Topic: chaotic dynamics

Neural mass models (NMM) seek to unveil the fundamental principles governing macroscopic neural activity, drawing insights from the brain's intricate and multi-scale structure. The advent of an exact mean-field theory for networks of quadratic integrate-and-fire (QIF) neurons represents a significant leap forward in our comprehension of the link between microscale neural mechanisms and mesoscopic brain activity [1]. This model establishes a robust coupling between two biophysically relevant macroscopic quantities: the firing rate and the mean membrane potential, which collectively drive the ev

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