| Presentation | 2017-01-26 Fast Receptive field Inference with Sparse Fourirer Representation by using LASSO Takeshi Tanida, Hirotaka Sakamoto, Yasuhiko Igarashi, Takeshi Ideriha, Satoru Tokuda, Kota Sasaki, Izumi Ohzawa, Masato Okada, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose fast receptive eld(RF) inference. The RF describes how a neuron sums up its inputs acrossspace and time. The traditional RF estimators such as the spike-triggered average, converge slowly and often requirelarge amounts of spike data. Previous research introduce a family of prior distribution to low cost estimation, byutilizing an approach known as empirical Bayes. In this study, we estimate the accurate RF by using regressionanalysis and variable selection based on the least absolute shrinkage and selection operator (Lasso) with respect tothe 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 robusterthan 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) |
| Keyword(in English) | Receptive Field Inference, / Spike Triggered Average, / Lasso |
| Paper # | NC2016-52 |
| Date of Issue | 2017-01-19 (NC) |
| Conference Information | |
| Committee | NC / NLP |
|---|---|
| Conference Date | 2017/1/26(2days) |
| 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 |
| Chair | Shigeo Sato(Tohoku Univ.) / Hisato Fujisaka(Hiroshima City Univ.) |
| Vice Chair | Masafumi Hagiwara(Keio Univ.) / Masaharu Adachi(Tokyo Denki Univ.) |
| Secretary | Masafumi Hagiwara(Kyoto Sangyo Univ.) / Masaharu Adachi(Tokyo Inst. of Tech.) |
| Assistant | Hisanao Akima(Tohoku Univ.) / Yoshihisa Shinozawa(Keio Univ.) / Hiroyuki Asahara(Okayama Univ. of Science) / Toshihiro Tachibana(Shonan Inst. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on Nonlinear Problems |
|---|---|
| Language | JPN |
| 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 |
| 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) |
| Date | 2017-01-26 |
| Paper # | NC2016-52 |
| Volume (vol) | vol.116 |
| Number (no) | NC-424 |
| Page | pp.pp.25-30(NC), |
| #Pages | 6 |
| Date of Issue | 2017-01-19 (NC) |