Accumulator Based Arbitration Model for both Supervised and Reinforcement Learning Inspired by Prefrontal Cortex

  • Masahiko Osawa
  • Yuta Ashihara
  • Takuma Seno
  • Michita Imai
  • Satoshi Kurihara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)

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.

Keywords

Accumulator model Ensemble learning Hierarchical architecture Prefrontal cortex 

Notes

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 17J00580.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Masahiko Osawa
    • 1
    • 2
  • Yuta Ashihara
    • 3
    • 4
  • Takuma Seno
    • 1
  • Michita Imai
    • 1
  • Satoshi Kurihara
    • 3
  1. 1.Keio UniversityKohoku-ku, Yokohama-shiJapan
  2. 2.Research Fellow of Japan Society for the Promotion of Science (DC1)TokyoJapan
  3. 3.The University of Electro-CommunicationsChofuJapan
  4. 4.Xcompass LtdTokyoJapan

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