Cognitive Replay

Cognitive replay, inspired by the brain's ability to internally rehearse past experiences, aims to improve the efficiency and robustness of artificial neural networks, particularly in continual learning scenarios where models must adapt to new information without forgetting previously learned knowledge. Current research focuses on developing efficient replay mechanisms, including those leveraging generative models and adaptive buffer management strategies that prioritize the replay of crucial information, often inspired by biological memory consolidation processes. These advancements address the significant challenge of catastrophic forgetting in artificial intelligence, potentially leading to more adaptable and human-like learning systems with applications in areas such as robotics and personalized medicine.