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Paper Abstract and Keywords
Presentation 2017-01-26 16:00
Fast Receptive field Inference with Sparse Fourirer Representation by using LASSO
Takeshi Tanida, Hirotaka Sakamoto, Yasuhiko Igarashi, Takeshi Ideriha, Satoru Tokuda (Univ. of Tokyo), Kota Sasaki, Izumi Ohzawa (Osaka Univ.), Masato Okada (Univ. of Tokyo/RIKEN) NC2016-52
Abstract (in Japanese) (See Japanese page) 
(in English) We propose fast receptive eld(RF) inference. The RF describes how a neuron sums up its inputs across
space and time. The traditional RF estimators such as the spike-triggered average, converge slowly and often require
large amounts of spike data. Previous research introduce a family of prior distribution to low cost estimation, by
utilizing an approach known as empirical Bayes. In this study, we estimate the accurate RF by using regression
analysis and variable selection based on the least absolute shrinkage and selection operator (Lasso) with respect to
the Fourier coefficients of the STA data. On the assumption that the RF has sparsity in the Fourier representation,
the Lasso gives the denoised RF estimator. We compare our proposed method with the previous Bayesian methods,
in the experiments of RF estimation by using arti cial and real data sets. We show that our method is robuster
than the previous method and can estimate fast and accurately, in the case that the observed spike data are few.
Keyword (in Japanese) (See Japanese page) 
(in English) Receptive Field Inference, / Spike Triggered Average, / Lasso / / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 424, NC2016-52, pp. 25-30, Jan. 2017.
Paper # NC2016-52 
Date of Issue 2017-01-19 (NC) 
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 NC2016-52

Conference Information
Committee NC NLP  
Conference Date 2017-01-26 - 2017-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Kitakyushu Foundation for the Advanement of Ind. Sci. and Tech. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Implementation of Neuro Computing,Analysis and Modeling of Human Science, etc 
Paper Information
Registration To NC 
Conference Code 2017-01-NC-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fast Receptive field Inference with Sparse Fourirer Representation by using LASSO 
Sub Title (in English)  
Keyword(1) Receptive Field Inference,  
Keyword(2) Spike Triggered Average,  
Keyword(3) Lasso  
Keyword(4)  
Keyword(5)  
Keyword(6)  
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1st Author's Name Takeshi Tanida  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Hirotaka Sakamoto  
2nd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
3rd Author's Name Yasuhiko Igarashi  
3rd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
4th Author's Name Takeshi Ideriha  
4th Author's Affiliation The University of Tokyo (Univ. of Tokyo)
5th Author's Name Satoru Tokuda  
5th Author's Affiliation The University of Tokyo (Univ. of Tokyo)
6th Author's Name Kota Sasaki  
6th Author's Affiliation Osaka University (Osaka Univ.)
7th Author's Name Izumi Ohzawa  
7th Author's Affiliation Osaka University (Osaka Univ.)
8th Author's Name Masato Okada  
8th Author's Affiliation The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN)
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Speaker Author-1 
Date Time 2017-01-26 16:00:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2016-52 
Volume (vol) vol.116 
Number (no) no.424 
Page pp.25-30 
#Pages 6 
Date of Issue 2017-01-19 (NC) 

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