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Paper Abstract and Keywords
Presentation 2026-06-12 13:45
Estimation of Optimal Threshold Based on Stability and Correction Conditions in the Saito-Jin'no Learning Method
Kannei Nakamura, Kenya Jin'no (Tokyo City Univ.) NLP2026-22 CCS2026-22
Abstract (in Japanese) (See Japanese page) 
(in English) The Saito--Jin'no learning method is an associative memory derived from the Hopfield neural network, which can store desired patterns exactly by means of binary hysteresis and self-coupling coefficients. This study focuses on the hysteresis threshold, which significantly affects recall performance, and proposes a theoretical method to derive a per-neuron optimal threshold based on stability and correction conditions for each neuron. Through simulations using random images and CIFAR-10, we compared the theoretical predictions with measured values, and evaluated the recall accuracy of the proposed method against a conventional approach that uses a uniform threshold across all neurons. The results show that, in the regime where the stored patterns are linearly separable, the proposed method achieves higher recall accuracy than the conventional approach.
Keyword (in Japanese) (See Japanese page) 
(in English) associative memory / recurrent neural networks / hysteresis / / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 68, NLP2026-22, pp. 115-118, June 2026.
Paper # NLP2026-22 
Date of Issue 2026-06-04 (NLP, CCS) 
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 NLP2026-22 CCS2026-22

Conference Information
Committee CCS NLP  
Conference Date 2026-06-11 - 2026-06-12 
Place (in Japanese) (See Japanese page) 
Place (in English) I-site Namba 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear Problems, Complex Communication Sciences, etc. 
Paper Information
Registration To NLP 
Conference Code 2026-06-CCS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimation of Optimal Threshold Based on Stability and Correction Conditions in the Saito-Jin'no Learning Method 
Sub Title (in English)  
Keyword(1) associative memory  
Keyword(2) recurrent neural networks  
Keyword(3) hysteresis  
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1st Author's Name Kannei Nakamura  
1st Author's Affiliation Tokyo City University (Tokyo City Univ.)
2nd Author's Name Kenya Jin'no  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ.)
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Speaker Author-1 
Date Time 2026-06-12 13:45:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2026-22, CCS2026-22 
Volume (vol) vol.126 
Number (no) no.68(NLP), no.69(CCS) 
Page pp.115-118 
#Pages 4 
Date of Issue 2026-06-04 (NLP, CCS) 

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