MAP estimation algorithm for phase response curves based on analysis of the observation process

Keisuke Ota, Toshiaki Omori, Toru Aonishi

研究成果: Article査読

20 被引用数 (Scopus)

抄録

Many research groups have sought to measure phase response curves (PRCs) from real neurons. However, methods of estimating PRCs from noisy spike-response data have yet to be established. In this paper, we propose a Bayesian approach for estimating PRCs. First, we analytically obtain a likelihood function of the PRC from a detailed model of the observation process formulated as Langevin equations. Then we construct a maximum a posteriori (MAP) estimation algorithm based on the analytically obtained likelihood function. The MAP estimation algorithm derived here is equivalent to the spherical spin model. Moreover, we analytically calculate a marginal likelihood corresponding to the free energy of the spherical spin model, which enables us to estimate the hyper-parameters, i.e., the intensity of the Langevin force and the smoothness of the prior.

本文言語English
ページ(範囲)185-202
ページ数18
ジャーナルJournal of Computational Neuroscience
26
2
DOI
出版ステータスPublished - 2009

ASJC Scopus subject areas

  • 感覚系
  • 認知神経科学
  • 細胞および分子神経科学

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