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Exploring Age-Related Changes in Multisensory Integration using Machine Learning tools
The project “Exploring Age-Related Changes in Multisensory Integration using Machine Learning tools” aims to investigate how changes in neurotransmitter concentrations influence the way young and older adults integrate multisensory information and perceive time. Its main method of investigation involves applying machine learning tools to analyze the extensive dataset collected from behavioral tasks, Magnetic Resonance Spectroscopy, and Transcranial Magnetic Stimulation. This analysis aims to uncover patterns, correlations, and insights.
Faculty Supervisor:
Michael Barnett-Cowan
Student:
Partner:
National Technical University of Ukraine
Discipline:
Computer science
Sector:
Health and Related Sciences & Technology; Artificial Intelligence; Technology
University:
University of Waterloo
Program:
Globalink Research Award
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