The human brain can self-organize rich and diverse sparse neural pathways to
incrementally master hundreds of cognitive tasks. However, most existing
continual learning algorithms for deep artificial and spiking neural networks
are unable to adequately auto-regulate the limited resources in the network,
which leads to performance drop along with energy consumption rise as the
increase of tasks. In this paper, we propose a brain-inspired continual
learning algorithm with adaptive reorganization of neural pathways, which
employs Self-Organizing Regulation networks to reorganize the single and
limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to
efficiently cope with incremental tasks. The proposed model demonstrates
consistent superiority in performance, energy consumption, and memory capacity
on diverse continual learning tasks ranging from child-like simple to complex
tasks, as well as on generalized CIFAR100 and ImageNet datasets. In particular,
the SOR-SNN model excels at learning more complex tasks as well as more tasks,
and is able to integrate the past learned knowledge with the information from
the current task, showing the backward transfer ability to facilitate the old
tasks. Meanwhile, the proposed model exhibits self-repairing ability to
irreversible damage and for pruned networks, could automatically allocate new
pathway from the retained network to recover memory for forgotten knowledge.

基于大脑启发的自适应神经路径重组的持续学习算法，在增量任务中通过自组织调控网络资源以有效应对多样化的认知任务，表现出卓越的性能、能量消耗和内存容量。具备学习更复杂任务、整合过去学习知识以及自我修复能力的特点。