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• W1991695159 startingPage "731" @default.
• W1991695159 abstract "We review the use of neural field models for modelling the brain at the large scales necessary for interpreting EEG, fMRI, MEG and optical imaging data. Albeit a framework that is limited to coarse-grained or mean-field activity, neural field models provide a framework for unifying data from different imaging modalities. Starting with a description of neural mass models, we build to spatially extend cortical models of layered two-dimensional sheets with long range axonal connections mediating synaptic interactions. Reformulations of the fundamental non-local mathematical model in terms of more familiar local differential (brain wave) equations are described. Techniques for the analysis of such models, including how to determine the onset of spatio-temporal pattern forming instabilities, are reviewed. Extensions of the basic formalism to treat refractoriness, adaptive feedback and inhomogeneous connectivity are described along with open challenges for the development of multi-scale models that can integrate macroscopic models at large spatial scales with models at the microscopic scale." @default.
• W1991695159 created "2016-06-24" @default.
• W1991695159 creator A5015605154 @default.
• W1991695159 date "2010-09-01" @default.
• W1991695159 modified "2023-10-17" @default.
• W1991695159 title "Large-scale neural dynamics: Simple and complex" @default.
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• W1991695159 doi "https://doi.org/10.1016/j.neuroimage.2010.01.045" @default.
• W1991695159 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20096791" @default.
• W1991695159 hasPublicationYear "2010" @default.

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