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Mean-Field Models for EEG/MEG: From Oscillations to Waves

Byrne, Áine; Ross, James; Nicks, Rachel; Coombes, Stephen

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Áine Byrne

James Ross


Neural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.


Byrne, Á., Ross, J., Nicks, R., & Coombes, S. (2022). Mean-Field Models for EEG/MEG: From Oscillations to Waves. Brain Topography, 35, 36–53.

Journal Article Type Article
Acceptance Date Apr 21, 2021
Online Publication Date May 15, 2021
Publication Date 2022-01
Deposit Date Nov 21, 2022
Publicly Available Date Nov 21, 2022
Journal Brain Topography
Print ISSN 0896-0267
Electronic ISSN 1573-6792
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 35
Pages 36–53
Public URL
Publisher URL


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