Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Electro-mechanical Systems01:19

Electro-mechanical Systems

1.7K
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
1.7K
System of Memory01:23

System of Memory

7.4K
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...
7.4K
Working Memory01:24

Working Memory

905
Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...
905
Long-Term Memory01:18

Long-Term Memory

697
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
697
Traumatic Memory01:20

Traumatic Memory

591
Emotionally traumatic events often lead to memories that are exceptionally vivid and enduring, sometimes persisting with remarkable clarity throughout an individual's life. A classic example of this phenomenon is a person who survives a car accident. Even years later, they may recall every detail of the event with startling accuracy — the screeching of the tires, the jarring impact, and the acrid smell of burning rubber. Such vividness contrasts sharply with how an individual...
591
Repressed Memory01:16

Repressed Memory

526
Repressed memories are a psychological phenomenon where memories of traumatic events are unconsciously blocked from a person's awareness. This process occurs as a defense mechanism, protecting the mind from the emotional impact of distressing or painful experiences. For example, a person who has experienced childhood trauma may grow up with no conscious recollection of the event. In such cases, the memories are thought to be buried deep within the subconscious, inaccessible to the conscious...
526

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Reinforcement learning control of quantum error correction.

Nature·2026
Same author

Optical metasurfaces for general vision processing on the edge.

Nature·2026
Same author

Author Correction: Programmable 200 GOPS Hopfield-inspired photonic Ising machine.

Nature·2026
Same author

Online training and pruning of multi-wavelength photonic neural networks.

Nanophotonics (Berlin, Germany)·2025
Same author

Fully integrated hybrid multimode-multiwavelength photonic processor with picosecond latency.

Nature communications·2025
Same author

Programmable 200 GOPS Hopfield-inspired photonic Ising machine.

Nature·2025

相关实验视频

Updated: Feb 9, 2026

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.2K

带有电光模拟内存的神经形态光子计算.

Sean Lam1, Ahmed Khaled2, Simon Bilodeau3

  • 1Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. seanlm@student.ubc.ca.

Nature communications
|February 7, 2026
PubMed
概括

我们开发了一个与神经形态光子电路集成的模拟电子内存,以降低能源成本. 这项创新可以在机器学习任务中节省超过26倍的电力,从而实现高效,高速的计算.

更多相关视频

Fabrication of 1-D Photonic Crystal Cavity on a Nanofiber Using Femtosecond Laser-induced Ablation
13:02

Fabrication of 1-D Photonic Crystal Cavity on a Nanofiber Using Femtosecond Laser-induced Ablation

Published on: February 25, 2017

10.2K
Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.7K

相关实验视频

Last Updated: Feb 9, 2026

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.2K
Fabrication of 1-D Photonic Crystal Cavity on a Nanofiber Using Femtosecond Laser-induced Ablation
13:02

Fabrication of 1-D Photonic Crystal Cavity on a Nanofiber Using Femtosecond Laser-induced Ablation

Published on: February 25, 2017

10.2K
Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.7K

科学领域:

  • 神经形态工程的神经形态工程
  • 这是光子计算.
  • 机器学习硬件 机器学习硬件

背景情况:

  • 神经形光子系统依赖于模拟信号,需要能源密集的数字对模拟转换器 (DAC) 和模拟对数字转换器 (ADC).
  • 传统的·诺伊曼架构由于存储器和转换器之间的数据移动而面临重大能源成本.

研究的目的:

  • 提出和演示一个与光子计算单元直接集成的模拟电子内存.
  • 为了消除耗费能源的数据移动,减少对神经形态光子系统中DAC/ADC的依赖.

主要方法:

  • 一个神经形态光子电路与芯片上的电容模拟内存的单立体集成.
  • 使用机器学习进行现场培训和在MNIST数据集上推断的性能评估.

主要成果:

  • 与传统的SRAM-DAC架构相比,实现了超过26倍的节能.
  • 演示了>90%的推断准确度,最小模拟内存保留与网络延迟比率为100.
  • 展示了漏洞模拟内存的可行性,而没有显著的性能降低.

结论:

  • 将模拟内存集成到神经形态光子架构中,为节能,高速计算提供了一个可扩展的途径.
  • 这种方法大大减少了数据的移动和对转换器的依赖.
  • 为高级AI任务实现神经形态光子系统的实际实现.