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

Despite significant advancements in deep learning, artificial neural networks (ANNs) still suffer from catastrophic forgetting in continual learning, where training on new tasks causes them to easily forget previously learned information. In contrast, the human brain retains diverse information through declarative and nondeclarative memory systems ([Bear et al., 2020, Figure 24.1, p. 838]), storing it in either short-term or long-term memory. A key factor that protects humans from drastic forgetting is thought to be the reactivation of neural activity patterns representing previous experiences--referred to as memory replay (Wilson and McNaughton [1994], Rasch and Born [2007], Oudiette and Paller [2013], Van de Ven et al. [2016]). To address catastrophic forgetting in ANNs, previous works have attempted to mimic the brain's memory replay mechanism. Notably, studies such as Van de Ven et al. [2020], Millichamp and Chen [2021], Ran et al. [2024] have demonstrated that brain-inspired mechanisms can help retain performance during continual learning in AI. Motivated by these findings, we aim to draw inspiration from the brain to develop mechanisms for long-term memory in AI. Specifically, we focus on analyzing the impact of brain-inspired components on AI performance and providing insights to guide future research directions. 1