Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation

DOI10.3934/MBE.2014.11.49zbMath1279.60086OpenAlexW2334064307WikidataQ46625911 ScholiaQ46625911MaRDI QIDQ395708

Shigeru Shinomoto, Hideaki Kim

Publication date: 30 January 2014

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

Full work available at URL: https://doi.org/10.3934/mbe.2014.11.49

zbMATH Keywords

Bayesian analysismethod of momentsfirst-passage timeneuronal modelstate-space methodinput estimationspike dataspike frequency adaptation

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of stochastic analysis (to PDEs, etc.) (60H30) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (7)

Integrated stationary Ornstein-Uhlenbeck process, and double integral processes ⋮ Successive spike times predicted by a stochastic neuronal model with a variable input signal ⋮ On a stochastic neuronal model integrating correlated inputs ⋮ Closed-form solutions for the first-passage-time problem and neuronal modeling ⋮ Fractional Ornstein-Uhlenbeck process with stochastic forcing, and its applications ⋮ Fractionally integrated Gauss-Markov processes and applications ⋮ Colored noise and a stochastic fractional model for correlated inputs and adaptation in neuronal firing

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