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

3.0K
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...
3.0K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

441
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
441
State Space Representation01:27

State Space Representation

630
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
630
Functional Brain Systems: Reticular Formation01:13

Functional Brain Systems: Reticular Formation

5.5K
The reticular formation is a complex network of gray and white matter located within the brainstem extending from the medulla to the midbrain.
Within the reticular formation, there are several distinct nuclei that can be classified into three broad categories. The Raphe nuclei are located along the midline of the brainstem. They are primarily known for their role in synthesizing and releasing serotonin, a neurotransmitter involved in regulating mood, appetite, sleep, and circadian rhythms. The...
5.5K

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

Observation of Anisotropic Magnetoresistance in Layered Nonmagnetic Semiconducting PdSe<sub>2</sub>.

ACS applied materials & interfaces·2021
Same author

Reopening International Borders without Quarantine: Contact Tracing Integrated Policy against COVID-19.

International journal of environmental research and public health·2021
Same author

Do the positioning variables of the cage contribute to adjacent facet joint degeneration? Radiological and clinical analysis following intervertebral fusion.

Annals of translational medicine·2021
Same author

HID: The Hybrid Image Decomposition Model for MRI and CT Fusion.

IEEE journal of biomedical and health informatics·2021
Same author

Genetically Predicted Cigarette Smoking in Relation to Risk of Polycystic Ovary Syndrome.

Clinical epidemiology·2021
Same author

Assessing the Country-Level Excess All-Cause Mortality and the Impacts of Air Pollution and Human Activity during the COVID-19 Epidemic.

International journal of environmental research and public health·2021

相关实验视频

Updated: Feb 28, 2026

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

10.9K

一个用于大脑计算的合网络:E-I在双吸引力动态系统中平衡嵌入.

Shunmin Yao1, Ziyang Wang1, Rui Zhu1

  • 1Shanghai University of Electric Power, Shanghai, 201306, China.

Brain research bulletin
|February 26, 2026
PubMed
概括
此摘要是机器生成的。

连续吸引神经网络 (CANNs) 模拟大脑功能. 这项研究表明,神经网络中的动态激发-抑制平衡如何提高信息表示的准确性和稳定性,这表明了协作网络活动.

关键词:
共存是一种共存.连续吸引器的吸引力.在 EI 余额中保持平衡.多网络交互的互动.

更多相关视频

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.9K
3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.7K

相关实验视频

Last Updated: Feb 28, 2026

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

10.9K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.9K
3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.7K

科学领域:

  • 计算神经科学是一种神经科学.
  • 神经网络建模神经网络建模
  • 由大脑启发的计算

背景情况:

  • 连续吸引神经网络 (CANN) 对于模拟大脑中的连续变量表示至关重要.
  • 真实的大脑网络表现出复杂的,非随机的神经元连接和相互作用.
  • 了解多神经元组相互作用是解读复杂大脑网络行为的关键.

研究的目的:

  • 提出基于CANN的选择性合网络模型,用于研究复杂的大脑网络.
  • 为了研究在快速激发-抑制 (E-I) 平衡下两个CANN类之间的相互作用,用于运动方向识别.
  • 探索复杂网络相互作用在大脑信息处理中的作用.

主要方法:

  • 开发了一种选择性合网络模型,集成连续吸引神经网络 (CANNs).
  • 在动态E-I平衡下,具有不同选择偏好的两个CANN之间的模拟相互作用.
  • 使用理论分析与模拟结果一起解释网络动态.

主要成果:

  • 快速的E-I平衡促进了多个神经网络之间的间接链接效应.
  • 偶联网络中的间接相互抑制可以提高特定位置的响应准确性和稳定性.
  • 证明了E-I平衡可以调解不同神经网络之间的协作活动.

结论:

  • 大脑中的动态E-I平衡使神经网络之间的间接合和协调成为可能.
  • 这种机制提高了神经表示的稳定性和准确性.
  • 结果为开发类似大脑的计算系统的多网络合提供了洞察力.