Supervised learning in spiking, neural networks with noise-threshold
Supervised learning in spiking, neural networks with noise-threshold
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具有噪声阈值的尖峰神经网络中的监督学习
DOI：
10.1016/j.neucom.2016.09.044
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发表时间：
2017
期刊：
影响因子：
6
通讯作者：
Kurths Juergen
中科院分区：
文献类型：
--
作者：
Zhang Malu;Qu Hong;Xie Xiurui;Kurths Juergen
具有与生物神经系统类似的处理尖峰的能力，尖峰神经元的网络预计将实现与活的大脑类似的性能。尽管基于尖峰神经元的应用取得了成就，但大多数应用都假设无噪声条件进行学习和测试。这种假设虽然相当普遍，但忽略了噪声广泛存在于尖峰神经网络（SNN）中并且神经响应会受到噪声的显著干扰的事实。因此，如何处理噪声是神经网络应用中的一个重要问题。在这里，通过分析所采用的策略，使尖峰神经元对噪声鲁棒性，也启发了生物神经元，我们提出了一种策略，训练尖峰神经元的动态发射阈值命名为噪声阈值。噪声阈值可以应用于现有的监督学习方法，以提高它们的噪声容忍度。实验结果表明，结合噪声阈值，现有监督学习方法的抗噪声能力显著提高，即使在高噪声条件下，训练后的神经元也能准确可靠地再现目标棘波序列。更重要的是，基于SNNs的计算模型配备了噪声阈值，具有更强的鲁棒性，即使在不同类型的噪声下也可以实现良好的性能。因此，噪声阈值的确定对于SNN的实际应用和理论研究都具有重要意义。
With a similar capability of processing spikes as biological neural systems, networks of spiking neurons are expected to achieve a performance similar to that of living brains. Despite the achievement of spiking neuron based applications, most of them assume noise-free condition for learning and testing. This assumption, though fairly general, ignores the fact that noise widely exists in spiking neural networks (SNNs) and the neural response can be significantly disturbed by noise. Therefore, how to deal with noise is an important issue in the applications of SNNs. Here, by analyzing strategies employed to make spiking neurons robust to noise, also inspired by biological neurons, we propose a strategy that train spiking neurons with a dynamic firing threshold named noise-threshold. The noise-threshold can be applied by the existing supervised learning methods to improve the noise tolerance of them. Experimental results show that, with a combination of noise-threshold, the anti-noise capability of the existing supervised learning methods improves significantly, and the trained neuron can precisely and reliably reproduce target sequences of spikes even under highly noisy conditions. More importantly, the SNNs-based computational model equipped with a noise-threshold is more robust and can achieve a good performance even with different types of noise. Therefore, the noise-threshold is significant to practical applications and theoretical researches of SNNs.