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相关概念视频

Long-term Potentiation01:25

Long-term Potentiation

2.8K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
2.8K
Neuroplasticity01:01

Neuroplasticity

344
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.
344
Integration of Synaptic Events01:28

Integration of Synaptic Events

1.5K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
1.5K
Long-term Depression01:03

Long-term Depression

2.5K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
2.5K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

2.5K
Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
2.5K
Neural Circuits01:25

Neural Circuits

1.2K
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...
1.2K

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相关实验视频

Updated: Jun 29, 2025

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

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在尖端网络中进行自适应性突触缩放,以实现持续学习和增强强性.

Mingkun Xu, Faqiang Liu, Yifan Hu

    IEEE transactions on neural networks and learning systems
    |March 27, 2024
    PubMed
    概括

    我们介绍了一个适应性突触缩放机制,用于增强神经网络 (SNN),以增强学习. 这种方法提高了抗扰和持续学习任务的性能,证明了SNN的潜力.

    科学领域:

    • 计算神经科学是一种神经科学.
    • 人工智能的人工智能

    背景情况:

    • 突触可塑性对于神经网络的功能至关重要,突触缩放维持平衡.
    • 尖端神经网络 (SNN) 通过时间利用反向传播,但缺乏强大的突触缩放机制.

    研究的目的:

    • 为SNN提出和评估一个经验依赖的自适应性突触缩放机制 (AS-SNN).
    • 为了提高SNN在抗扰,持续学习和图形学习任务中的性能.

    主要方法:

    • 开发了一种两阶段的学习过程:在前进路径中适应性短期增强/减弱,在后退路径中进行梯度调节的长期巩固.
    • 该机制使用突触前活动来调节突触强度,理论上被证明是趋同的.
    • 在对抗扰和持续学习的N-MNIST基准和图表学习任务上进行了测试.

    主要成果:

    • 在N-MNIST基准测试中,AS-SNN提高了44%的抗扰度和25%的持续学习任务的准确性.
    • 在图形学习任务中观察到预期的发射率回调和稀疏的编码.
    • 通过废除研究和成本评估证明了有效性和效率.

    结论:

    • 提出的非参数自适应缩放方法对SNN是有效和高效的.

    更多相关视频

    Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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    Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
    10:45

    Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

    Published on: May 29, 2017

    9.9K
    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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    Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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  • AS-SNN显示出在SNN中推进持续和强大的学习的巨大潜力.