亚尖峰膜电位对尖峰神经网络中准确推断的影响。,Scientific Reports

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亚尖峰膜电位对尖峰神经网络中准确推断的影响。
Scientific Reports ( IF 3.8 ) Pub Date : 2020-02-26 , DOI: 10.1038/s41598-020-60572-8
Sungmin Hwang ₁ , Jeesoo Chang ₁ , Min-Hye Oh ₁ , Jong-Ho Lee ₁ , Byung-Gook Park ₁

Affiliation

尖峰神经网络（SNN）被认为是第三代人工神经网络，具有提高常规计算系统能源效率的潜力。尽管尖峰神经元的激发速率是模拟值神经网络（ANN）中整流线性单位（ReLU）激活的近似值，但由于ANN和SNN之间的操作差异，仍有许多挑战需要克服。与实际的生物和生物物理过程不同，神经元和SNN的各种硬件实现方式不允许膜电位降到静息电位以下-换句话说，神经元必须允许亚静息膜电位。由于存在兴奋性突触后电位（EPSP）和抑制性突触后电位（IPSP），SNN中负值的突触权重会在某个时间点诱导亚静止膜电位。如果不允许膜保持亚静止电位，则随着时间的流逝会积累错误，从而导致推理操作不准确。由于仅使用空间突触整合，因此在ANN中未观察到此现象，但是它会导致SNN中严重的性能下降。在本文中，我们演示了亚静止膜电位对SNN中精确推理操作的影响。此外，讨论了可以保持亚静止膜电位的硬件SNN的几个重要考虑因素。本文中的所有结果表明，神经元必须具有亚静止膜电位才能实现高性能SNN。

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Impact of the Sub-Resting Membrane Potential on Accurate Inference in Spiking Neural Networks.

Spiking neural networks (SNNs) are considered as the third generation of artificial neural networks, having the potential to improve the energy efficiency of conventional computing systems. Although the firing rate of a spiking neuron is an approximation of rectified linear unit (ReLU) activation in an analog-valued neural network (ANN), there remain many challenges to be overcome owing to differences in operation between ANNs and SNNs. Unlike actual biological and biophysical processes, various hardware implementations of neurons and SNNs do not allow the membrane potential to fall below the resting potential-in other words, neurons must allow the sub-resting membrane potential. Because there occur an excitatory post-synaptic potential (EPSP) as well as an inhibitory post-synaptic potential (IPSP), negatively valued synaptic weights in SNNs induce the sub-resting membrane potential at some time point. If a membrane is not allowed to hold the sub-resting potential, errors will accumulate over time, resulting in inaccurate inference operations. This phenomenon is not observed in ANNs given their use of only spatial synaptic integration, but it can cause serious performance degradation in SNNs. In this paper, we demonstrate the impact of the sub-resting membrane potential on accurate inference operations in SNNs. Moreover, several important considerations for a hardware SNN that can maintain the sub-resting membrane potential are discussed. All of the results in this paper indicate that it is essential for neurons to allow the sub-resting membrane potential in order to realize high-performance SNNs.

更新日期：2020-02-26

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