Presentation | 2007-10-18 Variational Bayesian Hidden Markov Models for extracting spatiotemporal spike pattern Kentaro KATAHIRA, Jun NISHIKAWA, Kazuo OKANOYA, Masato OKADA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Hidden Markov Model (HMM) is used to extracting spatio-temporal pattern from spikes recorded by multielectrode. The EM algorithm that archieves maximum likelihood estimation is used to train HMM in that context. Therefore the previous method has problems of overlearning, and the number of hidden states must be determined by trail-and-error. To overcome those problems, we applied Variational Byases (VB) method to train the model. Our method can determine the number of states automatically and avoid overlearning. We performed an experiment on synthetic data and a real neural data from nucleus HVC of Bengalese finch during listening to own song. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Maltielectrode spike trains / Variational Bayes / Hidden Markov Model / HVC |
Paper # | NC2007-34 |
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Committee | NC |
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Conference Date | 2007/10/11(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Variational Bayesian Hidden Markov Models for extracting spatiotemporal spike pattern |
Sub Title (in English) | |
Keyword(1) | Maltielectrode spike trains |
Keyword(2) | Variational Bayes |
Keyword(3) | Hidden Markov Model |
Keyword(4) | HVC |
1st Author's Name | Kentaro KATAHIRA |
1st Author's Affiliation | Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute() |
2nd Author's Name | Jun NISHIKAWA |
2nd Author's Affiliation | RIKEN Brain Science Institute |
3rd Author's Name | Kazuo OKANOYA |
3rd Author's Affiliation | RIKEN Brain Science Institute |
4th Author's Name | Masato OKADA |
4th Author's Affiliation | Graduate School of Frontier Sciences, The University of Tokyo:RIKEN Brain Science Institute |
Date | 2007-10-18 |
Paper # | NC2007-34 |
Volume (vol) | vol.107 |
Number (no) | 263 |
Page | pp.pp.- |
#Pages | 6 |
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