Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence. Existing continual learning frameworks are usually applicable to Deep Neural Networks (DNNs) and lack the exploration on more brain-inspired, energy-efficient Spiking Neural Networks (SNNs). Drawing on continual learning mechanisms during child growth and development, we propose Dynamic Structure Development of Spiking Neural Networks (DSD-SNN) for efficient and adaptive continual learning. When learning a sequence of tasks, the DSD-SNN dynamically assigns and grows new neurons to new tasks and prunes redundant neurons, thereby increasing memory capacity and reducing computational overhead. In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task). We validate the effectiveness of the proposed model on multiple class incremental learning and task incremental learning benchmarks. Extensive experiments demonstrated that our model could significantly improve performance, learning speed and memory capacity, and reduce computational overhead. Besides, our DSD-SNN model achieves comparable performance with the DNNs-based methods, and significantly outperforms the state-of-the-art (SOTA) performance for existing SNNs-based continual learning methods.

翻译：儿童具备顺序学习多种认知任务的能力，这是迈向人工通用智能长期目标的主要挑战。现有持续学习框架通常适用于深度神经网络（DNNs），缺乏对更具脑启发、高能效的脉冲神经网络（SNNs）的探索。借鉴儿童成长发育过程中的持续学习机制，我们提出脉冲神经网络的动态结构发展方法（DSD-SNN），用于高效且自适应的持续学习。在学习任务序列时，DSD-SNN动态分配并生长新神经元以处理新任务，同时修剪冗余神经元，从而增加记忆容量并降低计算开销。此外，重叠的共享结构有助于快速将所有已获取知识应用于新任务，使单个网络能够支持多个增量任务（无需为每个任务设置独立的子网络掩码）。我们在多个类别增量学习和任务增量学习基准上验证了所提模型的有效性。大量实验表明，我们的模型能够显著提升性能、学习速度与记忆容量，同时降低计算开销。此外，DSD-SNN模型达到了与基于DNN的方法相当的性能，并显著超越了现有基于SNN的持续学习方法的当前最优（SOTA）性能。