Recently, the use bio-plausible learning techniques such as Hebbian and Spike-Timing-Dependent Plasticity (STDP) have drawn significant attention for the design of compute-efficient AI systems that can continuously learn on-line at the edge. A key differentiating factor regarding this emerging class of neuromorphic continual learning system lies in the fact that learning must be carried using a data stream received in its natural order, as opposed to conventional gradient-based offline training where a static training dataset is assumed available a priori and randomly shuffled to make the training set independent and identically distributed (i.i.d). In contrast, the emerging class of neuromorphic continual learning systems covered in this survey must learn to integrate new information on the fly in a non-i.i.d manner, which makes these systems subject to catastrophic forgetting. In order to build the next generation of neuromorphic AI systems that can continuously learn at the edge, a growing number of research groups are studying the use of bio-plausible Hebbian neural network architectures and Spiking Neural Networks (SNNs) equipped with STDP learning. However, since this research field is still emerging, there is a need for providing a holistic view of the different approaches proposed in literature so far. To this end, this survey covers a number of recent works in the field of neuromorphic continual learning; provides background theory to help interested researchers to quickly learn the key concepts; and discusses important future research questions in light of the different works covered in this paper. It is hoped that this survey will contribute towards future research in the field of neuromorphic continual learning.

翻译：近年来，赫布可塑性与脉冲时序依赖可塑性等生物合理学习机制在构建计算高效、能在边缘端持续在线学习的人工智能系统方面受到广泛关注。这类新兴神经形态持续学习系统的关键区别特征在于：学习必须通过按自然顺序接收的数据流进行，这与传统的基于梯度的离线训练形成鲜明对比——后者假设静态训练数据集可先验获取，并通过随机打乱使训练数据满足独立同分布假设。相比之下，本综述所涵盖的新兴神经形态持续学习系统必须以非独立同分布的方式动态整合新信息，这使得系统容易遭受灾难性遗忘。为构建能在边缘端持续学习的新一代神经形态人工智能系统，越来越多的研究团队正在探索采用具有生物合理性的赫布神经网络架构与搭载STDP学习机制的脉冲神经网络。然而，由于该研究领域尚处于兴起阶段，亟需对现有文献提出的不同方法进行系统性梳理。为此，本综述涵盖了神经形态持续学习领域的多项近期研究成果；提供了背景理论以帮助研究者快速掌握核心概念；并基于所综述的不同工作讨论了重要的未来研究方向。期望本综述能为神经形态持续学习领域的未来研究作出贡献。