Classifying dynamic transitions in high dimensional neural mass models: A random forest approach
Classifying dynamic transitions in high dimensional neural mass models: A random forest approach
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United States: Public Library of Science
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English
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United States: Public Library of Science
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Neural mass models (NMMs) are increasingly used to uncover the large-scale mechanisms of brain rhythms in health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understand how dynamics change when parameters are varied. Despite being considered low dimensional in compar...
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Classifying dynamic transitions in high dimensional neural mass models: A random forest approach
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TN_cdi_plos_journals_2025710369
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2025710369
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ISSN
1553-7358,1553-734X
E-ISSN
1553-7358
DOI
10.1371/journal.pcbi.1006009
