article

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons

Authors:

Sandrine Chemla,

Frederic Chavane,

Alain DestexheAuthors Info & Claims

Journal of Computational Neuroscience, Volume 44, Issue 1

Pages 45 - 61

https://doi.org/10.1007/s10827-017-0668-2

Published: 01 February 2018 Publication History

Abstract

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns.

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Cited By

Lu Y Rinzel J(2024)Firing rate models for gamma oscillations in I-I and E-I networksJournal of Computational Neuroscience10.1007/s10827-024-00877-z52:4(247-266)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1007/s10827-024-00877-z
Shirani FChoi H(2023)On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networksJournal of Computational Neuroscience10.1007/s10827-023-00863-x52:1(73-107)Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1007/s10827-023-00863-x

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

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cover image Journal of Computational Neuroscience

Journal of Computational Neuroscience Volume 44, Issue 1

February 2018

142 pages

ISSN:0929-5313

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

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Published: 01 February 2018

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Cited By

Lu Y Rinzel J(2024)Firing rate models for gamma oscillations in I-I and E-I networksJournal of Computational Neuroscience10.1007/s10827-024-00877-z52:4(247-266)Online publication date: 19-Aug-2024
https://dl.acm.org/doi/10.1007/s10827-024-00877-z
Shirani FChoi H(2023)On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networksJournal of Computational Neuroscience10.1007/s10827-023-00863-x52:1(73-107)Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1007/s10827-023-00863-x

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