| Presentation | 2022-09-30 A dynamic state-space reinforcement learning model explaining functional differentiation of higher motor areas in the cerebral cortex Naoki Tamura, Hajime Mushiake, Kazuhiro Sakamoto, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Complex and sequential behaviors based on various cues depend on the frontal higher motor areas of the cerebral cortex. This study aims to understand the learning mechanisms of delayed response and action sequence tasks used to elucidate the functions of these areas, through a dynamic state-space reinforcement learning model. The model dynamically extended the state space or action value function based on the decision uniqueness criterion. Furthermore, two sub-modules were used for new state generation: one using action history within the same episode and the other using different episode/sensory cue combinations, and the model successfully learned the above tasks by their parallel learning. Our model promotes understanding of functional differentiation of these areas, and opens a new field of computational higher brain dysfunction. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Higher motor areas / Reinforcement Learning / Dynamic State-Space / Decision Uniqueness |
| Paper # | NC2022-42 |
| Date of Issue | 2022-09-22 (NC) |
| Conference Information | |
| Committee | NC / MBE |
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| Conference Date | 2022/9/29(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tohoku Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Brain Architecture, NC, ME |
| Chair | Hiroshi Yamakawa(Univ of Tokyo) / Junichi Hori(Niigata Univ.) |
| Vice Chair | Hirokazu Tanaka(Tokyo City Univ.) / Hisashi Yoshida(Kinki Univ.) |
| Secretary | Hirokazu Tanaka(NTT) / Hisashi Yoshida(NICT) |
| Assistant | Yoshimasa Tawatsuji(Waseda Univ.) / Tomoki Kurikawa(KMU) / Emi Yuda(Tohoku Univ) / Miki Kaneko(Osaka Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on ME and Bio Cybernetics |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A dynamic state-space reinforcement learning model explaining functional differentiation of higher motor areas in the cerebral cortex |
| Sub Title (in English) | |
| Keyword(1) | Higher motor areas |
| Keyword(2) | Reinforcement Learning |
| Keyword(3) | Dynamic State-Space |
| Keyword(4) | Decision Uniqueness |
| 1st Author's Name | Naoki Tamura |
| 1st Author's Affiliation | Tohoku University(Tohoku Univ) |
| 2nd Author's Name | Hajime Mushiake |
| 2nd Author's Affiliation | Tohoku University(Tohoku Univ) |
| 3rd Author's Name | Kazuhiro Sakamoto |
| 3rd Author's Affiliation | Tohoku Medical and Pharmaceutical University(TMPU) |
| Date | 2022-09-30 |
| Paper # | NC2022-42 |
| Volume (vol) | vol.122 |
| Number (no) | NC-195 |
| Page | pp.pp.44-48(NC), |
| #Pages | 5 |
| Date of Issue | 2022-09-22 (NC) |