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.

翻译：人脑能够自组织形成丰富多样的稀疏神经通路，逐步掌握数百种认知任务。然而，现有面向深度人工神经网络和脉冲神经网络的持续学习算法大多无法充分自动调节网络中有限的资源，导致随着任务数量增加，性能下降与能耗上升。本文提出一种受大脑启发的基于神经通路自适应重组的持续学习算法，通过自组织调控网络将单一且资源受限的脉冲神经网络（SOR-SNN）重组为丰富的稀疏神经通路，以高效应对增量任务。该模型在从儿童类简单任务到复杂任务的多样化持续学习场景，以及泛化的CIFAR100和ImageNet数据集上，在性能、能耗和记忆容量方面均展现出持续优势。特别地，SOR-SNN模型在掌握更复杂任务和更多任务方面表现卓越，并能将过去学到的知识与当前任务信息相整合，展现出促进旧任务学习的反向迁移能力。同时，该模型对不可逆损伤具有自修复能力，对经过剪枝的网络，能从保留网络中自动分配新通路来恢复对遗忘知识的记忆。