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

Parallel Processing01:20

Parallel Processing

227
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
227
Neural Circuits01:25

Neural Circuits

1.6K
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...
1.6K

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

Integrated photonic neural network with on-chip backpropagation training.

Nature·2026
Same author

Adaptive Boosting LLMs for Text Classification.

IEEE transactions on neural networks and learning systems·2026
Same author

DialogueLLM: Context and emotion knowledge-tuned large language models for emotion recognition in conversations.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Bistable random momentum transfer in a linear on-chip resonator.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Modulation-free laser stabilization with aided acquisition for extended locking range.

Optics express·2025
Same author

Generative language reconstruction from brain recordings.

Communications biology·2025

相关实验视频

Updated: Sep 11, 2025

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze
11:15

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

Published on: February 20, 2014

13.2K

数字-模拟混合矩阵乘法处理器用于光学神经网络.

Xiansong Meng1, Deming Kong2, Kwangwoong Kim3

  • 1DTU Electro, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark.

Nature communications
|August 12, 2025
PubMed
概括

光学神经网络 (ONN) 为人工智能实现了更高的计算效率. 一个新的混合光学处理器提供了高数值精度,克服了模拟设计的局限性,以获得更好的AI性能.

更多相关视频

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

12.0K
Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording
06:36

Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording

Published on: September 1, 2022

3.9K

相关实验视频

Last Updated: Sep 11, 2025

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze
11:15

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

Published on: February 20, 2014

13.2K
Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

12.0K
Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording
06:36

Optrode Array for Simultaneous Optogenetic Modulation and Electrical Neural Recording

Published on: September 1, 2022

3.9K

科学领域:

  • 光子学是指光子学的使用方法.
  • 人工智能的人工智能
  • 计算机工程 计算机工程

背景情况:

  • 光学神经网络 (ONN) 为人工智能 (AI) 提供了潜在的计算效率增长.
  • 在ONN中现有的模拟矩阵向量乘法 (MVM) 由于电光处理中的噪声而受到有限的数值精度的影响.
  • 这种精度限制阻碍了ONN在复杂的人工智能任务中的性能.

研究的目的:

  • 为 ONNs 提出并展示一种新的数字-模拟混合MVM架构.
  • 在不损害效率的情况下,在光学计算中实现高数值精度.
  • 为了验证这种混合架构在图像处理和对象检测中的实际应用.

主要方法:

  • 一个概念验证混合光学处理器 (HOP) 的制造.
  • 测试高清图像处理的HOP,评估像素误差率和信号噪声比.
  • 在MNIST数字识别任务中评估准确性.
  • 将HOP应用到你只看一次 (YOLO) 对象检测.

主要成果:

  • 制造出来的HOP实现了16位的数字精度.
  • 在图像处理中显示的像素误差率为1.8 × 10-3在18.2dB的信号噪声比下.
  • 在MNIST数字识别中没有显示精度损失.
  • 证实了数字精度在YOLO中对高可靠性物体检测的关键作用.

结论:

  • 数字模拟混合MVM架构成功提高了ONN的数值精度.
  • 这种方法克服了纯粹模拟光学计算的固有局限性.
  • 混合光学计算概念适用于各种光子MVM实现,为准确的光学AI铺平了道路.