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相关概念视频

Introduction to R01:11

Introduction to R

270
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
270
Interpreting R Charts01:22

Interpreting R Charts

67
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
67
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

558
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
558

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Dynamic population coding and its relationship to working memory.

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Differential Processing of Isolated Object and Multi-item Pop-Out Displays in LIP and PFC.

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Intelligent information loss: the coding of facial identity, head pose, and non-face information in the macaque face patch system.

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The dynamics of invariant object recognition in the human visual system.

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The neural decoding toolbox.

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Incorporation of new information into prefrontal cortical activity after learning working memory tasks.

Proceedings of the National Academy of Sciences of the United States of America·2012
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Predicting vasovagal syncope during head-up tilt test: three machine learning approaches.

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Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.

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FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.

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A 3D-printed phantom to validate subject orientation in 3D imaging and recordings.

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IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.

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Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.

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相关实验视频

Updated: Jul 5, 2025

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

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NeuroDecodeR:一个用于R中神经解码的包.

Ethan M Meyers1,2,3

  • 1Department of Statistics and Data Science, Yale University, New Haven, CT, United States.

Frontiers in neuroinformatics
|January 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个R包,简化了研究人员的神经解码分析. 该包方便对神经活动进行可重现的分析,加速神经科学发现.

关键词:
在这个过程中,R是R.数据分析数据分析数据分析数据科学数据科学机器学习是机器学习.多变量模式分析多变量模式分析神经解码的神经解码读取的读取结果统计 统计 统计 统计 统计

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Author Spotlight: Understanding Processing of Olfactory and Spatial Information by Brain with Real-Time Behavioral Analysis
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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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.
  • 生物信息学是一种生物信息学.

背景情况:

  • 神经解码对于分析大脑活动至关重要.
  • 复杂的编码要求可能会阻碍采用神经解码方法.
  • 需要采用简化方法,以扩大对这些分析的获取.

研究的目的:

  • 引入一个用户友好的R包用于神经解码分析.
  • 降低执行解码分析的研究人员的进入障碍.
  • 提高神经科学研究的可复制性和效率.

主要方法:

  • 开发了一个用于神经解码的模块化R包.
  • 提供了与该包兼容的数据格式的指导方针.
  • 包括实践示例,用现实数据展示包使用情况.

主要成果:

  • R包简化了各种解码分析的实施.
  • 模块化设计允许灵活和定制的分析管道.
  • 通过两个真实数据分析示例证明了易用性.

结论:

  • R包显著降低了神经解码的技术障碍.
  • 与R的生态系统的整合促进了可重复的研究.
  • 预计通过可访问的数据分析加速神经科学发现的步伐.