Large-scale neural dynamics: simple and complex

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 differ...

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Main Author: Coombes, Stephen
Format: Article
Published: Elsevier 2009
Subjects:
Online Access:https://eprints.nottingham.ac.uk/1221/
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author Coombes, Stephen
author_facet Coombes, Stephen
author_sort Coombes, Stephen
building Nottingham Research Data Repository
collection Online Access
description 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 extended 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.
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spelling nottingham-12212020-05-04T16:28:49Z https://eprints.nottingham.ac.uk/1221/ Large-scale neural dynamics: simple and complex Coombes, Stephen 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 extended 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. Elsevier 2009-12-23 Article PeerReviewed Coombes, Stephen (2009) Large-scale neural dynamics: simple and complex. NeuroImage . ISSN 1053-8119 (In Press) brain wave equation EEG fMRI http://www.sciencedirect.com/science/journal/10538119
spellingShingle brain wave equation
EEG
fMRI
Coombes, Stephen
Large-scale neural dynamics: simple and complex
title Large-scale neural dynamics: simple and complex
title_full Large-scale neural dynamics: simple and complex
title_fullStr Large-scale neural dynamics: simple and complex
title_full_unstemmed Large-scale neural dynamics: simple and complex
title_short Large-scale neural dynamics: simple and complex
title_sort large-scale neural dynamics: simple and complex
topic brain wave equation
EEG
fMRI
url https://eprints.nottingham.ac.uk/1221/
https://eprints.nottingham.ac.uk/1221/