论文标题
部分可观测时空混沌系统的无模型预测
Supervised Training of Siamese Spiking Neural Networks with Earth Mover's Distance
论文作者
论文摘要
这项研究将高度呈现的暹罗神经网络模型适应事件数据域。我们引入了一个有监督的培训框架，以优化具有尖峰神经网络（SNN）的尖峰火车之间的地球搬运工（EMD）。我们使用新型转换方案转换为尖峰域的MNIST数据集的图像进行训练。通过测量不同数据集编码类型的分类器性能来评估输入图像的暹罗嵌入质量。这些模型的性能类似于现有的基于SNN的方法（最高为0.9386），而仅使用约15％的隐藏层神经元来对每个示例进行分类。此外，没有使用稀疏神经代码的模型比稀疏的模型慢45％。这些属性使该模型适用于低能消耗和低预测潜伏期应用。
This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth Mover's Distance (EMD) between spike trains with spiking neural networks (SNN). We train this model on images of the MNIST dataset converted into spiking domain with novel conversion schemes. The quality of the siamese embeddings of input images was evaluated by measuring the classifier performance for different dataset coding types. The models achieved performance similar to existing SNN-based approaches (F1-score of up to 0.9386) while using only about 15% of hidden layer neurons to classify each example. Furthermore, models which did not employ a sparse neural code were about 45% slower than their sparse counterparts. These properties make the model suitable for low energy consumption and low prediction latency applications.
