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. 2020 Jun 5;11:2853. doi: 10.1038/s41467-020-16548-3

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© The Author(s) 2020

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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Fig. 1 — a The neural network is implemented on the prototype neuromorphic hardware system BrainScaleS 2. b This system features an analog-neural network core as well as an on-chip general purpose processor that allows for flexible plasticity implementation. c For low degree of input (K_ext = 0.25), strong recurrent connections develop, and the activity shows irregular bursts, resembling a critical state. d For high degrees of input (K_ext = 0.56), firing becomes more irregular and asynchronous.