Topic: differential Hebbian learning

Neural replay, a phenomenon where activation sequences experienced during wakefulness are replayed during rest or sleep, is thought to play a crucial role in memory consolidation and planning [1]. It is commonly modeled as a chain of activity, where links are formed based on temporal associations in the stimulus. Existing work [2,3] focuses on simple patterns, yet we know that the brain is capable of learning complex spatiotemporal dynamics. Specifically chaotic dynamics are even challenging for conventional deep learning algorithms due to exploding gradients. We demonstrate that by introducin