Episodic Memory

Episodic memory research explores how past experiences are encoded, stored, and retrieved, aiming to understand the mechanisms underlying this crucial cognitive function. Current research focuses on developing computational models of episodic memory, often leveraging neural networks like transformers and recurrent networks, and incorporating these models into larger systems for tasks such as robot learning, time series forecasting, and continual learning. These advancements have implications for improving AI systems' ability to learn and adapt continuously, as well as for gaining a deeper understanding of human memory processes.