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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
PublisherSpringer Verlag
Pages608-617
Number of pages10
Volume10634 LNCS
ISBN (Print)9783319700861
DOIs
Publication statusPublished - 2017 Jan 1
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: 2017 Nov 142017 Nov 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10634 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

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

Fingerprint

Arbitration
Supervised learning
Reinforcement learning
Supervised Learning
Cortex
Reinforcement Learning
Brain
Module
Animals
Neurons
Model
Neuron

Keywords

  • Accumulator model
  • Ensemble learning
  • Hierarchical architecture
  • Prefrontal cortex

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Osawa, M., Ashihara, Y., Seno, T., Imai, M., & Kurihara, S. (2017). Accumulator based arbitration model for both supervised and reinforcement learning inspired by prefrontal cortex. In Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (Vol. 10634 LNCS, pp. 608-617). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10634 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70087-8_63

Accumulator based arbitration model for both supervised and reinforcement learning inspired by prefrontal cortex. / Osawa, Masahiko; Ashihara, Yuta; Seno, Takuma; Imai, Michita; Kurihara, Satoshi.

Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Vol. 10634 LNCS Springer Verlag, 2017. p. 608-617 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10634 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Osawa, M, Ashihara, Y, Seno, T, Imai, M & Kurihara, S 2017, Accumulator based arbitration model for both supervised and reinforcement learning inspired by prefrontal cortex. in Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. vol. 10634 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10634 LNCS, Springer Verlag, pp. 608-617, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 17/11/14. https://doi.org/10.1007/978-3-319-70087-8_63
Osawa M, Ashihara Y, Seno T, Imai M, Kurihara S. Accumulator based arbitration model for both supervised and reinforcement learning inspired by prefrontal cortex. In Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Vol. 10634 LNCS. Springer Verlag. 2017. p. 608-617. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-70087-8_63
Osawa, Masahiko ; Ashihara, Yuta ; Seno, Takuma ; Imai, Michita ; Kurihara, Satoshi. / Accumulator based arbitration model for both supervised and reinforcement learning inspired by prefrontal cortex. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Vol. 10634 LNCS Springer Verlag, 2017. pp. 608-617 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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