A neural network with a biologically possible architecture can implement Bayesian estimation and reproduce Piéron's law

• Futagi Daiki

  Graduate School of Information Science and Engineering, Ritsumeikan University

• Kitano Katsunori

  Department of Human and Computer Intelligence, Ritsumeikan University

書誌事項

タイトル別名

• A neural network with a biologically possible architecture can implement Bayesian estimation and reproduce Piéron's law

抄録

Bayesian estimation theory has been expected to explain how the brain deals with uncertainty. Several previous studies have implied that cortical network models could implement Bayesian computation. However, the feasibility of the required computational procedures is still unclear under the physiological and anatomical constraints of neural systems. Here, we propose a neural network model that implements the algorithm in a biologically realizable manner, incorporating discrete choice theory. Our model successfully demonstrates an orientation discrimination task with significantly noisy visual images and the relation between the stimulus intensity and the reaction time known as Piéron’s law.

収録刊行物

• Nonlinear Theory and Its Applications, IEICE

  Nonlinear Theory and Its Applications, IEICE 7 (2), 146-155, 2016

  一般社団法人 電子情報通信学会

キーワード

• Bayesian estimation theory
• neural network
• discrete choice method
• normalization
• Piéron's law

詳細情報

• CRID: 1390282680321049216
• NII論文ID: 130005142578
• DOI: 10.1587/nolta.7.146
• ISSN: 21854106
• Web Site: https://www.jstage.jst.go.jp/article/nolta/7/2/7_146/_pdf
• 本文言語コード: en
• データソース種別:
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  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可