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. 2019 Dec 4;9:18303. doi: 10.1038/s41598-019-54137-7

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Sampling in spiking networks with biologically plausible noise networks. Kullback-Leibler divergence D_KL(p, p^*) between the empirical state distribution p of a sampling network of spiking neurons and the state distribution p^* generated by the corresponding Boltzmann machine as a function of the number N of noise sources. Error bands indicate mean ± SEM over 10 random network realizations (see Supplementary Material).