# Teaching AI to Remember: Insights from Brain-Inspired Replay in Continual Learning

## TL;DR

This paper explores brain-inspired internal replay mechanisms in neural networks to improve continual learning, demonstrating that replay reduces forgetting but may limit initial accuracy and task differentiation.

## Contribution

It provides an in-depth analysis of internal replay's effects on memory retention and representation in continual learning, highlighting trade-offs and limitations.

## Key findings

- Internal replay mitigates forgetting significantly.
- Pairing replay with Synaptic Intelligence enhances stability.
- Replay increases representational overlap, affecting task differentiation.

## Abstract

Artificial neural networks (ANNs) continue to face challenges in continual learning, particularly due to catastrophic forgetting, the loss of previously learned knowledge when acquiring new tasks. Inspired by memory consolidation in the human brain, we investigate the internal replay mechanism proposed by~\citep{brain_inspired_replay1}, which reactivates latent representations of prior experiences during learning. As internal replay was identified as the most influential component among the brain-inspired mechanisms in their framework, it serves as the central focus of our in-depth investigation. Using the CIFAR-100 dataset in a class-incremental setting, we evaluate the effectiveness of internal replay, both in isolation and in combination with Synaptic Intelligence (SI). Our experiments show that internal replay significantly mitigates forgetting, especially when paired with SI, but at the cost of reduced initial task accuracy, highlighting a trade-off between memory stability and learning plasticity. Further analyses using log-likelihood distributions, reconstruction errors, silhouette scores, and UMAP projections reveal that internal replay increases representational overlap in latent space, potentially limiting task-specific differentiation. These results underscore the limitations of current brain-inspired methods and suggest future directions for balancing retention and adaptability in continual learning systems.