Paper Abstract and Keywords |
Presentation |
2018-06-08 09:45
Application of tensor decomposition to chaotic itinerancy time series Takahiro Arai, Toshio Aoyagi (Kyoto Univ.) NLP2018-29 CCS2018-2 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Tensor decomposition is a typical method for analyzing resting-state BOLD signals. This method can decompose the observed spatiotemporal brain activity into time-independent spatial patterns with their time-dependent weights. Brain activity can be then regarded as transition among these spatial patterns. Some experimental results suggest that brain activity is chaotic itinerancy, which means chaotic transition among attractor-ruins. Tensor decomposition is expected to describe brain activity as transition among attractor-ruins, which correspond to the time-independent spatial patterns. From experimental time series data, however, there is no way to determine whether the dynamics exhibit chaotic itinerancy or not. Hence, in search of properties indicating chaotic itinerancy, we apply tensor decomposition to multi-dimensional time-series data generated from a simple mathematical model and examine the resultant tensor decomposition. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Chaotic itinerancy / Tensor decomposition / Dynamical system / Milnor attractor / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 76, CCS2018-2, pp. 7-12, June 2018. |
Paper # |
CCS2018-2 |
Date of Issue |
2018-06-01 (NLP, CCS) |
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) |
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NLP2018-29 CCS2018-2 |
Conference Information |
Committee |
NLP CCS |
Conference Date |
2018-06-08 - 2018-06-10 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Kyoto Terrsa |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Synchronization, Networks, etc |
Paper Information |
Registration To |
CCS |
Conference Code |
2018-06-NLP-CCS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Application of tensor decomposition to chaotic itinerancy time series |
Sub Title (in English) |
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Keyword(1) |
Chaotic itinerancy |
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Tensor decomposition |
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Dynamical system |
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Milnor attractor |
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1st Author's Name |
Takahiro Arai |
1st Author's Affiliation |
Kyoto University (Kyoto Univ.) |
2nd Author's Name |
Toshio Aoyagi |
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Kyoto University (Kyoto Univ.) |
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Speaker |
Author-1 |
Date Time |
2018-06-08 09:45:00 |
Presentation Time |
25 minutes |
Registration for |
CCS |
Paper # |
NLP2018-29, CCS2018-2 |
Volume (vol) |
vol.118 |
Number (no) |
no.75(NLP), no.76(CCS) |
Page |
pp.7-12 |
#Pages |
6 |
Date of Issue |
2018-06-01 (NLP, CCS) |
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