A numerical method for computing interval distributions for an inhomogeneous Poisson point process modified by random dead times

DOI10.1007/S00422-021-00868-8zbMath1473.62312OpenAlexW3137328828WikidataQ123025194 ScholiaQ123025194MaRDI QIDQ2036849

Adam J. Peterson

Publication date: 30 June 2021

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00422-021-00868-8

zbMATH Keywords

numerical methodsimulationPoisson point processinterval distributioninhomogeneous processrandom dead time

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Computational methods for problems pertaining to probability theory (60-08) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (1)

Damped oscillations of the probability of random events followed by absolute refractory period: exact analytical results

Uses Software

• GitHub

Cites Work

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• Probability density function of successive intervals of a nonhomogeneous Poisson process under low-frequency conditions
• Normalizing transformations for dead-time-modified Poisson counting distributions
• A nonstationary Poisson point process describes the sequence of action potentials over long time scales in lateral-superior-olive auditory neurons
• Output Dead-Time in Point Processes
• On the extraction of the signal-excitation function from a non-Poisson cochlear neural spike train
• Algorithms for removing recovery-related distortion from auditory-nerve discharge patterns
• An Adjustment to the Time-Rescaling Method for Application to Short-Trial Spike Train Data
• The Effect of a Refractory Period on the Power Spectrum of Neuronal Discharge
• Quantum metal optics

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