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

Neuron Structure01:31

Neuron Structure

196.6K
Overview
196.6K
Neuron Structure01:30

Neuron Structure

18.1K
Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
Structure and Function of Neurons
The neuronal cell body—the soma— houses the nucleus and organelles vital to...
18.1K
Neural Circuits01:25

Neural Circuits

3.0K
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...
3.0K
Storage01:23

Storage

532
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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

Aversive Learning Induces Context-Gated Global Reorganization of Neural Dynamics in <i>Caenorhabditis elegans</i>.

bioRxiv : the preprint server for biology·2025
Same author

Network modularity reveals context and state-dependent reorganization of time-varying functional connectivity in single-cell resolved neural activity recordings.

bioRxiv : the preprint server for biology·2025
Same author

Automated cell annotation in multi-cell images using an improved CRF_ID algorithm.

eLife·2025
Same author

Action at a Distance: Theoretical Mechanisms of Cross-Dendritic Heterosynaptic Modification.

eNeuro·2023
Same author

Pathogenic bacteria modulate pheromone response to promote mating.

Nature·2023
Same author

Forgetting generates a novel state that is reactivatable.

Science advances·2022
Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026
Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026
Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026
Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026
Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

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

相关实验视频

Updated: May 2, 2026

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools
10:41

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Published on: December 16, 2015

9.3K

CeDNe:一个多尺度的计算框架,用于在C中建模结构-功能关系. 优雅的神经系统.

Sahil Moza1, Yun Zhang1

  • 1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.

bioRxiv : the preprint server for biology
|November 24, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了CeDNe,这是一个计算框架,集成了各种数据集来分析神经电路. 这种工具将网络结构与功能连接起来,进步了我们对C. elegans及其他类型的大脑动态的理解.

更多相关视频

Isotropic Light-Sheet Microscopy and Automated Cell Lineage Analyses to Catalogue Caenorhabditis elegans Embryogenesis with Subcellular Resolution
08:16

Isotropic Light-Sheet Microscopy and Automated Cell Lineage Analyses to Catalogue Caenorhabditis elegans Embryogenesis with Subcellular Resolution

Published on: June 6, 2019

8.7K
Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans
08:47

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

Published on: July 5, 2019

10.3K

相关实验视频

Last Updated: May 2, 2026

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools
10:41

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Published on: December 16, 2015

9.3K
Isotropic Light-Sheet Microscopy and Automated Cell Lineage Analyses to Catalogue Caenorhabditis elegans Embryogenesis with Subcellular Resolution
08:16

Isotropic Light-Sheet Microscopy and Automated Cell Lineage Analyses to Catalogue Caenorhabditis elegans Embryogenesis with Subcellular Resolution

Published on: June 6, 2019

8.7K
Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans
08:47

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

Published on: July 5, 2019

10.3K

科学领域:

  • 神经科学是一个神经科学.
  • 计算生物学 计算生物学
  • 系统生物学 系统生物学

背景情况:

  • 了解神经回路需要整合多层次数据.
  • 线虫C. elegans由于其完整的连接体和丰富的分子数据,提供了一个独特的模型系统.
  • 需要一个统一的框架来连接结构和功能神经数据.

研究的目的:

  • 介绍CeDNe,一个开源的计算框架.
  • 通过整合解剖学,分子和成像数据集来实现多模式数据分析.
  • 弥合神经网络结构和功能之间的差距.

主要方法:

  • 开发了CeDNe,一个基于图形的计算框架.
  • 集成多种数据集,包括连接学,转录学和神经活动.
  • 实现了用于网络分析和神经动力学模拟的模块化工具.

主要成果:

  • CeDNe提供了一个统一的环境,用于交叉引用omics层.
  • 启用了网络连接,图案和路径的可视化和分析.
  • 促进神经动态的模拟和网络模型的优化.

结论:

  • CeDNe为数据驱动的神经系统建模建立了一个可扩展的基础.
  • 该框架促进了C. elegans的计算连接经济学和多式联络分析.
  • CeDNe 作为一种可通用的工具,用于研究其他生物体中神经结构-功能关系.