Memory Consolidation

Memory consolidation research focuses on enabling artificial intelligence systems to learn continuously without catastrophic forgetting – the loss of previously acquired knowledge when learning new information. Current efforts center on developing biologically-inspired algorithms and architectures, such as those incorporating synaptic consolidation, metaplasticity, and experience replay mechanisms, often within spiking neural networks or deep learning models with specialized memory modules. These advancements aim to create more robust and efficient AI systems capable of lifelong learning, mirroring the human brain's ability to retain and integrate information over time, with applications ranging from robotics to personalized medicine.