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相关概念视频

Electrical Synapses01:28

Electrical Synapses

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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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Propagation of Action Potentials01:23

Propagation of Action Potentials

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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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Magnetic Field Due To A Thin Straight Wire01:28

Magnetic Field Due To A Thin Straight Wire

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Consider an infinitely long straight wire carrying a current I. The magnetic field at point P at a distance a from the origin can be calculated using the Biot-Savart law.
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相关实验视频

Updated: Jul 1, 2025

Fabrication of Magnetic Platforms for Micron-Scale Organization of Interconnected Neurons
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量子化磁域壁突触用于高效的深度神经网络.

Seema Dhull, Walid Al Misba, Arshid Nisar

    IEEE transactions on neural networks and learning systems
    |March 12, 2024
    PubMed
    概括

    这项研究证明了使用磁域壁 (DW) 突触阵列进行高效的芯片上神经网络训练和推断. 与CMOS设计相比,量子化神经网络 (QNN) 实现了高精度,同时显著改善了面积,能量和延迟.

    科学领域:

    • 这就是Spintronics.
    • 神经形态计算是一种神经形态计算.
    • 固态电路的使用方法

    背景情况:

    • 使用非挥发性内存 (NVM) 的量子化突触权重为硬件提供了高效的神经网络.
    • 挑战包括有限的量子化状态,设备变化和NVM设备中的随机性.

    研究的目的:

    • 以展示基于磁域壁 (DW) 的突触阵列的神经网络的芯片上训练和推断.
    • 为了评估这个量子化神经网络 (QNN) 架构的性能,考虑到设备的非理想性.

    主要方法:

    • 利用磁性DW设备的严格模型,包括随机性和过程变化.
    • 通过物理约束实现DW固定,以获得稳定的量化重量.
    • 模拟VGG8架构用于CIFAR-10图像分类,使用提取的突触装置特征.

    主要成果:

    • 实现了92.4%的训练准确度和90.4%的推断准确度用于QNN.
    • 与纯CMOS设计相比,在面积,能量和延迟方面取得了显著的改善.
    • 评估性能指标,考虑到工艺变化和DW设备和外围电路中的非理想性.

    结论:

    • 拟议的QNN架构可以通过磁性DW突触实现高效的芯片内学习.

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  • 这种方法为资源有限的硬件在面积,能量和延迟方面提供了实质性的优势.
  • 该研究强调了基于DW的NVM在实际神经形态计算应用中的潜力.