Self and non-self discrimination mechanism based on predictive learning with estimation of uncertainty

Ryoichi Nakajo, Maasa Takahashi, Shingo Murata, Hiroaki Arie, Tetsuya Ogata*

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, we propose a model that can explain the mechanism of self and non-self discrimination. Infants gradually develop their abilities for self–other cognition through interaction with the environment. Predictive learning has been widely used to explain the mechanism of infants’ development. We hypothesized that infants’ cognitive abilities are developed through predictive learning and the uncertainty estimation of their sensory-motor inputs. We chose a stochastic continuous time recurrent neural network, which is a dynamical neural network model, to predict uncertainties as variances. From the perspective of cognitive developmental robotics, a predictive learning experiment with a robot was performed. The results indicate that training made the robot predict the regions related to its body more easily. We confirmed that self and non-self cognitive abilities might be acquired through predictive learning with uncertainty estimation.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
編集者Kazushi Ikeda, Minho Lee, Akira Hirose, Seiichi Ozawa, Kenji Doya, Derong Liu
出版社Springer Verlag
ページ228-235
ページ数8
ISBN(印刷版)9783319466804
DOI
出版ステータスPublished - 2016
イベント23rd International Conference on Neural Information Processing, ICONIP 2016 - Kyoto, Japan
継続期間: 2016 10 162016 10 21

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9950 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other23rd International Conference on Neural Information Processing, ICONIP 2016
国/地域Japan
CityKyoto
Period16/10/1616/10/21

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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