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相关概念视频

Neuroplasticity01:01

Neuroplasticity

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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.
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Neural Circuits01:25

Neural Circuits

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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...
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The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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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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Neurogenesis and Regeneration of Nervous Tissue01:15

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In the CNS, neurogenesis, the birth of new neurons from stem cells, is limited to the hippocampus in adults. In other regions of the brain and spinal cord, neurogenesis is almost non-existent due to inhibitory influences from neuroglia, especially oligodendrocytes, and the absence of growth-stimulating cues. The myelin produced by oligodendrocytes in the CNS inhibits neuronal regeneration. Furthermore, astrocytes proliferate rapidly after neuronal damage, forming scar tissue that physically...
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Author Spotlight: Investigating the Mechanisms of Neural Circuit Assembly and Synapse Formation in Drosophila
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对比性神经元修剪用于后门防御

Yu Feng, Benteng Ma, Dongnan Liu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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    此摘要是机器生成的。

    这项研究介绍了对抗性神经修剪 (CNP),这是对深度神经网络 (DNN) 后门攻击的新防御. CNP有效地识别和删除关键神经元,以最小的修剪确保模型的安全.

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    科学领域:

    • 人工智能的人工智能
    • 机器学习安全 机器学习安全
    • 深度神经网络 深度神经网络

    背景情况:

    • 深度神经网络 (DNN) 容易受到后门攻击,恶意触发器通过中毒的训练数据嵌入其中.
    • 识别和修剪负责这些后门的神经元至关重要,但当前的方法具有挑战性,通常需要标记干净的数据.

    研究的目的:

    • 提出一种新的防御策略,即对比神经元修剪 (CNP),以减轻DNN后门攻击.
    • 解决现有的神经元修剪技术的局限性,特别是它们依赖标记清洁数据的局限性.

    主要方法:

    • CNP利用了在后门模型中中毒样本的特征空间聚类.
    • 它使用对比式学习生成良性-良性和良性-毒性特征对来增强特征分离.
    • 在批量规范化层中,对这些对有显著反应差异的神经元被识别和修剪.

    主要成果:

    • 通过削减少量的百分比 (大约约1%) 来有效地防御CNP的后门攻击. 1%) 的神经元.
    • 该方法在各种基准测试中显示出稳定性和有效性.
    • 它在模型的特征空间中改善了良性和有毒特征之间的分离.

    结论:

    • 对比神经元修剪 (CNP) 提供了有效和高效的防御DNN后门攻击.
    • 这种方法通过准特定的神经元来缓解攻击,而不需要标记清洁数据.
    • CNP在保护深度学习模型免受对抗威胁方面取得了重大进展.