The human brain uses a phase code to temporally segregate overlapping, competing memories along the phase cycle of the theta rhythm.

Different rhythms uniquely contribute to task-related processing in the hippocampus, and changes in the rhythmic profile of the hippocampus reflect dynamic coordination of its cell activity.

Computational modeling suggests that the BLA is capable of producing the recorded LFP rhythms via interactions of interneuron subtypes, and that those rhythms may be necessary for associative learning.

Variations in the frequency of theta brain waves enable a single network of brain regions to generate appropriate responses to stimuli with different kinds of emotional value.

Integration and segregation of information of memory and sensory in the hippocampus could be achieved by the coordination of distinct theta-gamma coding frameworks.

Theta oscillations synchronize perception with the emerging motor intention in an automatic and task-independent way.

Spontaneous theta oscillations and interneuron-specific phase preferences emerge spontaneously in a full-scale model of the isolated hippocampal CA1 subfield, corroborating and extending recent experimental findings.

A statistically principled approach developed to estimate phase of rhythmic signals in real-time shows robustness to multiple sources of error and also provides confidence criteria.

Consistent with an existing computational model of hippocampal function, using real-time feedback to drive artificial memories at the trough of the hippocampal theta rhythm improves apparent recall.

Computational modelling shows that coupled theta and gamma oscillations in the auditory cortex can decompose speech into its syllabic constituents, and organize the neural spiking at faster timescale into a decodable format.