Gaussian Process Approach to Spiking Neurons for Inhomogeneous Poisson Inputs

DOI10.1162/089976601317098529zbMath0984.92006DBLPjournals/neco/AmemoriI01OpenAlexW2109679791WikidataQ77160507 ScholiaQ77160507MaRDI QIDQ2770867

Ken-Ichi Amemori, Shin Ishii

Publication date: 14 May 2002

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/089976601317098529

zbMATH Keywords

Poisson processfiring probability densitymembrane potential densitystochastic spiking neuron model

Mathematics Subject Classification ID

Neural biology (92C20) Signal detection and filtering (aspects of stochastic processes) (60G35) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70)

Related Items (6)

Stochastic Properties of Coincidence-Detector Neural Cells ⋮ A First-Order Nonhomogeneous Markov Model for the Response of Spiking Neurons Stimulated by Small Phase-Continuous Signals ⋮ Gauss-diffusion processes for modeling the dynamics of a couple of interacting neurons ⋮ Nonstationary shot noise modeling of neuron membrane potentials by closed-form moments and Gram-Charlier expansions ⋮ NOISE-ENHANCED TRANSMISSION OF TIME-MODULATED NEUROTRANSMITTER RANDOM POINT TRAINS IN A NOISY NEURON ⋮ Stochastic modeling of the firing activity of coupled neurons periodically driven

Cites Work

• Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.
• First-passage-time density and moments of the ornstein-uhlenbeck process
• Dynamics of a recurrent network of spiking neurons before and following learning
• Rapid state switching in balanced cortical network models
• Mathematical Analysis of Random Noise

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