Identification Of A Stochastic Resonate-And-Fire Neuronal Model Via Nonlinear Least Squares And Maximum Likelihood Estimation

Keywords

Maximum likelihood; Neuronal model; Parameter estimation; Resonate-and-fire; Simulated annealing; Stochastic threshold

Abstract

Recent work has shown that the resonate-and-fire neuronal model is both computationally efficient and suitable for large network simulations. In this paper, we examine the estimation problem of a resonate-and-fire neuronal model with stochastic firing threshold. The model parameters are divided into two sets. The first set is associated with the subthreshold behaviour and can be estimated by a least squares algorithm, while the second set includes parameters associated with the firing threshold and its identification is formulated as a maximum likelihood estimation problem. The latter is in turn solved by a simulated annealing approach that avoids local optima. The proposed identification approach is evaluated using both simulated and in-vitro data, which shows a good match between prediction by identified model and the actual data, concluding the efficiency and accuracy of the proposed approach.

Publication Date

1-1-2017

Publication Title

International Journal of Modelling, Identification and Control

Volume

28

Issue

3

Number of Pages

221-231

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1504/IJMIC.2017.086565

Socpus ID

85029547675 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85029547675

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