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相关概念视频

Oscillations In An LC Circuit01:30

Oscillations In An LC Circuit

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An idealized LC circuit of zero resistance can oscillate without any source of emf by shifting the energy stored in the circuit between the electric and magnetic fields. In such an LC circuit, if the capacitor contains a charge q before the switch is closed, then all the energy of the circuit is initially stored in the electric field of the capacitor. This energy is given by
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The contraction strength of muscles is regulated by motor neurons, which modulate the frequency of action potentials dispatched to the motor units based on the body's requirements. This process of varying the muscle stimulation frequency allows muscles to contract with a force that is precisely tailored to the needs of the moment, whether lifting a feather or a heavy box.
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...
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相关实验视频

Updated: May 13, 2025

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
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振荡神经网络具有可调节频率,用于以大脑为灵感的神经形态计算.

Ye-Seong Chung1, Seong-Yun Yun1, Joon-Kyu Han2

  • 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Nano letters
|April 16, 2025
PubMed
概括

我们开发了一种基于晶体管的振荡器,具有频率调整性 (SOFT),用于神经形态计算. 这一创新使得使用标准CMOS制造的节能,可扩展的振荡神经网络成为可能.

关键词:
可以调节频率的振荡器.注射锁定系统的注射锁定系统振荡神经网络 (ONN) 是一个神经网络.模板匹配的匹配方式时间信号分类时间信号分类

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相关实验视频

Last Updated: May 13, 2025

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科学领域:

  • 神经形态工程的神经形态工程
  • 固态设备 固态设备
  • 集成电路 集成电路

背景情况:

  • 神经形态计算旨在模仿大脑的结构和功能,以实现高效的计算.
  • 现有的神经形态硬件经常面临可扩展性和能源效率方面的挑战.
  • 振荡神经网络 (ONN) 为大脑启发的计算提供了一个有希望的范式.

研究的目的:

  • 引入一种基于晶体管的新振荡器,具有频率调整性 (SOFT).
  • 展示SOFT设备在实现神经形态计算任务方面的潜力.
  • 通过标准制造工艺实现低成本,高密度的ONN.

主要方法:

  • 使用互补金属氧化物半导体 (CMOS) 技术制造同类的基于单晶体管的振荡器 (1T-O) 和电阻 (1T-R).
  • 在单个晶圆上集成1T-O和1T-R设备,利用它们的结构特征.
  • 使用合的SOFT设备进行模板匹配的演示,并通过多个SOFT使用第一波注入锁定 (FHIL) 进行时间信号分类.

主要成果:

  • 在基于晶体管的振荡器 (SOFT) 中成功实现频率调整.
  • 使用SOFT设备展示模板匹配和时间信号分类.
  • 实现了高密度集成和节能,可扩展的ONN的潜力.

结论:

  • 软件设备为构建节能和可扩展的神经形态计算系统提供了可行的解决方案.
  • 使用标准的CMOS制造使得SOFT技术具有成本效益和易于实施.
  • 这项工作为先进的大脑启发的计算架构铺平了道路.