Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing

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Standard

Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing. / Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë.

I: Journal of Mathematical Neuroscience, Bind 4, Nr. 4, 4, 2014.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Iolov, A, Ditlevsen, S & Longtin, A 2014, 'Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing', Journal of Mathematical Neuroscience, bind 4, nr. 4, 4. https://doi.org/10.1186/2190-8567-4-4

APA

Iolov, A., Ditlevsen, S., & Longtin, A. (2014). Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing. Journal of Mathematical Neuroscience, 4(4), [4]. https://doi.org/10.1186/2190-8567-4-4

Vancouver

Iolov A, Ditlevsen S, Longtin A. Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing. Journal of Mathematical Neuroscience. 2014;4(4). 4. https://doi.org/10.1186/2190-8567-4-4

Author

Iolov, Alexandre ; Ditlevsen, Susanne ; Longtin, Andrë. / Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing. I: Journal of Mathematical Neuroscience. 2014 ; Bind 4, Nr. 4.

Bibtex

@article{d60b28073c3a432f9068ac043e74352f,
title = "Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing",
abstract = "Analysis of sinusoidal noisy leaky integrate-and-fire models and comparison with experimental data are important to understand the neural code and neural synchronization and rhythms. In this paper, we propose two methods to estimate input parameters using interspike interval data only. One is based on numerical solutions of the Fokker–Planck equation, and the other is based on an integral equation, which is fulfilled by the interspike interval probability density. This generalizes previous methods tailored to stationary data to the case of time-dependent input. The main contribution is a binning method to circumvent the problems of nonstationarity, and an easy-to-implement initializer for the numerical procedures. The methods are compared on simulated data",
author = "Alexandre Iolov and Susanne Ditlevsen and Andr{\"e} Longtin",
year = "2014",
doi = "10.1186/2190-8567-4-4",
language = "English",
volume = "4",
journal = "Journal of Mathematical Neuroscience",
issn = "2190-8567",
publisher = "SpringerOpen",
number = "4",

}

RIS

TY - JOUR

T1 - Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing

AU - Iolov, Alexandre

AU - Ditlevsen, Susanne

AU - Longtin, Andrë

PY - 2014

Y1 - 2014

N2 - Analysis of sinusoidal noisy leaky integrate-and-fire models and comparison with experimental data are important to understand the neural code and neural synchronization and rhythms. In this paper, we propose two methods to estimate input parameters using interspike interval data only. One is based on numerical solutions of the Fokker–Planck equation, and the other is based on an integral equation, which is fulfilled by the interspike interval probability density. This generalizes previous methods tailored to stationary data to the case of time-dependent input. The main contribution is a binning method to circumvent the problems of nonstationarity, and an easy-to-implement initializer for the numerical procedures. The methods are compared on simulated data

AB - Analysis of sinusoidal noisy leaky integrate-and-fire models and comparison with experimental data are important to understand the neural code and neural synchronization and rhythms. In this paper, we propose two methods to estimate input parameters using interspike interval data only. One is based on numerical solutions of the Fokker–Planck equation, and the other is based on an integral equation, which is fulfilled by the interspike interval probability density. This generalizes previous methods tailored to stationary data to the case of time-dependent input. The main contribution is a binning method to circumvent the problems of nonstationarity, and an easy-to-implement initializer for the numerical procedures. The methods are compared on simulated data

U2 - 10.1186/2190-8567-4-4

DO - 10.1186/2190-8567-4-4

M3 - Journal article

C2 - 24742022

VL - 4

JO - Journal of Mathematical Neuroscience

JF - Journal of Mathematical Neuroscience

SN - 2190-8567

IS - 4

M1 - 4

ER -

ID: 122433233