In this paper, the use of third-generation machine learning, also known as spiking neural network architecture, for continuous learning was investigated and compared to conventional models. The experimentation was divided into three separate phases. The first phase focused on training the conventional models via transfer learning. The second phase trains a Nengo model from their library. Lastly, each conventional model is converted into a spiking neural network and trained. Initial results from phase 1 are inline with known knowledge about continuous learning within current machine learning literature. All models were able to correctly identify the current classes, but they would immediately see a sharp performance drop in previous classes due to catastrophic forgetting. However, the SNN models were able to retain some information about previous classes. Although many of the previous classes were still identified as the current trained classes, the output probabilities showed a higher than normal value to the actual class. This indicates that the SNN models do have potential to overcome catastrophic forgetting but much work is still needed.

翻译：本文研究了第三代机器学习（即脉冲神经网络架构）在持续学习中的应用，并将其与传统模型进行了比较。实验分为三个阶段：第一阶段聚焦于通过迁移学习训练传统模型；第二阶段训练来自Nengo库的模型；最后，每个传统模型被转换为脉冲神经网络并进行训练。第一阶段的初步结果与当前机器学习文献中关于持续学习的已知知识一致。所有模型均能正确识别当前类别，但由于灾难性遗忘，它们在先前类别上的性能会立即出现显著下降。然而，脉冲神经网络模型能够保留一些关于先前类别的信息。尽管许多先前类别仍被识别为当前训练的类别，但输出概率显示实际类别的数值高于正常水平。这表明脉冲神经网络模型确实具有克服灾难性遗忘的潜力，但仍有大量工作需要完成。