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  Subsampling scaling

Levina, A., & Priesemann, V. (2017). Subsampling scaling. Nature Communications, 8: 15140, pp. 1-9. doi:10.1038/ncomms15140.

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https://www.nature.com/articles/ncomms15140 (Publisher version)
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 Creators:
Levina, A1, Author           
Priesemann, V, Author
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1External Organizations, ou_persistent22              

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 Abstract: In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the sheer size of the system, and cannot be overcome by longer sampling. Spatial subsampling can strongly bias inferences about a system's aggregated properties. To overcome the bias, we derive analytically a subsampling scaling framework that is applicable to different observables, including distributions of neuronal avalanches, of number of people infected during an epidemic outbreak, and of node degrees. We demonstrate how to infer the correct distributions of the underlying full system, how to apply it to distinguish critical from subcritical systems, and how to disentangle subsampling and finite size effects. Lastly, we apply subsampling scaling to neuronal avalanche models and to recordings from developing neural networks. We show that only mature, but not young networks follow power-law scaling, indicating self-organization to criticality during development.

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Language(s): eng - English
 Dates: 2017-05
 Publication Status: Published online
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/ncomms15140
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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
 Creator(s):
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Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 8 Sequence Number: 15140 Start / End Page: 1 - 9 Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723