Accumulator based arbitration model for both supervised and reinforcement learning inspired by prefrontal cortex

Masahiko Osawa, Yuta Ashihara, Takuma Seno, Michita Imai, Satoshi Kurihara

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

A method that provides an excellent performance by arbitrating multiple modules is important. There are variety of multi-module arbitration methods proposed in various contexts. However, there is yet to be a multi-module arbitration method proposed in reference to structure of animals’ brains. Considering that the animals’ brains achieve general-purpose multi-module arbitration, such function may be achieved by referring to the actual brain. In this paper, with reference to the knowledge of accumulator neurons hypothesized to exist in the prefrontal cortex, we propose an Accumulator Based Arbitration Model (ABAM). By arbitrating multiple modules, ABAM exerts a superior performance in both supervised learning and reinforcement learning task.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
出版社Springer Verlag
ページ608-617
ページ数10
10634 LNCS
ISBN(印刷版)9783319700861
DOI
出版ステータスPublished - 2017 1 1
イベント24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
継続期間: 2017 11 142017 11 18

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)
10634 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other24th International Conference on Neural Information Processing, ICONIP 2017
CountryChina
CityGuangzhou
Period17/11/1417/11/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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