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Journal of Computational Neuroscience

Erschienen in:

01.04.2009

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

verfasst von: Keisuke Ota, Toshiaki Omori, Toru Aonishi

Erschienen in: Journal of Computational Neuroscience | Ausgabe 2/2009

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Abstract

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.

Vorheriger Artikel The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states: II. Network and glial dynamics

Nächster Artikel Contextual modulation of V1 receptive fields depends on their spatial symmetry

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Titel: MAP estimation algorithm for phase response curves based on analysis of the observation process
verfasst von: Keisuke Ota
Toshiaki Omori
Toru Aonishi
Publikationsdatum: 01.04.2009
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 2/2009
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-008-0104-8

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