Data-driven approaches for unveiling the neurophysiological functions of the auditory system

Abstract

Typical neurophysiological experiments employ ``hypothesis-driven'' approaches: Researchers set a specific hypothesis, based on which stimuli and their parameters are chosen. However, there is always a concern that the hypothesis or stimulus parameter could be irrelevant to the essence of the brain function. The present paper review the authors' recent studies that have applied some ``data-driven'' approaches as relatively hypothesis-free methodologies to traditional questions in auditory neurophysiology, such as neural frequency tuning and cortical topography. The results provide some new insights into the functional organization of the cortex and the optimality of the brain structure for auditory processing.