A multivariate population density model of the dLGN/PGN relay

DOI10.1007/s10827-006-7753-2zbMath1099.92010OpenAlexW2138402061WikidataQ47850976 ScholiaQ47850976MaRDI QIDQ849512

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/s10827-006-7753-2

zbMATH Keywords

visionburstdorsal lateral geniculate nucleus (PGN)perigeniculate nucleus (dLGN)population density modelthalamic reticular neuronthalamocortical relay neurontonic

Mathematics Subject Classification ID

Multivariate analysis (62H99) Monte Carlo methods (65C05) Neural biology (92C20) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (5)

Variable synaptic strengths controls the firing rate distribution in feedforward neural networks ⋮ A Principled Dimension-Reduction Method for the Population Density Approach to Modeling Networks of Neurons with Synaptic Dynamics ⋮ Models of cardiac excitation-contraction coupling in ventricular myocytes ⋮ Critical Analysis of Dimension Reduction by a Moment Closure Method in a Population Density Approach to Neural Network Modeling ⋮ Spike Train Statistics and Dynamics with Synaptic Input from any Renewal Process: A Population Density Approach

Cites Work

• Unnamed Item
• Feedback inhibition and throughput properties of an integrate-and-fire-or-burst network model of retinogeniculate transmission
• A population density approach that facilitates large-scale modeling of neural networks: Analysis and an application in orientation tuning
• Population density methods for large-scale modelling of neuronal networks with realistic synaptic kinetics: cutting the dimension down to size
• A Population Study of Integrate-and-Fire-or-Burst Neurons
• A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses

This page was built for publication: A multivariate population density model of the dLGN/PGN relay