Discovering the Neuroanatomical Correlates of Music with Machine Learning In Eduardo Reck Miranda (Eds.). Handbook of Artificial Intelligence for Music. Part 1. Springer Tatsuya Daikoku 1 International Research Center for Neurointelligence, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 2 Centre for Neuroscience in Education, Department of psychology, University of Cambridge, Cambridge, United Kingdom
[email protected] Please cite as: Daikoku T. Discovering the Neuroanatomical Correlates of Music with Machine Learning. In Eduardo Reck Miranda (Eds.). Handbook of Artificial Intelligence for Music. Part 1. Springer (in press). 6.1 Introduction 1 Music is ubiquitous in our lives yet unique to humans. The interaction between music and the brain is complex, engaging a variety of neural circuits underlying sensory perception, learning and memory, action, social communication, and creative activities. Over the past decades, a growing body of literature has revealed the neural and computational underpinnings of music processing including not only sensory perception (e.g., pitch, rhythm, and timbre), but also local/non-local structural processing (e.g., melody and harmony). These findings have also influenced Artificial Intelligence and Machine Learning systems, enabling computers to possess human-like learning and composing abilities. Despite the plenty of evidence, more study is required for complete account of music knowledge and creative mechanisms in human brain. This chapter reviews the neural correlates of unsupervised learning with regard to the computational and neuroanatomical architectures of music processing. Further, we offer a novel theoretical perspective on the brain’s unsupervised learning machinery that considers computational and neurobiological constraints, highlighting the connections between neuroscience and machine learning.