期刊
PLOS COMPUTATIONAL BIOLOGY
卷 14, 期 2, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1006007
关键词
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资金
- NIHR Oxford Health Biomedical Research Centre
- Wellcome Trust [203139/Z/16/Z, 106183/Z/14/Z]
- MRC UK MEG Partnership Grant [MR/K005464/1]
- MRC Doctoral Training Grant [MR/K501086/1]
- UK Engineering and Physical Sciences Research Council (EPSRC) [EP/L023067/1]
- MRC [MR/L009013/1]
- EPSRC [EP/L023067/1] Funding Source: UKRI
- MRC [MR/K005464/1, MR/L009013/1, MR/M009122/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [1363667, EP/L023067/1] Funding Source: researchfish
- Medical Research Council [MR/K005464/1, MR/L009013/1] Funding Source: researchfish
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.
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