To adapt to real-world dynamics, intelligent systems need to assimilate new knowledge without catastrophic forgetting, where learning new tasks leads to a degradation in performance on old tasks. To address this, continual learning concept is proposed for enabling autonomous systems to acquire new knowledge and dynamically adapt to changing environments. Specifically, energy-efficient continual learning is needed to ensure the functionality of autonomous systems under tight compute and memory resource budgets (i.e., so-called autonomous embedded systems). Neuromorphic computing, with brain-inspired Spiking Neural Networks (SNNs), offers inherent advantages for enabling low-power/energy continual learning in autonomous embedded systems. In this paper, we comprehensively discuss the foundations and methods for enabling continual learning in neural networks, then analyze the state-of-the-art works considering SNNs. Afterward, comparative analyses of existing methods are conducted while considering crucial design factors, such as network complexity, memory, latency, and power/energy efficiency. We also explore the practical applications that can benefit from SNN-based continual learning and open challenges in real-world scenarios. In this manner, our survey provides valuable insights into the recent advancements of SNN-based continual learning for real-world application use-cases.

翻译：为适应现实世界的动态变化，智能系统需要在不发生灾难性遗忘的情况下吸收新知识，即学习新任务不会导致旧任务性能下降。为此，持续学习的概念被提出，旨在使自主系统能够获取新知识并动态适应不断变化的环境。具体而言，需要实现高能效的持续学习，以确保自主系统在严格的计算和内存资源预算（即所谓的自主嵌入式系统）下正常运行。神经形态计算凭借其受大脑启发的脉冲神经网络，为在自主嵌入式系统中实现低功耗/低能耗的持续学习提供了固有优势。本文全面探讨了在神经网络中实现持续学习的基础与方法，进而分析了考虑SNN的最新研究成果。随后，我们对现有方法进行了比较分析，同时考虑了网络复杂度、内存、延迟和功耗/能效等关键设计因素。我们还探讨了能够受益于基于SNN的持续学习的实际应用，以及现实场景中存在的开放挑战。通过这种方式，本综述为基于SNN的持续学习在现实应用案例中的最新进展提供了有价值的见解。