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

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4 Author(s)
Jun Chen ; Dept. of Electr. Eng. & Comput. Sci., Univ. of Central Florida, Orlando, FL, USA ; Suarez, J. ; Molnar, P. ; Behal, A.

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.

Published in:

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

Date of Conference:

3-5 Feb. 2011