Successive spike times predicted by a stochastic neuronal model with a variable input signal

DOI10.3934/mbe.2016003zbMath1351.92008OpenAlexW2466164367WikidataQ50660745 ScholiaQ50660745MaRDI QIDQ335076

Giuseppe D'Onofrio, Enrica Pirozzi

Publication date: 2 November 2016

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

Full work available at URL: https://doi.org/10.3934/mbe.2016003

zbMATH Keywords

Ornstein-Uhlenbeck processfirst passage timeGauss-Markov processstochastic neuronal model

Mathematics Subject Classification ID

Neural biology (92C20) Diffusion processes (60J60) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70)

Related Items (12)

Integrated stationary Ornstein-Uhlenbeck process, and double integral processes ⋮ On two diffusion neuronal models with multiplicative noise: The mean first-passage time properties ⋮ Generating random variates from PDF of Gauss-Markov processes with a reflecting boundary ⋮ Mean first-passage time in a delayed tristable system driven by correlated multiplicative and additive white noises ⋮ Asymptotic results for first-passage times of some exponential processes ⋮ Fractional Ornstein-Uhlenbeck process with stochastic forcing, and its applications ⋮ Two-boundary first exit time of Gauss-Markov processes for stochastic modeling of acto-myosin dynamics ⋮ Simulation of sample paths for Gauss-Markov processes in the presence of a reflecting boundary ⋮ Asymptotics of two-boundary first-exit-time densities for Gauss-Markov processes ⋮ Colored noise and a stochastic fractional model for correlated inputs and adaptation in neuronal firing ⋮ Inference on the effect of non homogeneous inputs in Ornstein-Uhlenbeck neuronal modeling ⋮ On the construction of a special class of time-inhomogeneous diffusion processes

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