Feedback inhibition and throughput properties of an integrate-and-fire-or-burst network model of retinogeniculate transmission

DOI10.1007/S10827-005-1084-6zbMath1098.92012OpenAlexW1999678808WikidataQ46679666 ScholiaQ46679666MaRDI QIDQ849486

Jeffrey R. Groff, Marco A. Huertas, Gregory D. Smith

Publication date: 31 October 2006

Published in: Journal of Computational Neuroscience (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10827-005-1084-6

zbMATH Keywords

integrate-and-firethalamusburstgeniculate nucleusperigeniculate nucleusthalamic reticularthalamocortical

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Neural biology (92C20)

Related Items (4)

The Firing of an Excitable Neuron in the Presence of Stochastic Trains of Strong Synaptic Inputs ⋮ A multivariate population density model of the dLGN/PGN relay ⋮ The role of cortical feedback in the generation of the temporal receptive field responses of lateral geniculate nucleus neurons: a computational modelling study ⋮ Neural Fields with Rebound Currents: Novel Routes to Patterning

Cites Work

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• Analysis of bursting in a thalamic neuron model
• A population density approach that facilitates large-scale modeling of neural networks: Analysis and an application in orientation tuning
• On the simulation of large populations of neurons.
• A Population Study of Integrate-and-Fire-or-Burst Neurons

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