Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al., JCNS 2017)

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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.
Reference:
1 . Zerlaut Y, Chemla S, Chavane F, Destexhe A (2017) Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons. J Comput Neurosci [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Brian 2; Python;
Model Concept(s): Vision;
Implementer(s):
This notebook describes the implementation of the following paper:
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 and Alain Destexhe
paper's link: https://link.springer.com/article/10.1007%2Fs10827-017-0668-2
If you use this code, please cite:
Zerlaut et al. J Comput Neurosci (2017). https://doi.org/10.1007/s10827-017-0668-2

(Please examine the html file or run the notebook for how to run).