MAP estimation algorithm for phase response curves based on analysis of the observation process.
J Comput Neurosci
; 26(2): 185-202, 2009 Apr.
Article
em En
| MEDLINE
| ID: mdl-18751879
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Neurônios
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article