<\/div>\n

Redes neuronales biol\u00f3gicas:<\/b><\/h3>\n
funcionan con procesos neurobiologicos que se establecen relaciones de alta complejidad ya que funcionan con electricidad van intercambiando electricidad de una neurona a otra. <\/div>\n
<\/a><\/div>\n
esta compuesta por el cuerpo de la c\u00e9lula llamada soma y dos tipos de ramificaciones, el Ax\u00f3n y las dendritas, las neuronas reciben impulsos el\u00e9ctricos de otras neuronas a trav\u00e9s de la dendrita, y transmiten se\u00f1ales por el cuerpo de la c\u00e9lula a trav\u00e9s del ax\u00f3n, el cuerpo y las dendritas reciben las se\u00f1ales de entrada y el cuerpo celular las combina e integra y emite las se\u00f1ales de salida. <\/div>\n
\n
<\/div>\n

Redes neuronales artificiales:<\/b> <\/h3>\n
las redes neuronales artificiales emulan una parte del funcionamiento del cerebro humana, as\u00ed que su principal diferencia es que las artificiales s\u00f3lo tratan de imitar el comportamiento de una neurona biol\u00f3gica.<\/div>\n
<\/a><\/div>\n
<\/div>\n
Pueden ser de una sola capa o multicapa, esta es multicapa, tiene unas capas de entrada<\/div>\n
que son las que reciben la informaci\u00f3n del exterior, luego tienen una capa oculta donde se procesan los datos, son las que transforman la entrada que la unidad podr\u00eda usar, luego la capa de salida que son los posibles resultados. <\/div>\n
<\/div>\n

Unidad l\u00f3gica de umbral: <\/h3>\n
Las Ulus son las que comparan la suma ponderada de las entradas con el umbral, si lo supera como resultado da 1 de lo contrario da 0.<\/div>\n
<\/div>\n

Entrenamiento:<\/h4>\n
Una solo ulu es conocida como perceptr\u00f3n, el entrenamiento consiste en ajustar los pesos de variables hasta que se consigan salidas deseadas, adem\u00e1s consiste en minimizar una funci\u00f3n de error, una de las m\u00e1s usadas es la del error cuadr\u00e1tica, necesitamos que sus entradas sean num\u00e9ricas si es 1, significa que el producto escalar es mayor que el umbral si no va ser 0.<\/div>\n

Perceptr\u00f3n <\/h4>\n
Es cada ulu y tiene una linealidad de funcionalidad no binaria que se pueden separar.<\/div>\n
<\/div>\n

EJEMPLO DE UNA RED NEURONAL:<\/h2>\n
<\/div>\n
<\/a><\/div>\n
Tres tipos de capa: Capas de entrada, capas ocultas y capas de salidas.<\/div>\n
<\/div>\n
En las capas de entrada tenemos un paciente medico con sus ciertos valores m\u00e9dicos y en las capas de salida vemos si tiene un riesgo alto de sufrir una enfermedad o riesgo bajo de no tenerla.<\/div>\n
<\/div>\n

Redes de propagaci\u00f3n hacia adelante:<\/h3>\n

<\/p>\n

<\/a><\/div>\n
Son grafos que no poseen bucles que son est\u00e1ticos y s\u00f3lo producen un conjunto de valores como resultado.<\/div>\n

Retroalimentaci\u00f3n:  <\/h3>\n
<\/a><\/div>\n
Estos si poseen bucles debido a conexiones de retroalimentaci\u00f3n<\/div>\n
<\/div>\n

Redes de propagaci\u00f3n hacia atras:<\/h3>\n
<\/a><\/div>\n
<\/div>\n
Se aplica un patron de entrada para la primera capa de la red y se propaga a todas las capas superiores y genera una salida desp\u00faes se calcula el error para cada neurona de salida. <\/div>\n
<\/div>\n

APLICACIONES:<\/h2>\n
Medicina: analisis de celulas portadoras del cancer mamario<\/div>\n
Voz: Asistentes de voz (Siri, Alexa de amazon)<\/div>\n
Seguridad: reconocimiento de huellas digitales<\/div>\n
Bancos: Lecturas de cheques<\/div>\n
Automoviles: sistema de piloto autom\u00e1tico <\/div>\n
<\/div>\n
<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

Antes de comenzar la lectura del blog quiero decirles que gran parte del conocimiento aplicado, incluso las im\u00e1genes, son sacadas de este libro, por si lo quieren adquirir: Redes neuronales biol\u00f3gicas: funcionan con procesos neurobiologicos que se establecen relaciones de alta complejidad ya que funcionan con electricidad van intercambiando electricidad de una neurona a otra.  […]<\/p>","protected":false},"author":3,"featured_media":539,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-510","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tecnologia"],"yoast_head":"\n\u00bfQu\u00e9 son las redes neuronales biol\u00f3gicas y redes neuronales artificiales? 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