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

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
Neural Regulation01:37

Neural Regulation

40.3K
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.
40.3K

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

Reconfigurable Photoelectric Coaxial Fiber-Based Memristors for Neuromorphic Computing.

ACS nano·2026
Same author

Ultralow Power Optoelectronic Reconfigurable Hf<sub>0.2</sub>Zr<sub>0.8</sub>O<sub><i>x</i></sub>-Based Antiferroelectric Device for Adaptive Image Recognition.

ACS applied materials & interfaces·2026
Same author

Accelerating spiking neural networks with photonic reconfigurable devices.

Nature communications·2026
Same author

Investigation of the formation mechanism of the oxidized soybean protein/linoleic acid aggregates: Effect of heating treatment on the soybean protein emulsion structure, antioxidant properties and accessibility of vitamin D<sub>3</sub>.

International journal of biological macromolecules·2026
Same author

One-Step Annealing-Configured Hf<sub>0.2</sub>Zr<sub>0.8</sub>O<sub>2</sub> Memristive-Antiferroelectric Devices for Bioinspired CSNN Neuromorphic Computing.

Nano letters·2025
Same author

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications.

Nano-micro letters·2025

相关实验视频

Updated: Sep 16, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

7.9K

基于memristor的人工神经网络用于硬件神经形态计算

Boyan Jin1, Zhenlong Wang1, Tianyu Wang1,2,3,4

  • 1School of Integrated Circuits, Shandong University, Jinan 250101, China.

Research (Washington, D.C.)
|July 8, 2025
PubMed
概括

记忆器为人工智能 (AI) 硬件中的·诺伊曼瓶提供了解决方案. 这些新型设备通过整合内存和处理来实现高效的低功耗神经网络,用于先进的AI计算.

更多相关视频

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.1K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

相关实验视频

Last Updated: Sep 16, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

7.9K
A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.1K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.4K

科学领域:

  • 神经科学和计算机工程 神经科学和计算机工程
  • 材料科学和设备物理学 材料科学和设备物理学

背景情况:

  • 人工神经网络 (ANN) 旨在复制人类大脑对AI的认知.
  • 传统硬件面临着·诺伊曼瓶,将内存和处理分开.
  • 记忆器,非易失性记忆器,其电阻取决于历史,提供了一个潜在的解决方案.

研究的目的:

  • 审查将memristors集成到神经网络架构中的情况.
  • 探索memristor原理及其在内存计算中的应用.
  • 确定基于memristor的AI的挑战和未来方向.

主要方法:

  • 神经网络进化和memristor技术的全面审查.
  • 对模仿生物突触的记忆装置原理的分析.
  • 讨论各种神经网络模型 (CNN,RNN,Spiking) 与memristor集成.

主要成果:

  • 记忆器使内存计算和并行处理成为可能,克服了·诺伊曼瓶.
  • 记忆器件支持突触可塑性机制,这对学习至关重要.
  • 记忆器的集成可以导致更高效和可扩展的AI系统.

结论:

  • 基于memristor的神经网络承诺在低功耗的情况下提供高计算性能.
  • 材料,设备工程和系统集成方面的进步至关重要.
  • 这些系统弥合了下一代人工智能生物和电子信息处理之间的差距.