Artificial neural networks normally require precise weights to operate, despite their origins in biological systems, which can be highly variable and noisy. When implementing artificial networks which utilize analog 'synaptic' devices to encode weights, however, inherent limits are placed on the accuracy and precision with which these values can be encoded. In this work, we investigate the effects that inaccurate synapses have on spiking neurons and spiking neural networks. Starting with a mathematical analysis of integrate-and-fire (IF) neurons, including different non-idealities (such as leakage and channel noise), we demonstrate that noise can be used to make the behavior of IF neurons more robust to synaptic inaccuracy. We then train spiking networks which utilize IF neurons with and without noise and leakage, and experimentally confirm that the noisy networks are more robust. Lastly, we show that a noisy network can tolerate the inaccuracy expected when hafnium-oxide based resistive random-access memory is used to encode synaptic weights.

中文翻译：

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尖峰神经网络的随机性和鲁棒性

人工神经网络通常需要精确的权重才能运行，尽管它们起源于生物系统，生物系统可能高度可变且嘈杂。然而，在实现利用模拟“突触”设备对权重进行编码的人工网络时，对这些值进行编码的准确性和精度存在固有限制。在这项工作中，我们研究了不准确的突触对尖峰神经元和尖峰神经网络的影响。从对积分和激发 (IF) 神经元的数学分析开始，包括不同的非理想情况（例如泄漏和通道噪声），我们证明了噪声可用于使 IF 神经元的行为对突触不准确更稳健。然后我们训练利用 IF 神经元的尖峰网络，有无噪声和泄漏，并通过实验证实嘈杂的网络更健壮。最后，我们表明，当基于氧化铪的电阻随机存取存储器用于编码突触权重时，噪声网络可以容忍预期的不准确性。