| Presentation | 2009-01-20 Which model can properly describe dynamics and smoothness of firing rate? Ken TAKIYAMA, Kentaro KATAHIRA, Masato OKADA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We construct the algorithm using belief propagation(BP), which algorithm simultaneously estimates firing rate and calculates marginal likelihood. Our algorithm can determine the degree and the form of the firing rate smoothness based on hyperparameter estimation and model selection by maximizing the marginal likelihood. Prior distribution is Line process model, Gauss model, or Cauchy model. We discuss which model is appropriate to describe the firing rate and whether appropriate model changes or not depending on the functional form of the firing rate. We conduct two firing rate estimation experiments: the first firing rate evolves smoothly, but the second firing rate involves discontinuity. The second experiment is assumed that the extrinsic stimuli whose timings are entirely-unknown are added to the neuron. The Line process model shows the largest mariginal likelihood value of the three models in both experiments. The Line process model also being able to estimate the unknown stimulus timings, we show the effectivness of the Line process model in the firing estimation issue. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Firing rate estimation / Belief Propagation / Bayesian estimation / Line process / Gaussian graphical model / Hyperparameter estimation / Model selection |
| Paper # | NC2008-98 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2009/1/12(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| 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) | Which model can properly describe dynamics and smoothness of firing rate? |
| Sub Title (in English) | |
| Keyword(1) | Firing rate estimation |
| Keyword(2) | Belief Propagation |
| Keyword(3) | Bayesian estimation |
| Keyword(4) | Line process |
| Keyword(5) | Gaussian graphical model |
| Keyword(6) | Hyperparameter estimation |
| Keyword(7) | Model selection |
| 1st Author's Name | Ken TAKIYAMA |
| 1st Author's Affiliation | Graduate School of Frontier Sciences, The University of Tokyo() |
| 2nd Author's Name | Kentaro KATAHIRA |
| 2nd Author's Affiliation | Graduate School of Frontier Sciences, The University of Tokyo:Brain Science Institute, RIKEN |
| 3rd Author's Name | Masato OKADA |
| 3rd Author's Affiliation | Graduate School of Frontier Sciences, The University of Tokyo:Brain Science Institute, RIKEN |
| Date | 2009-01-20 |
| Paper # | NC2008-98 |
| Volume (vol) | vol.108 |
| Number (no) | 383 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |