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Paper Abstract and Keywords
Presentation 2022-08-05 10:30
Detecting causality for spike trains based on reconstructing dynamical system from inter-spike intervals
Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) CCS2022-36
Abstract (in Japanese) (See Japanese page) 
(in English) In this report, by modifying a nonlinear method of detecting causality, we propose a method of detecting causality for point processes, such as spike trains, based on nonlinear dynamical systems theory. We modified a previous method of detecting causality based on the accuracy of mutual prediction using information on attractors reconstructed from observed time series through the time-delay coordinate system by applying the possibility of reconstructing dynamical systems from the inter-spike intervals and by considering the firing times. We also used twin surrogate data for significance test of prediction accuracy. We applied the proposed method to spike trains of two neurons generated from a mathematical neuron model and investigated its effectiveness. As a result, we confirmed that the proposed method correctly detects causality when neurons are bidirectionally or unidirectionally coupled.
Keyword (in Japanese) (See Japanese page) 
(in English) Nonlinear time series analysis / Causality / Point process / Spike train / Twin surrogates / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 145, CCS2022-36, pp. 48-53, Aug. 2022.
Paper # CCS2022-36 
Date of Issue 2022-07-28 (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 CCS2022-36

Conference Information
Committee IN CCS  
Conference Date 2022-08-04 - 2022-08-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido University(Centennial Hall) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network Science, Future Network, Cloud/SDN/Virtualization, Contents Delivery/Contents Exchange, and others 
Paper Information
Registration To CCS 
Conference Code 2022-08-IN-CCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Detecting causality for spike trains based on reconstructing dynamical system from inter-spike intervals 
Sub Title (in English)  
Keyword(1) Nonlinear time series analysis  
Keyword(2) Causality  
Keyword(3) Point process  
Keyword(4) Spike train  
Keyword(5) Twin surrogates  
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Keyword(7)  
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1st Author's Name Kazuya Sawada  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Yutaka Shimada  
2nd Author's Affiliation Saitama University (Saitama Univ.)
3rd Author's Name Tohru Ikeguchi  
3rd Author's Affiliation Tokyo University of Science (TUS)
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Speaker Author-1 
Date Time 2022-08-05 10:30:00 
Presentation Time 20 minutes 
Registration for CCS 
Paper # CCS2022-36 
Volume (vol) vol.122 
Number (no) no.145 
Page pp.48-53 
#Pages 6 
Date of Issue 2022-07-28 (CCS) 

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