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通过形态相边界神经元的全铁电尖端神经网络

Jangsaeng Kim1,2, Eun Chan Park1, Wonjun Shin3

  • 1Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 9, 2024
PubMed
概括

这项研究引入了一种全铁电尖端神经网络 (SNN),使用新型双门晶体管用于人工神经元. 这一突破提高了高级神经形态计算应用的面积和能源效率.

关键词:
这是一个双门的门.泄漏的整合和发射神经元.形态变异的相位边界这是一个神经形态神经形态的神经形态.峰值频率适应的调整尖的神经网络的神经网络.

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The Role of Ion Channels in Neuronal Computation01:19

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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.