Feature extractionplays an important role before pattern recognition takes place. The existing artificial neural networks (ANNs), however, ignoreto learn and represent temporal information, instead of only utilizing spatial information for recognition. Moreover, the substantial computational and energy costs resulted from the conventional ANN-based classifiers, limit their uses in mobile and embedded applications. In this work, we develop a sparse temporal encoding method which exploits both spatial and temporal information. On the basis of spike-timing-dependent plasticity and multi-scale structure, the resulting temporal feature representation integrates with a temporal spiking neural network (SNN) classifier to achieve high efficiency of parallel computing for feature extraction. Experimental evaluation on four benchmark datasets from image classification and speech recognition tasks show the proposed SNN model yielding state-of-the-art accuracy.

中文翻译：

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带有事件编码的尖峰事件驱动的深度尖峰神经网络

特征提取在模式识别发生之前起着重要的作用。但是，现有的人工神经网络（ANN）忽略了学习并表示时间信息，而不是仅仅利用空间信息进行识别。此外，传统的基于ANN的分类器导致大量的计算和能源成本，限制了它们在移动和嵌入式应用中的使用。在这项工作中，我们开发了一种利用空间和时间信息的稀疏时间编码方法。在依赖于尖峰时序的可塑性和多尺度结构的基础上，所得的时间特征表示与时间尖峰神经网络（SNN）分类器集成在一起，从而实现了用于特征提取的并行计算的高效率。