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

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
Neurons as Communicators of the Brain01:22

Neurons as Communicators of the Brain

2.8K
Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
Cell Body
The cell body, also known...
2.8K
Neuron Structure01:30

Neuron Structure

17.6K
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...
17.6K
Neuron Structure01:31

Neuron Structure

230.4K
Overview
230.4K
Neurons: The Axon01:21

Neurons: The Axon

6.8K
Axons are long, cytoplasmic processes of nerve cells capable of propagating electrical impulses known as action potentials. The cytoplasm or axoplasm of an axon contains neurofibrils, neurotubules, small vesicles, lysosomes, mitochondria, and various enzymes, all encased within the axolemma, the plasma membrane of the axon.
The axon attaches to the cell body at a cone-shaped elevation called the axon hillock. The initial part of the axon, closest to the hillock, is known as the initial segment....
6.8K
Neuroplasticity01:01

Neuroplasticity

1.5K
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
1.5K

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

Modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms.

eLife·2026
Same author

Distinct CA1 inputs support shifts in neural dimensionality and memory resolution.

bioRxiv : the preprint server for biology·2026
Same author

Metabolite-responsive scaffold RNAs for dynamic CRISPR transcriptional regulation.

Nucleic acids research·2025
Same author

Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution.

Advances in neural information processing systems·2025
Same author

Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites.

Cerebral cortex (New York, N.Y. : 1991)·2025
Same author

Neural Encoding and Decoding at Scale.

ArXiv·2025
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026
查看所有相关文章

相关实验视频

Updated: Jan 9, 2026

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

7.3K

通过了解他人来了解自己:从人口背景中学习神经元身份

Vinam Arora1, Divyansha Lachi1, Ian J Knight1

  • 1University of Pennsylvania.

ArXiv
|December 11, 2025
PubMed
概括
此摘要是机器生成的。

一个新的自我监督框架NuCLR从神经活动中学习神经元表征,以识别单个神经元. 这种方法实现了对细胞类型和大脑区域的最先进解码,在动物中实现了泛化.

更多相关视频

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

556
Studying the Integration of Adult-born Neurons
09:00

Studying the Integration of Adult-born Neurons

Published on: March 25, 2011

14.2K

相关实验视频

Last Updated: Jan 9, 2026

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

7.3K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

556
Studying the Integration of Adult-born Neurons
09:00

Studying the Integration of Adult-born Neurons

Published on: March 25, 2011

14.2K

科学领域:

  • 计算神经科学是一种神经科学.
  • 机器学习用于神经科学

背景情况:

  • 从神经活动中推断神经元身份 (细胞类型,连接性,大脑区域) 是一个挑战.
  • 需要通用表示来解码神经元特定的信息.

研究的目的:

  • 介绍NuCLR,这是一个自我监督的框架,用于学习神经元表示.
  • 能够使单个神经元与其活动模式的差异化.

主要方法:

  • NuCLR使用不同时间/刺激对神经元活动的对比学习.
  • 一个时空变压器整合了人口上下文的等同变换.
  • 根据电生理学和成像数据集进行评估.

主要成果:

  • 实现了最先进的细胞类型和大脑区域解码.
  • 向未见的动物展示了强大的零射击概括.
  • 显示的性能随着更多的预训练动物而提高,并且具有标签效率.

结论:

  • 庞大而多样化的神经数据集使模型能够学习可概括的神经元身份表示.
  • NuCLR推进了神经元级别的表示学习和解码能力.
  • 该框架为神经科学数据提供了标签有效的学习.