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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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

Type I hair cells of striolar and central zones in vestibular organs are essential for head stability and postural control.

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

Partitioning Neural Co-Variability.

ArXiv·2026
Same author

Eye-head coordination during goal-directed orienting in mice.

bioRxiv : the preprint server for biology·2026
Same author

Bat eye movements resolve a long-standing question in gaze control.

bioRxiv : the preprint server for biology·2026
Same author

Eye-head coordination during goal-directed orienting in mice.

Communications biology·2026
Same author

Bat eye movements resolve a long-standing question in gaze control.

Current biology : CB·2026
Same journal

Genetic Impacts on Variability of Body Fat Distribution Uncover Gene-Environment and Gene-Gene Interactions.

bioRxiv : the preprint server for biology·2026
Same journal

16S ribosomal RNA modification drives transcript-specific translation efficiency.

bioRxiv : the preprint server for biology·2026
Same journal

FlcE latches onto the FliL-stator complex to turbocharge flagellar motility in <i>Borrelia burgdorferi</i>.

bioRxiv : the preprint server for biology·2026
Same journal

Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.

bioRxiv : the preprint server for biology·2026
Same journal

Structural and functional insights into the Rcs phosphorelay.

bioRxiv : the preprint server for biology·2026
Same journal

The structural basis of RanGAP1 regulation and catalysis in nuclear transport.

bioRxiv : the preprint server for biology·2026
查看所有相关文章

相关实验视频

Updated: Jun 27, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

11.5K

CUSP:从多电极阵列记录中进行复杂的尖峰分类,使用U-net序列对序列预测.

Chenhao Bao1, Robyn Mildren1, Adam S Charles1,2

  • 1Dept. of Biomedical Engineering, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21205, USA.

bioRxiv : the preprint server for biology
|December 3, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了CUSP,这是一个自动化的深度学习工具,用于准确检测小脑神经元中的复杂尖峰 (CSs). 这种方法超越了现有的算法,为更广泛的神经科学应用提供了神经活动的强大分析.

关键词:
复杂的尖峰尖峰是一个复杂的尖峰.深度学习是一种深度学习.多电极阵列多电极阵列.尖刺分类 分类.

更多相关视频

Author Spotlight: Optimizing Micro-Drive Systems for Precise Electrode Positioning in Marmoset
07:37

Author Spotlight: Optimizing Micro-Drive Systems for Precise Electrode Positioning in Marmoset

Published on: August 4, 2023

1.3K
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

5.7K

相关实验视频

Last Updated: Jun 27, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'
10:31

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

11.5K
Author Spotlight: Optimizing Micro-Drive Systems for Precise Electrode Positioning in Marmoset
07:37

Author Spotlight: Optimizing Micro-Drive Systems for Precise Electrode Positioning in Marmoset

Published on: August 4, 2023

1.3K
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

5.7K

科学领域:

  • 神经科学是一个神经科学.
  • 计算神经科学是一种神经科学.
  • 机器学习 机器学习

背景情况:

  • 小脑普金日细胞中的复杂尖峰 (CSs) 是关键的神经信号,但由于它们的变化性和频率不高,难以检测.
  • 自动检测CSs受到波形变异性,低峰数和记录器件 (如电极漂移) 的阻碍.

研究的目的:

  • 引入CUSP (通过U-net序列预测进行CS排序),这是一种用于自动化复杂尖端排序的新型深度学习框架.
  • 为了使高密度神经记录中复杂的尖峰活动能够准确和稳定地检测和分析.

主要方法:

  • CUSP使用U-Net架构与混合自我注意力启动块,用于对CS事件的序列对序列预测.
  • 该框架整合了当地现场潜力和行动潜力信号,以提高检测准确度.
  • 检测到的CS事件被聚类并与简单的尖峰 (SSs) 配对,以重建普尔金尼细胞活动.

主要成果:

  • 在从大脑录音中检测复杂的尖峰时,CUSP实现了人类专家的性能 (F1 = 0.83 ± 0.03).
  • 深度学习框架成功地识别了在手动注释过程中错过的有效CS事件.
  • 与传统和最先进的算法相比,CUSP表现优越,在CS检测方面表现优于它们.

结论:

  • CUSP提供了一个可扩展和强大的解决方案,用于分析大脑小脑和其他神经数据集中复杂的尖峰模式.
  • 该框架对波形变化和电极漂移的稳定性使得可以准确地长期跟踪神经活动.
  • 通过将专家级准确性与自动化相结合,CUSP为研究神经信息编码提供了一个广泛适用的工具.