论文标题
SAPINET：基于事件的稀疏时空振荡器，用于野外学习
Sapinet: A sparse event-based spatiotemporal oscillator for learning in the wild
论文作者
论文摘要
我们介绍了Sapinet - 用于\ textit {wild the Wild}的基于尖峰的时间（事件）的多层神经网络 - 也就是说：一击在线学习多个输入而无需灾难性的遗忘，而无需数据特异性的超级参数重新调整。 SAPINET的关键特征包括数据正则化，模型缩放，数据分类和DeNoising。该模型还支持刺激相似性映射。我们提出了一种系统的方法来调整网络的性能。我们研究了不同气味相似性，高斯和冲动噪声的模型性能。 SAPINET在标准机器嗅觉数据集上实现了高分类精度，而无需对特定数据集进行微调。
We introduce Sapinet -- a spike timing (event)-based multilayer neural network for \textit{learning in the wild} -- that is: one-shot online learning of multiple inputs without catastrophic forgetting, and without the need for data-specific hyperparameter retuning. Key features of Sapinet include data regularization, model scaling, data classification, and denoising. The model also supports stimulus similarity mapping. We propose a systematic method to tune the network for performance. We studied the model performance on different levels of odor similarity, gaussian and impulse noise. Sapinet achieved high classification accuracies on standard machine olfaction datasets without the requirement of fine tuning for a specific dataset.
