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Get Free AccessBoltzmann-Shannon entropy is a measure of the information hidden within multiple indistinguishable rearrangements of a single system. An alternative metric (``selection entropy'') is derived, which instead quantifies the hidden information associated with indistinguishable selections that can take place between systems. Selection entropy is shown to be more sensitive than the KL-divergence (relative entropy) in the context of neuroimaging time series analysis.
Erik D. Fagerholm, Zalina Dezhina, Rosalyn Moran, Karl Friston, Federico Turkheimer, Robert Leech (2023). Selection entropy: The information hidden within neuronal patterns. , 5(2), DOI: https://doi.org/10.1103/physrevresearch.5.023197.
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Type
Article
Year
2023
Authors
6
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1103/physrevresearch.5.023197
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