The project seeks to identify for a reciprocal relationship between learning and sleep. This would enable sleep not only to be affected by learning, but in turn to affect subsequent learning. Should this reciprocal interaction be played out within a common set of neuroplastic mechanisms it would allow learning to prime plasticity during subsequent sleep to enhance learning the next day in an unrelated memory task.

We will combine high-density sleep recording with learning tasks to identify those aspects of sleep affected by earlier learning, and affecting subsequent learning. This will expose the student to behavioural analysis, sleep analysis, and advanced modelling of EEG data to preform statistical/cluster analysis to determine the relationship between sleep, and learning performance.

The student will develop a wide range of skills. We will provide training across the scientific method and technical aspects including MATLAB, sleep scoring and sleep analysis. This will ensure the student will be in a strong position for future research, or seek exciting opportunities outside of the University/academic sector.

There is substantial scope for the successful applicant to sculpt the project to their interests.

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