Subsampling scaling

• Published in: Nature communications 2017-05, Vol.8 (1), p.15140-15140, Article 15140
• Main Authors: Levina, A., Priesemann, V.
• Format: Article
• Language: English
• Subjects: 631/114/1314; 631/378/116; 631/553/2718; Avalanches; Bias; Epidemics; Epidemics - statistics & numerical data; Humanities and Social Sciences; Humans; multidisciplinary; Nerve Net - physiology; Neural networks; Neurons; Neurosciences; Probability; Probability distribution; Random variables; Science; Science (multidisciplinary); Selection Bias; Statistics as Topic
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms15140

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. We can often observe only a small fraction of a system, which leads to biases in the inference of its global properties. Here, the authors develop a framework that enables overcoming subsampling effects, apply it to recordings from developing neural networks, and find that neural networks become critical as they mature.