On the classification of experimental data modeled via a stochastic leaky integrate and fire model through boundary values

DOI10.1007/s11538-006-9107-7zbMath1334.92080OpenAlexW2117244881WikidataQ33265607 ScholiaQ33265607MaRDI QIDQ263681

Laura Sacerdote, Alessandro E. P. Villa, Cristina Zucca

Publication date: 5 April 2016

Published in: Bulletin of Mathematical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11538-006-9107-7

zbMATH Keywords

gamma distributionFano factorinterspike timesinverse first passage time problemleaky integrate and fireneuronOrnstein-Uhlenbeck

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Neural biology (92C20) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Related Items (6)

On the inverse first-passage-time problem for a Wiener process ⋮ The gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model ⋮ Stochastic Integrate and Fire Models: A Review on Mathematical Methods and Their Applications ⋮ The effects of background noise on a biophysical model of olfactory bulb mitral cells ⋮ Linear programming and the inverse method of images ⋮ The inverse first passage time method for a two dimensional Ornstein Uhlenbeck process with neuronal application

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Integrate-and-fire models with nonlinear leakage
• Topological and phenomenological classification of bursting oscillations
• A first-passage-time analysis of the periodically forced noisy leaky integrate-and-fire model
• A spatial stochastic neuronal model with Ornstein-Uhlenbeck input current
• On the parameter estimation for diffusion models of single neuron's activities. I: Application to spontaneous activities of mesencephalic reticular formation cells in sleep and waking states
• On the comparison of Feller and Ornstein-Uhlenbeck models for neural activity
• Dynamical cell assembly hypothesis -- Theoretical possibility of spatio-temporal coding in the cortex
• Les fonctions aléatoires du type de Markoff associees à certaines équations linéaires aux dérivées partielles du type parabolique
• Estimation of the input parameters in the Feller neuronal model
• Introduction to Theoretical Neurobiology
• Nonlinear Dynamics of Point Process Systems and Data
• Analytic Expressions for Rate and CV of a Type I Neuron Driven by White Gaussian Noise
• Mean Instantaneous Firing Frequency Is Always Higher Than the Firing Rate
• Diffusion approximation and first passage time problem for a model neuron

This page was built for publication: On the classification of experimental data modeled via a stochastic leaky integrate and fire model through boundary values