网络分割与自适应突触可塑性融合的连续学习OA北大核心CSTPCD

Spiking neural networks will suffers fromcatastrophic forgetting in the process of continual learning.Existing unsupervised methods need a large enough network size to achieve good learning results,but it will consume a lot of computing time and resources.This paper aims to achieve better continual learning effect with a small network scale.Inspired by neuroscience,a method of combining split network with existing adaptive synaptic plasticity was proposed:firstly,the network was divided into subnetworks with the same number of tasks and without overlapping each other,and then each subnetwork performed unsupervised learning through adaptive synaptic plasticity.This method is easy to implement,requires little computationaloverhead,and allows small-scale spiking neural networks to maintain high performance across large numbers of sequentially presented tasks.The results show a significantly higher test accuracy with split network than without the split network method on the small-scale spiking neural networks.The average increase is about 40％for the four datasets.