Functional roles of brain anatomical structures in the fault tolerance

2020 Fiscal Year Final Research Report

• Project/Area Number: 18K11527
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 62010:Life, health and medical informatics-related
• Research Institution: Yamaguchi University
• Principal Investigator: Samura Toshikazu 山口大学, 大学院創成科学研究科, 准教授 (30566617)
• Project Period (FY): 2018-04-01 – 2021-03-31
• Keywords: 脳 / リカレントニューラルネットワーク / 故障耐性 / 過学習抑制 / 初期構造 / 初期化

Outline of Final Research Achievements

In this study, we introduced brain anatomical structures that are ubiquitous across brain regions as initial constraint into recurrent neural network (RNNs). We evaluated their roles in computation and showed that the structural distinction between excitatory and inhibitory neurons contributes to prevent overfitting. Moreover, the partial connectivity contributes to the improvement of fault tolerance of RNNs. These structures complementarily work and improve the performance and fault tolerance of the network.

Free Research Field

計算論的神経科学

Academic Significance and Societal Importance of the Research Achievements

脳の様々な領域に共通する解剖構造によるリカレントニューラルネットワーク（RNN）の性能向上と故障に対する頑健性向上への寄与を示した．初期構造として導入するだけで学習後の性能向上につながるため，計算コストが少なくて済む．そのため，これらの知見は，計算資源が乏しく，一部が故障しても性能を維持する必要のある状況におけるRNNに適用でき，適用範囲の広いRNNの初期化手法としての応用が考えられる．