TY - GEN
T1 - Accumulator based arbitration model for both supervised and reinforcement learning inspired by prefrontal cortex
AU - Osawa, Masahiko
AU - Ashihara, Yuta
AU - Seno, Takuma
AU - Imai, Michita
AU - Kurihara, Satoshi
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 17J00580.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Accumulator model
KW - Ensemble learning
KW - Hierarchical architecture
KW - Prefrontal cortex
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U2 - 10.1007/978-3-319-70087-8_63
DO - 10.1007/978-3-319-70087-8_63
M3 - Conference contribution
AN - SCOPUS:85035118425
SN - 9783319700861
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 608
EP - 617
BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
A2 - Li, Yuanqing
A2 - Liu, Derong
A2 - Xie, Shengli
A2 - El-Alfy, El-Sayed M.
A2 - Zhao, Dongbin
PB - Springer Verlag
T2 - 24th International Conference on Neural Information Processing, ICONIP 2017
Y2 - 14 November 2017 through 18 November 2017
ER -