fMRI evidence for off-task replay predicts subsequent replanning behavior in humans, suggesting that learning from simulated experience during replay helps update past policies in reinforcement learning.

Temporally delayed linear modelling provides a domain-general linear framework for sequence detection and statistical testing, and is able to detect replays in both human neuroimaging and animal electrophysiology.

A brain–computer interface for real-time identification of transient neural activity patterns enables causal inference of the role of these patterns in cognition through closed-loop manipulation.

Computational modeling predicts that sleep replay plays a protective role against catastrophic forgetting by revealing synaptic mechanisms allowing overlapping populations of neurons to store multiple interfering memories.

An experimentally supported model of grid cells naturally enables them to participate in firing sequences that encode rapid trajectories in space.

Hippocampal place cells modulate their firing rate during replay events to reflect the increases, or decreases, in firing rate experienced between contexts during behavior.

Predictive cognitive maps can be learned during behavior and replays using in spiking neurons.

A model of hippocampal replay is proposed that gives a biologically plausible account of how the hippocampus could prioritize replay and produce a variety of different replay statistics, and is efficient in driving spatial learning.

Replay of recently experienced trajectories during a decision task is coupled with more effective adaptation to change, whereas replay during rest is associated with limited decision making flexibility.