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

• Furukawa Shigeto

  NTT Communication Science Laboratories, NTT Corporation

• Terashima Hiroki

  NTT Communication Science Laboratories, NTT Corporation

• Koumura Takuya

  NTT Communication Science Laboratories, NTT Corporation

• Tsukano Hiroaki

  Brain Research Institute, Niigata University

抄録

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.

収録刊行物

• Acoustical Science and Technology

  Acoustical Science and Technology 41 (1), 63-66, 2020-01-01

  一般社団法人 日本音響学会

キーワード

• Neurophysiology
• Machine learning
• Auditory system
• Neural network

詳細情報

• CRID: 1390565134814804096
• NII論文ID: 130007782606
• DOI: 10.1250/ast.41.63
• ISSN: 13475177
• ISSN: 03694232
• ISSN: 13463969
• Web Site: https://www.jstage.jst.go.jp/article/ast/41/1/41_E19210/_pdf
• 本文言語コード: en
• データソース種別:
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