Subsampling scaling Journal Article

Author(s): Levina, Anna; Priesemann, Viola
Article Title: Subsampling scaling
Affiliation IST Austria
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.
Keywords: Data processing; Computational neuroscience; Criticality
Journal Title: Nature Communications
Volume: 8
ISSN: 2041-1723
Publisher: Nature Publishing Group  
Date Published: 2017-05-04
Start Page: Article number: 15140
Copyright Statement: CC BY
Sponsor: A.L. received funding from the People Program (Marie Curie Actions) of the European Union’s Seventh Framework Program (FP7/2007–2013) under REA grant agreement no. [291734].
DOI: 10.1038/ncomms15140
Open access: yes (OA journal)