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Journal of Computational Neuroscience

, Volume 44, Issue 1, pp 45–61 | Cite as

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

  • Yann Zerlaut
  • Sandrine Chemla
  • Frederic Chavane
  • Alain Destexhe
Article

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.

Keywords

Recurrent network dynamics Mean-field description Adex model Voltage-sensitive dye imaging 

Notes

Acknowledgments

Research supported by the CNRS, the ICODE excellence network, the European Community: Human Brain Project H2020-720270 and a Flag-Era JTC (SLOW-DYN) to A.D., FET Grant BrainScaleS FP7-269921 to A.D. and F.C and the ANR BalaV1 and Trajectory (ANR-13-BSV4-0014-02 to F.C). Y.Z. was supported by fellowships from the Initiative d’Excellence Paris-Saclay and the Fondation pour la Recherche Médicale (FDT 20150532751).

Compliance with Ethical Standards

Experimental protocols have been approved by the Marseille Ethical Committee in Neuroscience (approval A10/01/13, official national registration French Ministry of Research). All procedures complied with the French and European regulations for animal research, as well as the guidelines from the Society for Neuroscience.

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Unité de Neurosciences, Information et ComplexitéCentre National de la Recherche ScientifiqueGif sur YvetteFrance
  2. 2.Neural Coding laboratory, Center for Neuroscience and Cognitive Systems @UniTnIstituto Italiano di TecnologiaRoveretoItaly
  3. 3.Centre de Recherche Cerveau et CognitionUMR 5549 CNRS & Université Paul Sabatier Toulouse IIIToulouseFrance
  4. 4.Institut de Neurosciences de la Timone (INT)UMR 7289 CNRS & Aix-Marseille UniversitéMarseille Cedex 05France
  5. 5.European Institute for Theoretical NeuroscienceParisFrance

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