ArticleJournal of computational neuroscience2018
Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.
Article in Journal of computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Biophysically inspired mean-field model of neuronal populations driven by ion-exchange mechanisms.eLife · 2026Article
- Comprehensive profiling of brain dynamics during anesthesia across phylogeny.Nature neuroscience · 2026Article
- Neural population models for EEG: From Canonical models to alternative model structures.PLoS computational biology · 2026Article
- Spatially structured heterogeneity shapes large-scale cortical dynamics in a model of the human cortex.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The refresh rate of overhead projectors may affect the perception of fast moving objects: a modelling study.Journal of computational neuroscience · 2026Article
- TVB C++: A Fast and Flexible Back-End for The Virtual Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A biologically plausible decision-making model based on interacting neural populations.PloS one · 2026Article
- Region-specific mean field models enhance simulations of local and global brain dynamics.NPJ systems biology and applications · 2025Article
- Biologically realistic mean field model of spiking neural networks with fast and slow inhibitory synapses.Journal of computational neuroscience · 2025Article
- A computational approach to evaluate how molecular mechanisms impact large-scale brain activity.Nature computational science · 2025Article
- A burst-dependent thalamocortical substrate for perceptual awareness.PLoS computational biology · 2025Article
- Macular: a multi-scale simulation platform for the retina and the primary visual system.Frontiers in neuroinformatics · 2025Article
- A multi-scale study of thalamic state-dependent responsiveness.PLoS computational biology · 2024Article
- Firing rate models for gamma oscillations in I-I and E-I networks.Journal of computational neuroscience · 2024Article
- Article
- Model-agnostic neural mean field with a data-driven transfer function.Neuromorphic computing and engineering · 2024Article
- A mean-field model of gamma-frequency oscillations in networks of excitatory and inhibitory neurons.Journal of computational neuroscience · 2024Article
- Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function.bioRxiv : the preprint server for biology · 2024Article
- On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks.Journal of computational neuroscience · 2024Article
- Shared input and recurrency in neural networks for metabolically efficient information transmission.PLoS computational biology · 2024Article
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4 authors.
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No grant is acknowledged in the PubMed record.
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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