Wave propagation mediated by GABA(B) synapse and rebound excitation in an inhibitory network: A reduced model approach

Zhixiong Chen; Bard Ermentrout; Xiao Jing Wang

doi:10.1023/A:1008878310900

Wave propagation mediated by GABA(B) synapse and rebound excitation in an inhibitory network: A reduced model approach

Zhixiong Chen, Bard Ermentrout, Xiao Jing Wang

Neural Science

Research output: Contribution to journal › Article › peer-review

Abstract

A reduction method is used to analyze a spatially structured network model of inhibitory neurons. This network model displays wave propagation of postinhibitory rebound activity, which depends on GABA(B) synaptic interactions among the neurons. The reduced model allows explicit solutions for the wavefronts and their velocity as a function of various parameters, such as the synaptic coupling strength. These predictions are shown to agree well with the numerical simulations of the conductance-based biophysical model.

Original language	English (US)
Pages (from-to)	53-69
Number of pages	17
Journal	Journal of Computational Neuroscience
Volume	5
Issue number	1
DOIs	https://doi.org/10.1023/A:1008878310900
State	Published - 1998

Keywords

GABA(B) receptor
Sleep spindle rhythm
Thalamus
Wavefront velocity

ASJC Scopus subject areas

Sensory Systems
Cognitive Neuroscience
Cellular and Molecular Neuroscience

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10.1023/A:1008878310900

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title = "Wave propagation mediated by GABA(B) synapse and rebound excitation in an inhibitory network: A reduced model approach",

abstract = "A reduction method is used to analyze a spatially structured network model of inhibitory neurons. This network model displays wave propagation of postinhibitory rebound activity, which depends on GABA(B) synaptic interactions among the neurons. The reduced model allows explicit solutions for the wavefronts and their velocity as a function of various parameters, such as the synaptic coupling strength. These predictions are shown to agree well with the numerical simulations of the conductance-based biophysical model.",

keywords = "GABA(B) receptor, Sleep spindle rhythm, Thalamus, Wavefront velocity",

author = "Zhixiong Chen and Bard Ermentrout and Wang, {Xiao Jing}",

note = "Funding Information: The work was partly supported by NSF and NIMH (to GBE); by the NSF (IBN-9409202), the NIMH (MH53717) and the Alfred P. Sloan Foundation (to XJW).",

year = "1998",

doi = "10.1023/A:1008878310900",

language = "English (US)",

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T1 - Wave propagation mediated by GABA(B) synapse and rebound excitation in an inhibitory network

T2 - A reduced model approach

AU - Chen, Zhixiong

AU - Ermentrout, Bard

AU - Wang, Xiao Jing

N1 - Funding Information: The work was partly supported by NSF and NIMH (to GBE); by the NSF (IBN-9409202), the NIMH (MH53717) and the Alfred P. Sloan Foundation (to XJW).

PY - 1998

Y1 - 1998

N2 - A reduction method is used to analyze a spatially structured network model of inhibitory neurons. This network model displays wave propagation of postinhibitory rebound activity, which depends on GABA(B) synaptic interactions among the neurons. The reduced model allows explicit solutions for the wavefronts and their velocity as a function of various parameters, such as the synaptic coupling strength. These predictions are shown to agree well with the numerical simulations of the conductance-based biophysical model.

AB - A reduction method is used to analyze a spatially structured network model of inhibitory neurons. This network model displays wave propagation of postinhibitory rebound activity, which depends on GABA(B) synaptic interactions among the neurons. The reduced model allows explicit solutions for the wavefronts and their velocity as a function of various parameters, such as the synaptic coupling strength. These predictions are shown to agree well with the numerical simulations of the conductance-based biophysical model.

KW - GABA(B) receptor

KW - Sleep spindle rhythm

KW - Thalamus

KW - Wavefront velocity

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Wave propagation mediated by GABA(B) synapse and rebound excitation in an inhibitory network: A reduced model approach

Abstract

Keywords

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