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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

682
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
682
Echo01:06

Echo

880
The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...
880
State Space Representation01:27

State Space Representation

519
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...
519
Deconvolution01:20

Deconvolution

537
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
537

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

Online supervised learning of temporal patterns in biological neural networks under feedback control.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Construction of an enterprise-level global supply chain database.

Nature communications·2025
Same author

Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modeling <i>in vitro</i> neurons.

Frontiers in neuroscience·2025
Same author

Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing.

Frontiers in artificial intelligence·2024
Same author

Dynamical predictive coding with reservoir computing performs noise-robust multi-sensory speech recognition.

Frontiers in computational neuroscience·2024
Same author

Computation harvesting from nature dynamics for predicting wind speed and direction.

PloS one·2023

相关实验视频

Updated: Jan 13, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

2.0K

在回声状态网络中进行无监督学习,用于输入重建.

Taiki Yamada1, Yuichi Katori2, Kantaro Fujiwara3

  • 1Graduate School of Information Science and Technology, University of Tokyo, Bunkyo, Tokyo 113-0033, Japan yamada-taiki@g.ecc.u-tokyo.ac.jp.

Neural computation
|October 29, 2025
PubMed
概括

反响状态网络 (ESN) 可以使用无监督学习 (UL) 重建输入时间序列,而无目标输出. 这利用了已知的ESN参数,减少了对噪音过等任务的监督的依赖.

更多相关视频

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.4K
Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning
08:43

Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning

Published on: October 22, 2015

10.7K

相关实验视频

Last Updated: Jan 13, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

2.0K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.4K
Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning
08:43

Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning

Published on: October 22, 2015

10.7K

科学领域:

  • 计算神经科学是一种神经科学.
  • 机器学习 机器学习
  • 经常性的神经网络.

背景情况:

  • 反响状态网络 (ESN) 是具有固定的输入/反复层和可训练的读取层的循环神经网络.
  • 在计算效率高的时间序列数据处理方面,ESN非常出色.
  • 传统的ESN培训依赖于有目标产出的监督学习.

研究的目的:

  • 研究ESN中输入重建 (IR) 的无监督学习 (UL).
  • 通过利用已知的ESN参数来证明,在没有监管目标的情况下,IR是可以实现的.
  • 探索基于UL的IR在动态系统复制和噪声过中的应用.

主要方法:

  • 制定输入重建作为无监督学习问题.
  • 使用已知的先验ESN参数,满足可逆性条件.
  • 为ESN输入重建开发基于UL的算法.

主要成果:

  • 在ESN中输入重建可以通过无监督学习实现.
  • 已知的ESN参数,当可逆时,可以实现基于UL的IR,而无需监督目标.
  • 基于UL的IR算法适用于自主处理和动态系统复制.

结论:

  • 预先了解ESN参数可以显著减少对监督学习的需求.
  • 利用固定网络参数的特定值为ESN设计提供了一个新的原则.
  • 基于UL的ESN为潜在的大脑计算机制提供了洞察力,并推进了计算神经科学模型.