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| Presentation | 2009-01-19 17:25 Reinforcement Meta-learning rule solves the distal reword problem Shojiro Araki (Tamagawa Univ), Yutaka Sakai (Tamagawa Univ. Brain Scie Inst.)  NC2008-95 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | It is known that spike-timing-dependent synaptic plasticity (STDP) epends on the initial strength of the synapse, and that the dependence is asymmetric for potentiation and depression. It is pointed out that this fact implies a problem: the destination of a synapse should be restricted in a small region detemined by the initial-strength dependence, and little reflect the input-output statistics. If it holds true, then the learning paradigm drawn by Hebb would be broken. In order to solve the problem, we proposed a meta-learning rule depending on reinforcement signals. We applied the meta-learning for STDP learning rule that possesses asymmetric initial-strength dependence, and demonstrated that a single model neuron can learn the selectivity reflecting input statistics. We assume that the reinforcement signals reflect rewards given to the animal, and spread over the whole brain. Here we demonstrate that a single model neuron can learn the selectivity reflecting inputs correlated with rewards given a few seconds after the inputs. The proposed reinforcement meta-learning can solve the distal reward problem as well as the problem in the initial-strength dependence. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | STDP / dopamine / meta-learning rule / distal reword / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 108, no. 383, NC2008-95, pp. 79-83, Jan. 2009. | |
| Paper # | NC2008-95 | |
| Date of Issue | 2009-01-12 (NC) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | NC2008-95 |
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| Conference Information | |
| Committee | NC |
| Conference Date | 2009-01-19 - 2009-01-20 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Neural Dynamics, etc. |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2009-01-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Reinforcement Meta-learning rule solves the distal reword problem |
| Sub Title (in English) | |
| Keyword(1) | STDP |
| Keyword(2) | dopamine |
| Keyword(3) | meta-learning rule |
| Keyword(4) | distal reword |
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| Keyword(6) | |
| Keyword(7) | |
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| 1st Author's Name | Shojiro Araki |
| 1st Author's Affiliation | Tamagawa University (Tamagawa Univ) |
| 2nd Author's Name | Yutaka Sakai |
| 2nd Author's Affiliation | Tamagawa Univ. Brain Scie Inst. (Tamagawa Univ. Brain Scie Inst.) |
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| Speaker | Author-1 |
| Date Time | 2009-01-19 17:25:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2008-95 |
| Volume (vol) | vol.108 |
| Number (no) | no.383 |
| Page | pp.79-83 |
| #Pages | 5 |
| Date of Issue | 2009-01-12 (NC) |
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