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相关概念视频

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.
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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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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Neurulation01:30

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Neurulation is the embryological process which forms the precursors of the central nervous system and occurs after gastrulation has established the three primary cell layers of the embryo: ectoderm, mesoderm, and endoderm. In humans, the majority of this system is formed via primary neurulation, in which the central portion of the ectoderm—originally appearing as a flat sheet of cells—folds upwards and inwards, sealing off to form a hollow neural tube. As development proceeds, the...
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下行增长的神经网络

Vincenzo Laveglia1, Edmondo Trentin2

  • 1DINFO, Università di Firenze, Via di S. Marta 3, 50139 Firenze, Italy.

Entropy (Basel, Switzerland)
|May 27, 2023
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概括
此摘要是机器生成的。

本研究介绍了一个向下增长的神经网络 (DGNN),以优化深度学习架构. 通过智能增长网络,DGNN提高了模型准确性,防止过度匹配和增强学习能力.

关键词:
适应式架构是适应式架构.深度学习是一种深度学习.深度神经网络是一个神经网络.一个不断增长的神经网络目标传播的传播目标.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 定义最佳的深度学习架构是具有挑战性的,平衡模型大小以防止过拟合或不足拟合.
  • 已开发出自动化架构增长和修剪算法来应对这一挑战.

研究的目的:

  • 为自动增长深度神经网络架构引入一种新的方法,称为向下增长的神经网络 (DGNN).
  • 通过在训练期间优化深度神经网络的架构来增强深度神经网络的学习和概括能力.

主要方法:

  • DGNN适用于前深度神经网络.
  • 通过临时目标传播识别和生长负面影响表现的神经元组.
  • 同时增加网络深度和宽度.

主要成果:

  • 在UCI数据集上,DGNN在平均准确度方面取得了显著的改进.
  • 超过了既有的深度神经网络方法和现有的增长算法,如AdaNet和级联相关神经网络.

结论:

  • DGNN提供了一种优化深度神经网络架构的有效方法.
  • 这种方法成功地提高了模型性能和概括能力.