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. 2015 Jan 12;5:2. doi: 10.1186/2190-8567-5-2

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© S.J. Moon et al.; licensee Springer 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fig. 8 — Kolmogorov–Smirnov test of the hypothesis that the heterogeneity distribution of one of the double-cluster states is consistent with the full heterogeneity distribution; this test is necessary in order to identify the characteristics of the random variable of each of the sub-networks, for possible PC expansion. The cumulative distribution function (CDF) of the normal distribution (thin solid line) is compared with the CDFs of (thick lines) of sub-networks formed with 25, 50, 100, and 500 neurons, respectively. The original distribution for the entire population is a standard normal distribution

Inline graphic — Kolmogorov–Smirnov test of the hypothesis that the heterogeneity distribution of one of the double-cluster states is consistent with the full heterogeneity distribution; this test is necessary in order to identify the characteristics of the random variable of each of the sub-networks, for possible PC expansion. The cumulative distribution function (CDF) of the normal distribution (thin solid line) is compared with the CDFs of (thick lines) of sub-networks formed with 25, 50, 100, and 500 neurons, respectively. The original distribution for the entire population is a standard normal distribution