A numerical simulation of neural fields on curved geometries

Martin, R., Chappell, D.J. ORCID: 0000-0001-5819-0271, Chuzhanova, N. ORCID: 0000-0002-4655-3618 and Crofts, J.J. ORCID: 0000-0001-7751-9984, 2018. A numerical simulation of neural fields on curved geometries. Journal of Computational Neuroscience. ISSN 0929-5313

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Despite the highly convoluted nature of the human brain, neural field models typically treat the cortex as a planar two-dimensional sheet of neurons. Here, we present an approach for solving neural field equations on surfaces more akin to the cortical geometries typically obtained from neuroimaging data. Our approach involves solving the integral form of the partial integro-differential equation directly using collocation techniques alongside efficient numerical procedures for determining geodesic distances between neural units. To illustrate our methods, we study localised activity patterns in a two-dimensional neural field equation posed on a periodic square domain, the curved surface of a torus, and the cortical surface of a rat brain, the latter of which is constructed using neuroimaging data. Our results are twofold: Firstly, we find that collocation techniques are able to replicate solutions obtained using more standard Fourier based methods on a flat, periodic domain, independent of the underlying mesh. This result is particularly significant given the highly irregular nature of the type of meshes derived from modern neuroimaging data. And secondly, by deploying efficient numerical schemes to compute geodesics, our approach is not only capable of modelling macroscopic pattern formation on realistic cortical geometries, but can also be extended to include cortical architectures of more physiological relevance. Importantly, such an approach provides a means by which to investigate the influence of cortical geometry upon the nucleation and propagation of spatially localised neural activity and beyond. It thus promises to provide model-based insights into disorders like epilepsy, or spreading depression, as well as healthy cognitive processes like working memory or attention.

Item Type: Journal article
Publication Title: Journal of Computational Neuroscience
Creators: Martin, R., Chappell, D.J., Chuzhanova, N. and Crofts, J.J.
Publisher: Springer
Date: 11 October 2018
ISSN: 0929-5313
697Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 15 Oct 2018 10:34
Last Modified: 15 Oct 2018 10:34
URI: https://irep.ntu.ac.uk/id/eprint/34669

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