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Paper Abstract and Keywords
Presentation 2026-03-17 14:40
An Actor-Critic model using LSM with SAM Spiking Neural Network
Minoru Motoki, Heitaro Hirooka (Kumamoto KOSEN) NC2025-109
Abstract (in Japanese) (See Japanese page) 
(in English) This research proposes an Actor–Critic model using an LSM with a SAM spiking neural network and reports simulation results on a goal-reaching task. We have developed the SAM spiking neural network, which has advantages such as being hardware-implementable and low in power consumption, in order to realize on-device, trainable AI systems for various industries. Conventional models have a problem regarding feature representation in their spike trains. Therefore, in this research, we improved the feature representation capability by using high-dimensional dynamic feature extraction. We also evaluated the proposed SAM-LSM-RL model using a goal-reaching task, and as a result, the proposed model achieved a success rate comparable to that of the MLP-RL model. These findings suggest that increasing the resolution of feature extraction facilitates solving continuous-valued RL tasks.
Keyword (in Japanese) (See Japanese page) 
(in English) Spiking neuron / Liquid State Machine / Reservoir / SAM-SNN / Reinforcement Learning / Actor-Critic / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 413, NC2025-109, pp. 95-99, March 2026.
Paper # NC2025-109 
Date of Issue 2026-03-09 (NC) 
ISSN 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 NC2025-109

Conference Information
Committee NC MBE  
Conference Date 2026-03-16 - 2026-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NC 
Conference Code 2026-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Actor-Critic model using LSM with SAM Spiking Neural Network 
Sub Title (in English)  
Keyword(1) Spiking neuron  
Keyword(2) Liquid State Machine  
Keyword(3) Reservoir  
Keyword(4) SAM-SNN  
Keyword(5) Reinforcement Learning  
Keyword(6) Actor-Critic  
Keyword(7)  
Keyword(8)  
1st Author's Name Minoru Motoki  
1st Author's Affiliation National Institute of Technology, Kumamoto College (Kumamoto KOSEN)
2nd Author's Name Heitaro Hirooka  
2nd Author's Affiliation National Institute of Technology, Kumamoto College (Kumamoto KOSEN)
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Speaker Author-1 
Date Time 2026-03-17 14:40:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2025-109 
Volume (vol) vol.125 
Number (no) no.413 
Page pp.95-99 
#Pages 5 
Date of Issue 2026-03-09 (NC) 

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