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相关概念视频

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.6K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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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
Propagation of Action Potentials01:23

Propagation of Action Potentials

8.7K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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Action Potential01:14

Action Potential

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Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
10.5K
Neuron Structure01:30

Neuron Structure

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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...
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Neuron Structure01:31

Neuron Structure

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Overview
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相关实验视频

Updated: Jan 6, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
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Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

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多功能神经激活预测器与轴状结构量身定制能力 实现个性化神经调制计算

Hongda Li, Shunjing Wang, Xuesong Luo

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |September 25, 2025
    PubMed
    概括

    本研究介绍了一个快速而准确的卷积神经网络 (CNN) 框架用于神经调节. 它可以实现神经刺激的个性化计算模型,改善治疗优化.

    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
    • 生物医学工程 生物医学工程

    背景情况:

    • 神经调节疗法需要精确的刺激策略来进行个性化治疗.
    • 生物物理详细的计算模型对于理解神经激活至关重要,但在计算上是密集的.
    • 目前的模型在各种神经调节场景中缺乏通用性.

    研究的目的:

    • 为神经刺激建模开发一个快速,准确和可概括的计算框架.
    • 克服传统模型对异质神经点的计算负担.
    • 为了促进个性化模拟,为量身定制的神经调节治疗提供便利.

    主要方法:

    • 开发了一个基于卷积神经网络 (CNN) 的框架.
    • 该框架模拟了在各种细胞外刺激条件下的神经激活模式.
    • 该方法与传统计算方法进行了验证.

    主要成果:

    • 在CNN框架中,平均绝对误差 (MAE) 为6.91x10^-3 mV.
    • 在各种刺激场景中,预测准确度超过95%.
    • 与传统方法相比,该方法显示出更高的速度和准确性.

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    Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
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    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
    07:34

    A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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    Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

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    结论:

    • 开发的CNN框架为神经刺激建模提供了一个通用,快速和准确的替代方案.
    • 这种方法提高了神经调节中的计算模型的可扩展性和临床实用性.
    • 它支持为优化神经调节治疗开发个性化模拟.