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Storage01:23

Storage

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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System of Memory01:23

System of Memory

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Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
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Understanding Memory01:19

Understanding Memory

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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Neuroplasticity01:01

Neuroplasticity

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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Computación híbrida utilizando una red neuronal con memoria externa dinámica

Alex Graves1, Greg Wayne1, Malcolm Reynolds1

  • 1Google DeepMind, 5 New Street Square, London EC4A 3TW, UK.

Nature
|October 13, 2016
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo modelo de computadora neuronal diferenciable (DNC) integra una red neuronal con memoria externa, lo que permite la manipulación y el aprendizaje de datos complejos. Este avance de la IA supera las limitaciones de las redes neuronales tradicionales en el razonamiento estructurado y el almacenamiento de datos a largo plazo.

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Área de la Ciencia:

  • Inteligencia artificial
  • Aprendizaje automático
  • Ciencias de la computación

Sus antecedentes:

  • Las redes neuronales artificiales sobresalen en el procesamiento sensorial y de secuencias, pero luchan con estructuras de datos complejas y memoria a largo plazo debido a la falta de memoria externa.
  • Los modelos de redes neuronales existentes están limitados en tareas que requieren representación variable y manipulación de datos durante períodos prolongados.

Objetivo del estudio:

  • Introducir un nuevo modelo de aprendizaje automático, el ordenador neural diferenciable (DNC), capaz de interactuar con la memoria externa.
  • Demostrar la capacidad del DNC para aprender y realizar tareas complejas de razonamiento y estructuradas aprovechando sus capacidades de memoria externa.

Principales métodos:

  • Desarrolló un modelo de computadora neuronal diferenciable (DNC) que combina una red neuronal con una matriz de memoria externa de lectura y escritura.
  • Entrenó el DNC utilizando el aprendizaje supervisado para tareas de razonamiento e inferencia, y el aprendizaje por refuerzo para tareas de secuencia orientadas a objetivos.
  • Evaluó el DNC en problemas basados en gráficos sintéticos y del mundo real y un rompecabezas impulsado por secuencias simbólicas.

Principales resultados:

  • El DNC respondió con éxito preguntas sintéticas imitando el razonamiento y la inferencia del lenguaje natural.
  • Aprendizaje demostrado de la búsqueda de la ruta más corta y la inferencia de enlaces gráficos, generalizando a las redes de transporte y los árboles genealógicos.
  • Completó con éxito un rompecabezas de bloques móviles con objetivos cambiantes especificados por secuencias de símbolos.

Conclusiones:

  • Las computadoras neuronales diferenciables (DNC) cierran la brecha entre las redes neuronales y las computadoras convencionales al incorporar memoria externa.
  • Las DNC muestran la capacidad de resolver tareas complejas y estructuradas anteriormente inaccesibles para las redes neuronales estándar.
  • Este avance abre nuevas posibilidades para la IA en áreas que requieren razonamiento sofisticado, manipulación de datos y memoria a largo plazo.