Enhancing Sequential Learning with a Hybrid EWC- Integrated Spiking Neural Network

Abstract

When neural networks are trained on tasks one after another, they often forget previously learned information, a problem known as catastrophic forgetting. While Elastic Weight Consolidation (EWC) has been effective in reducing this issue in conventional neural networks, its application to Spiking Neural Networks (SNNs) has received limited attention. In this study, EWC is integrated with SNNs to develop a continual learning model capable of retaining earlier knowledge while learning new tasks sequentially. The model is evaluated using multiple task variants of the MNIST dataset, including rotated and permuted versions, which introduce distribution shifts across tasks in a controlled manner. Experimental results show that incorporating EWC significantly reduces forgetting compared to standard SNN training, while preserving the biologically inspired and energy- efficient properties of spiking models, as demonstrated through task-wise accuracy and forgetting metrics.