The hippocampus and neocortex engage in waves of mutual excitation wherein a sharp wave-ripple may occur at any time before, during or after the peak of the hippocampal-neocortical activation.

Hippocampal sharp-wave ripples are not required for rats to successfully perform spatial tasks that rely on memorization of locations at short timescales.

A new method is described to identify sharp-wave ripples from the rodent hippocampus with deep learning techniques, which may help to identify and characterize previously undetected physiological events.

Elevated cholinergic activity disrupts offline hippocampal state and impairs memory formation highlighting the need for a precise timing of cholinergic activity in learning and memory.

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.

Hippocampal sharp-wave sequences, which are considered the key phenomenon underlying consolidation of episodic memory, are preserved even after an animal’s memory is impaired.

Recordings of cellular and network activities of the mouse brain provide insights into how gamma rhythms contribute to memory formation.

During awake Sharp wave-ripple memory reactivation, hippocampal-cortical representations for movement- versus immobility-associated experiences are reactivated separately.

Machine learning models of coordinated hippocampal ensemble activity during sharp wave ripple activity encode structure that mirrors the place cell map expressed during exploration, and enable a new paradigm for analyzing and understanding this offline activity.

A new state space decoder revealed that most hippocampal sharp-wave ripples contain coherent spatial content, and that this 'replay' typically progresses at speeds similar to those seen during actual experiences.