Title

Maximum Likelihood Parameter Estimation In A Stochastic Resonate-And-Fire Neuronal Model

Keywords

Maximum likelihood; Parameter estimation; Resonate-and-fire; Simulated annealing

Abstract

Recent work has shown that resonate-and-fire model is both computationally efficient and suitable for large network simulations. In this paper, we examine the estimation problem of a resonate-and-fire model with random threshold. The model parameters are divided into two sets. The first set is associated with subthreshold behavior and can be optimized by a nonlinear least squares algorithm. The other set contains threshold and reset parameters and its estimation is formulated in terms of maximum likelihood formulation. We evaluate such a formulation with detailed Hodgkin-Huxley model data. © 2011 IEEE.

Publication Date

4-14-2011

Publication Title

2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011

Number of Pages

57-62

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCABS.2011.5729941

Socpus ID

79953827623 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79953827623

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