论文标题
时空特征提取的卷积尖峰神经网络
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
论文作者
论文摘要
尖峰神经网络（SNN）可用于低功率和嵌入式系统（例如新兴的神经形态芯片），原因是它们的基于事件的性质。此外，与传统的人工神经网络（ANN）相比，它们具有低计算成本的优势，同时保留了ANN的性质。但是，在卷积尖峰神经网络和其他类型的SNN的层中进行时间编码尚未研究。在本文中，我们可以洞悉旨在利用此特性的实验中卷积SNN的时空特征提取。浅卷积SNN胜过最先进的时空特征提取器方法，例如C3D，ConvlstM和类似的网络。此外，我们提出了一种新的深度峰值体系结构，以解决现实世界中的问题（特别是分类任务），与NMNIST（99.6％），DVS-CIFAR10（69.2％）（69.2％）和DVS-GESTURE（96.7％）和ANN方法相比，其性能优于较高的性能（99.6％）和UCF-101（96.7％）和ANN方法。还值得注意的是，培训过程是根据论文中解释的时空反向传播的变化实施的。
Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature. Also, they have the advantage of low computation cost in contrast to conventional artificial neural networks (ANNs), while preserving ANN's properties. However, temporal coding in layers of convolutional spiking neural networks and other types of SNNs has yet to be studied. In this paper, we provide insight into spatio-temporal feature extraction of convolutional SNNs in experiments designed to exploit this property. The shallow convolutional SNN outperforms state-of-the-art spatio-temporal feature extractor methods such as C3D, ConvLstm, and similar networks. Furthermore, we present a new deep spiking architecture to tackle real-world problems (in particular classification tasks) which achieved superior performance compared to other SNN methods on NMNIST (99.6%), DVS-CIFAR10 (69.2%) and DVS-Gesture (96.7%) and ANN methods on UCF-101 (42.1%) and HMDB-51 (21.5%) datasets. It is also worth noting that the training process is implemented based on variation of spatio-temporal backpropagation explained in the paper.
