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

Aggregates Classification01:29

Aggregates Classification

387
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
387
Cluster Sampling Method01:20

Cluster Sampling Method

12.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.8K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.1K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.1K
Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

530
Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
530

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

Association of metabolic syndrome and its components with systemic lupus erythematosus.

BMC endocrine disorders·2026
Same author

Genetic control of dynamic brain network reconfiguration during working memory.

PloS one·2026
Same author

Exposure to pesticides and renal function in agricultural workers in Rafsanjan, Iran: a case-control study.

Journal of health, population, and nutrition·2025
Same author

Behavioral relevance of category selectivity revealed by human ECoG data.

PloS one·2025
Same author

Neuron-type-specific Contributions to Dynamic Coding during Flexible Sensorimotor Decisions in Frontoparietal Cortex.

Journal of cognitive neuroscience·2025
Same author

Microstate Dynamics in Working Memory: Exploring Spatial Information Coding of Stimulus and Behavioral Performance.

Brain and behavior·2025
Same journal

Korean Red Ginseng Attenuates Dysfunctions and Modulates Inflammatory and Neuroplasticity Markers in the Harmaline-Induced Model of Essential Tremor.

Brain and behavior·2026
Same journal

Pseudohallucination and Pilocytic Astrocytoma in the Pons.

Brain and behavior·2026
Same journal

Volume Alterations in Thalamic Subnuclei in Parkinson's Disease Dementia and Machine Learning-Based Prediction of Diagnosis and Severity.

Brain and behavior·2026
Same journal

ESTELA-Study: Long-Term Effectiveness and Safety of Anti-Calcitonin Gene-Related Peptide Monoclonal Antibodies in Real-World Clinical Practice.

Brain and behavior·2026
Same journal

The "Brain's Traffic Map" Reveals Neural Pathways Linked to Coronary Microvascular Dysfunction in Women.

Brain and behavior·2026
Same journal

Psychedelic Therapy and the Role of Music: A Scoping Review of Quantitative Evidence on Subjective and Objective Outcomes.

Brain and behavior·2026
查看所有相关文章

相关实验视频

Updated: Sep 17, 2025

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

11.2K

通过梯度式预处理和非线性减少通过聚合集群的非线性减少来推进尖端分类.

Mohammad Amin Lotfi1, Fatemeh Zareayan Jahromy1, Mohammad Reza Daliri1

  • 1Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.

Brain and behavior
|July 3, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的无监督数学方法,用于准确的尖端分类,改进神经数据分析. 这种新方法实现了高精度,超过了分类神经信号的现有方法.

关键词:
最优的特点是最优的功能.频谱嵌入是指光谱嵌入尖刺分类 分类.统一的多元体近似和投影 (UMAP)

更多相关视频

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

相关实验视频

Last Updated: Sep 17, 2025

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

11.2K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

科学领域:

  • 计算神经科学是一种神经科学.
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 尖端分类对于分析神经活动至关重要,但目前的方法缺乏足够的准确性.
  • 手动的尖峰分类是耗时且低效的,特别是对于视觉上相似的尖峰.
  • 需要高度准确的,自动化的尖端分类技术.

研究的目的:

  • 开发一种具有高分类准确度的全自动化尖端分类方法.
  • 通过先进的尖峰分类来提高神经数据分析的可靠性.

主要方法:

  • 采用无监督的数学方法进行尖端分类,避免需要训练数据并降低计算成本.
  • 实施了两步方法:数据预处理和尖峰分类.
  • 利用非线性转换,包括统一多重近似和投影 (UMAP) 和光谱嵌入,以从尖峰波形中获得最佳特征提取,然后进行基于密度的聚类.

主要成果:

  • 在数据集1.1.上实现了非重叠尖峰的100%准确性和重叠尖峰的99.47%准确性.
  • 在具有挑战性的数据集部分显示了12%的准确性改进.
  • 在合成数据上的单元检测和尖峰集群中展示了有效性.

结论:

  • 拟议的方法在尖端分类中实现了无与伦比的准确性.
  • 这种方法超过了当前最先进的尖端分类技术的性能.