Work

Maximum likelihood parameter estimation in a stochastic resonate-and-fire neuronal model

Year: 2011

Type: conference-paper

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.

Source: IEEE International Conference on Computational Advances in Bio and Medical Sciences

DOI: 10.1109/iccabs.2011.5729941

Authors: Jun Chen, Jose Suarez, Peter Molnar, Aman Behal

Institutions: University of Central Florida, University of Sopron

Language: English

Topic: Neural dynamics and brain function

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W2145573148

FWCI: 2.963

Cites: 20

Cited by: 13

Related to: 10