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Paper Abstract and Keywords
Presentation 2025-12-12 13:30
Effects of Refractory Periods on the Accuracy and Output Spike Counts of SNNs
Kaoru Yokobori, Hiroki Tanioka, Tetsushi Ueta (Tokushima Univ.) NLP2025-77
Abstract (in Japanese) (See Japanese page) 
(in English) Artificial Neural Networks (ANNs) are neural networks
consisting of formal neurons and have developed rapidly in recent
years.
However, ANNs have the problem which large ones need large energy.
Spiking Neural Networks (SNNs), which model the timing of spikes and
changes in membrane potential, have attracted attention as a potential solution.
In this study, we implemented relative refractory periods in SNNs and
evaluated them in terms of output-layer average spike count and accuracy to investigate methods for reducing energy consumption while
minimizing accuracy decline.
Experimental results showed that, compared to SNN without refractory
periods, accuracy decreased by about 4 percentage points, while the
average number of spikes in the output-layer was reduced to about
one-quarter.
Keyword (in Japanese) (See Japanese page) 
(in English) Spiking Neural Network / Refractory Period / MNIST / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 283, NLP2025-77, pp. 117-122, Dec. 2025.
Paper # NLP2025-77 
Date of Issue 2025-12-04 (NLP) 
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 NLP2025-77

Conference Information
Committee NLP  
Conference Date 2025-12-11 - 2025-12-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Kochi Castle Museum of History 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear problem, etc 
Paper Information
Registration To NLP 
Conference Code 2025-12-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Effects of Refractory Periods on the Accuracy and Output Spike Counts of SNNs 
Sub Title (in English)  
Keyword(1) Spiking Neural Network  
Keyword(2) Refractory Period  
Keyword(3) MNIST  
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1st Author's Name Kaoru Yokobori  
1st Author's Affiliation Tokushima University (Tokushima Univ.)
2nd Author's Name Hiroki Tanioka  
2nd Author's Affiliation Tokushima University (Tokushima Univ.)
3rd Author's Name Tetsushi Ueta  
3rd Author's Affiliation Tokushima University (Tokushima Univ.)
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Speaker Author-1 
Date Time 2025-12-12 13:30:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # NLP2025-77 
Volume (vol) vol.125 
Number (no) no.283 
Page pp.117-122 
#Pages 6 
Date of Issue 2025-12-04 (NLP) 

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