The REM-Sleep Inspired Creative Replay for Continual Learning in Deep Neural Networks: Artificial Intelligence and Continual Learning Systems

Abstract

This paper proposes DREAMLOOP (Dream Replay with Enhanced Anchoring and Memory Loop Optimization), a REM-sleep-inspired continual learning framework that extends generative replay with feature-level stabilization mechanisms. The framework is evaluated on the Split-MNIST benchmark consisting of five sequential tasks using a three-layer Multi-Layer Perceptron (SimpleMLP) classifier, where the 128-dimensional intermediate feature layer serves as the anchor representation. DREAMLOOP integrates a class-conditional variational autoencoder (CVAE) for generative replay and a feature distillation constraint that preserves internal representations across tasks. We compare against the Naive Finetuning, Experience Replay (ER) with a fixed raw-sample buffer, and standard Generative Replay (GR) without stored real data. Performance is measured using accuracy matrices, final average accuracy, backward transfer (forgetting), and memory footprint analysis. Results show that DREAMLOOP substantially improves over pure generative replay by stabilizing intermediate feature representations, achieving 67.8% final average accuracy with a reduced memory budget (M=500), thereby demonstrating favorable trade-off between retention and storage efficiency