Diffusion approximation and first passage time problem for a model neuron. II. Outline of a computation method

DOI10.1016/0025-5564(83)90026-3zbMath0525.92006OpenAlexW1999319504MaRDI QIDQ593999

Sumit K. Garg

Publication date: 1983

Published in: Mathematical Biosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0025-5564(83)90026-3

zbMATH Keywords

Brownian bridgediffusion approximationsneurobiologytransformation techniquefirst passage time problemmodel neuronnonstationary effects

Mathematics Subject Classification ID

Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70) Computational methods for problems pertaining to biology (92-08) Physiological, cellular and medical topics (92Cxx)

Related Items

Diffusion approximation of the neuronal model with synaptic reversal potentials ⋮ Diffusion approximation and first-passage-time problem for a model neuron. III: A birth-and-death process approach ⋮ On the comparison of Feller and Ornstein-Uhlenbeck models for neural activity ⋮ A new Gompertz-type diffusion process with application to random growth ⋮ Delayed-exponential approximation of a linear homogeneous diffusion model of neuron ⋮ EVALUATION OF FIRST PASSAGE TIME DENSITIES FOR DIFFUSION PROCESSE ⋮ The effect of a random initial value in neural first-passage-time models ⋮ Stochastic Integrate and Fire Models: A Review on Mathematical Methods and Their Applications ⋮ Inferring the effect of therapy on tumors showing stochastic Gompertzian growth ⋮ On the therapy effect for a stochastic growth Gompertz-type model ⋮ An R package for an efficient approximation of first-passage-time densities for diffusion processes based on the FPTL function ⋮ A stochastic model related to the Richards-type growth curve. Estimation by means of simulated annealing and variable neighborhood search ⋮ Inference for stochastic neuronal models ⋮ A general nonstationary diffusion model for two-choice decision-making ⋮ Inference for stochastic neuronal models ⋮ First-passage-time location function: application to determine first-passage-time densities in diffusion processes ⋮ Stochastic dynamic models of response time and accuracy: A foundation primer

Cites Work

• Unnamed Item
• Unnamed Item
• The Ornstein-Uhlenbeck process as a model for neuronal activity. I. Mean and variance of the firing time
• On a weakly singular Volterra integral equation
• A note on modeling accumulation of information when the rate of accumulation changes over time
• On the transformation of diffusion processes into the Wiener process
• On the moments of the firing interval of the diffusion approximated model neuron
• MEAN VARIANCE AND SKEWNESS OF THE FIRST PASSAGE TIME FOR THE ORNSTEIN-UHLENBECK PROCESS
• A tandem random walk model for psychological discrimination
• FIRST PASSAGE TIME PROBLEMS AND SOME RELATED COMPUTATIONAL METHODS
• The minimum of a stationary Markov process superimposed on a U-shaped trend
• Diffusion approximation and first passage time problem for a model neuron
• Boundary-crossing probabilities for the Brownian motion and Poisson processes and techniques for computing the power of the Kolmogorov-Smirnov test