Subsampling scaling
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 cri...
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Nature Portfolio
2017
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oai:doaj.org-article:d66280b5799a4525aab1718bc929494c2021-12-02T14:40:59ZSubsampling scaling10.1038/ncomms151402041-1723https://doaj.org/article/d66280b5799a4525aab1718bc929494c2017-05-01T00:00:00Zhttps://doi.org/10.1038/ncomms15140https://doaj.org/toc/2041-1723We 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.A. LevinaV. PriesemannNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-9 (2017) |
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DOAJ |
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DOAJ |
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EN |
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Science Q |
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Science Q A. Levina V. Priesemann Subsampling scaling |
| description |
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. |
| format |
article |
| author |
A. Levina V. Priesemann |
| author_facet |
A. Levina V. Priesemann |
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A. Levina |
| title |
Subsampling scaling |
| title_short |
Subsampling scaling |
| title_full |
Subsampling scaling |
| title_fullStr |
Subsampling scaling |
| title_full_unstemmed |
Subsampling scaling |
| title_sort |
subsampling scaling |
| publisher |
Nature Portfolio |
| publishDate |
2017 |
| url |
https://doaj.org/article/d66280b5799a4525aab1718bc929494c |
| work_keys_str_mv |
AT alevina subsamplingscaling AT vpriesemann subsamplingscaling |
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