Energy and data-efficient online time series prediction for predicting
evolving dynamical systems are critical in several fields, especially edge AI
applications that need to update continuously based on streaming data. However,
current DNN-based supervised online learning models require a large amount of
training data and cannot quickly adapt when the underlying system changes.
Moreover, these models require continuous retraining with incoming data making
them highly inefficient. To solve these issues, we present a novel Continuous
Learning-based Unsupervised Recurrent Spiking Neural Network Model (CLURSNN),
trained with spike timing dependent plasticity (STDP). CLURSNN makes online
predictions by reconstructing the underlying dynamical system using Random
Delay Embedding by measuring the membrane potential of neurons in the recurrent
layer of the RSNN with the highest betweenness centrality. We also use
topological data analysis to propose a novel methodology using the Wasserstein
Distance between the persistence homologies of the predicted and observed time
series as a loss function. We show that the proposed online time series
prediction methodology outperforms state-of-the-art DNN models when predicting
an evolving Lorenz63 dynamical system.

该论文提出了一种基于连续学习无监督回路尖峰神经网络模型（CLURSNN）的在线时间序列预测方法，利用其测量具有最高度数中心性的 RSNN 中神经元的膜电位来重构底层动态系统，并且通过拓扑数据分析，提出了一种新的利用 Wasserstein 距离作为损失函数的方法，该方法在预测演变中的 Lorenz63 动态系统方面优于现有的 DNN 模型。