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| Presentation | 2015-11-10 15:00 Segmental Bayesian estimation of neuronal parameters from spike trains Isao Tokuda, Huu Hoang (Ritsumeikan Univ.)  CCS2015-63 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Multi-electrode recording is now a common technique to simultaneously collect neuronal spike data of a population of the neurons in a brain region, and thus allows exploring various underlying functions of the brain. Computational modeling usually emerges when the parameters of interest cannot be directly measured by the experiments. However, the inverse problem of estimating parameters from spike trains is severely ill-posed due to the huge mismatch in the system complexity between the brain and the model, and thus needs a stochastic approach to find most likely solutions among many possible ones. Since the brain typically exhibits complicated dynamics that is difficult for the model to reproduce, the modeling errors are inevitable. In the present thesis, we introduce a novel methodology based on the Bayesian inference framework to overcome that challenging issue. The experimental spike data is fractioned into short time segments and the model parameters are estimated segment by segment in the constraint that the segmental estimates are fluctuated around the neuronal estimates. By relaxing the parameter search, the segmental Bayes has been hypothesized to compensate the modeling errors and thus improve the estimation accuracy. The performance evaluation on experimental data indicated that the segmental Bayes outperforms the conventional Bayes and the minimum error method by minimizing the fitting errors in the feature space. It also had a strong robustness against non-stationarity of the spike data. This work has been collaboration with Dr. Okito Yamashita, Dr. Masa-aki Sato, Dr. Mitsuo Kawato, and Dr. Keisuke Toyama. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Multi-electrode recording / Bayesian Estimation / Parameter Estimation / Segmentation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 115, no. 300, CCS2015-63, pp. 99-102, Nov. 2015. | |
| Paper # | CCS2015-63 | |
| Date of Issue | 2015-11-02 (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) | |
| Download PDF | CCS2015-63 |
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| Conference Information | |
| Committee | CCS |
| Conference Date | 2015-11-09 - 2015-11-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Inamori Foundation Memorial Building, Kyoto Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Interaction and Communication, etc. |
| Paper Information | |
| Registration To | CCS |
| Conference Code | 2015-11-CCS |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Segmental Bayesian estimation of neuronal parameters from spike trains |
| Sub Title (in English) | |
| Keyword(1) | Multi-electrode recording |
| Keyword(2) | Bayesian Estimation |
| Keyword(3) | Parameter Estimation |
| Keyword(4) | Segmentation |
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| 1st Author's Name | Isao Tokuda |
| 1st Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 2nd Author's Name | Huu Hoang |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2015-11-10 15:00:00 |
| Presentation Time | 25 minutes |
| Registration for | CCS |
| Paper # | CCS2015-63 |
| Volume (vol) | vol.115 |
| Number (no) | no.300 |
| Page | pp.99-102 |
| #Pages | 4 |
| Date of Issue | 2015-11-02 (CCS) |
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