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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.
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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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
125
Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
478
Neural Regulation01:37

Neural Regulation

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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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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
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用于模式识别的振荡神经网络学习:在芯片上的学习视角和实现

Madeleine Abernot1, Nadine Azemard1, Aida Todri-Sanial1,2

  • 1Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Department of Microelectroncis, University of Montpellier, CNRS, Montpellier, France.

Frontiers in neuroscience
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概括

本研究介绍了使用振荡神经网络 (ONN) 的AI芯片上的持续学习. 它在数字ONN设计中使用Hebbian和Storkey规则展示了高效的无监督学习.

关键词:
在FPGA实施过程中.在芯片上学习振荡神经网络是一种神经网络.模式识别 模式识别 模式识别没有监督的学习学习.

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科学领域:

  • 神经形态计算是一种神经形态计算.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 人类大脑的学习是持续的,与当前人工智能的预训练,非进化模型形成鲜明对比.
  • 人工智能模型面临着不断变化的环境和数据,需要持续学习算法.
  • 直接在芯片上实施持续学习是一个重大挑战.

研究的目的:

  • 调查在芯片上的持续学习算法的实现.
  • 将霍普菲尔德神经网络 (HNN) 的无监督学习规则调整为振荡神经网络 (ONN).
  • 提出和验证数字ONN设计,用于无监督的芯片上学习.

主要方法:

  • 专注于振荡神经网络 (ONN) 作为神经形态计算范式.
  • 研究了HNN无监督学习规则 (Hebbian和Storkey) 对ONN的适应性.
  • 开发了一个数字ONN架构,用于芯片上实现.

主要成果:

  • 证明了拟议数字ONN架构的有效的芯片上学习能力.
  • 成功实施了使用Hebbian和Storkey规则的无监督学习.
  • 达到了数百微秒的学习时间,用于多达35个数字振荡器的网络.

结论:

  • 拟议的数字ONN设计可以实现有效的芯片上的持续学习.
  • 这项工作为在神经形态系统中实施无监督学习提供了一个可行的解决方案.
  • 这些发现为更具适应性和进化的AI模型铺平了道路.