A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks

Abeysuriya, Romesh G., Hadida, Jonathan, Sotiropoulos, Stamatios N., Jbabdi, Saad, Becker, Robert, Hunt, Benjamin A.E., Brookes, Matthew J. and Woolrich, Mark W. (2018) A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks. PLoS Computational Biology, 14 (2). e1006007. ISSN 1553-7358

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Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.

Item Type: Article
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine
University of Nottingham, UK > Faculty of Science > School of Physics and Astronomy
Identification Number: https://doi.org/10.1371/journal.pcbi.1006007
Depositing User: Eprints, Support
Date Deposited: 19 Feb 2018 11:24
Last Modified: 14 Mar 2018 10:19
URI: https://eprints.nottingham.ac.uk/id/eprint/49852

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