Achievement

Trainee Adam Gifford works in the area of higher order auditory perception, studying how spectro-temporal regularities in acoustic stimuli affect the ability to perceive and segregate distinct auditory "objects," as well as how these spectro-temporal regularities are related to regularities in neural oscillatory information. He is testing how stimuli with varying degrees of predictability in acoustic frequency content over time are reflected in changes in power and phase alignment in neural oscillations. He has begun to analyze neural recordings from human epilepsy patients who have implanted surface electrodes in auditory and non-auditory cortical fields, which allows identification of regions where oscillatory activity correlates with the regularity in frequency of a stimulus. Gifford’s IGERT cross-training in computer vision is enabling him to apply ideas from machine learning to his data analysis.