Simulations of a detailed network model show that the pattern of synaptic interactions resulting from learning is critical for the emergence of population bursts, sequential neuronal activity, and fast oscillations in the hippocampus.

Common cognitive maps across feature dimensions are spontaneously leveraged to facilitate storage of multiple sequences via compressed encoding and neural replay in human working memory.

Serially remembered items are successively reactivated during memory maintenance in the human brain, and replay profiles, temporally compressed and reverse in order, are associated with recency effect in behavioral performance.

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.

The combination of short-term and long-term plasticity enables hippocampus to learn goal-directed paths through replay in a reversed order.

In anticipation of a reward, hippocampal place cells simulate desired journeys through areas that have been seen, but not explored.

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

Biologically plausible changes in the excitabilities of single neurons may suffice to selectively modulate sequential network dynamics, without modifying of recurrent connectivity.

Computational simulations and data analysis show that random clustering in the connections of neurons receiving minimal external cues can generate place fields and their spontaneous activation in trajectory-like sequences.