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

669
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...
669
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

376
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...
376
Neural Circuits01:25

Neural Circuits

2.6K
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...
2.6K
Downsampling01:20

Downsampling

578
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
578
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

332
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
332
Deconvolution01:20

Deconvolution

532
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...
532

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

Object recognition from sparse simulated phosphenes and curved segments.

Vision research·2025
Same author

Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems.

Communications engineering·2025
Same author

Energy-based analog neural network framework.

Frontiers in computational neuroscience·2023
Same author

Reliable detection of causal asymmetries in dynamical systems.

Physical review. E·2023
Same author

Back-Propagation Learning in Deep Spike-By-Spike Networks.

Frontiers in computational neuroscience·2019
Same author

Attention Selectively Gates Afferent Signal Transmission to Area V4.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2018
Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026
Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026
Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026
Same journal

Schumann-anchored golden ratio organization of human neural oscillations.

Frontiers in computational neuroscience·2026
Same journal

Toward model-guided electrophysiology-Encoding of chirps in the electrosensory periphery of <i>Apteronotus leptorhynchus</i>.

Frontiers in computational neuroscience·2026
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.9K

交联皮层-模拟混合改善了深度非负矩阵因子化网络.

Mahbod Nouri1, David Rotermund1, Alberto Garcia-Ortiz2

  • 1Institute for Theoretical Physics, University of Bremen, Bremen, Germany.

Frontiers in computational neuroscience
|November 21, 2025
PubMed
概括
此摘要是机器生成的。

在人工神经网络中整合积极的远程信号和局部相互作用,灵感来自大脑,提高性能. 这种方法在基准任务上超越了传统的深度卷积网络.

关键词:
反向传播错误学习的错误学习卷积神经网络 (CNN) 是一种神经网络.皮层柱子 皮层柱子深度神经网络是深度的神经网络.非负矩阵因子分解 (NMF)

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

相关实验视频

Last Updated: Jan 10, 2026

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.9K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

科学领域:

  • 计算神经科学是一种计算神经科学.
  • 人工智能的人工智能是人工智能.
  • 深度学习是一种深度学习.

背景情况:

  • 生物约束可以提高人工神经网络 (ANN) 的性能.
  • 远程皮质信号的积极性以前没有改善ANN的性能.
  • 非负矩阵因子化 (NMF) 模型是积极的长距离相互作用,但使用NMF模块的深卷积神经网络 (CNN) 的表现不佳.

研究的目的:

  • 调查是否结合积极的远程信号,类似于皮质处理,提高深度神经网络的性能.
  • 确定将NMF的积极活动结合到中间模块中是否会改善传统的CNN.

主要方法:

  • 开发了新的深度卷积神经网络模块,集成非负矩阵分解 (NMF) 原则.
  • 引入了结合积极活动的中间模块,模仿皮质柱处理.
  • 在基准数据集上对标准深卷积网络进行网络性能评估.

主要成果:

  • 拟议的网络架构,包括积极的远程信号和局部交互,显著提高了对基准数据的性能.
  • 性能超过了同等规模的传统深卷积神经网络 (CNN) 的性能.
  • 这些发现表明,模仿ANN中的皮层超列处理有好处.

结论:

  • 整合积极的远程信号与局部交互,灵感来自皮质超柱,增强深度网络性能.
  • 这种生物启发的方法为开发更强大,更有效的深度学习模型提供了有希望的途径.
  • 该研究验证了将特定的生物信号机制纳入人工神经网络的潜力.