Computational model of face network in inferotemporal cortex based on mixture of sparse coding models

2020 Fiscal Year Final Research Report

• Project/Area Number: 18K11517
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 61060:Kansei informatics-related
• Research Institution: Advanced Telecommunications Research Institute International
• Principal Investigator: Hosoya Haruo 株式会社国際電気通信基礎技術研究所, 脳情報通信総合研究所, 主任研究員 (50335296)
• Project Period (FY): 2018-04-01 – 2021-03-31
• Keywords: 深層生成学習 / 顔ニューロン / 下側側頭野

Outline of Final Research Achievements

Inspired by the face-processing network in the primate higher visual cortex, we developed two deep generative models, group-based variational autoencoder (GVAE) and its extension, mixture of GVAEs. We quantitatively evaluated their performance in artificial intelligence tasks and their reproducibility of response properties of face neurons in macaque brain and thus showed advantages over existing models.

Free Research Field

計算神経科学

Academic Significance and Societal Importance of the Research Achievements

複雑の高次視覚野の計算原理の解明に向けて、重要な一歩を踏むことができた。また、構築した深層生成学習モデルは一般性があり、物体画像以外のデータセットにも幅広く適用可能性がある。