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. 2018 May 25;9:2084. doi: 10.1038/s41467-018-04548-3

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© The Author(s) 2018

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. 3 — Determinants of ON/OFF RF divergence in silico. a Input scheme to investigate role of sound alternation in RF divergence. In contrast to Fig. 2 (trained with input sequences where ON and OFF always followed each other), the network is presented with independent ON and OFF sequences where ON/OFF inputs need not necessarily alternate. b Final distribution of ON/OFF CF differences for neurons (n = 100) trained with independent ON/OFF input sequences (brown). Adult population trained with natural alternating inputs shown for comparison (grey). c Input scheme to investigate role of fast evoked synaptic inhibition in RF divergence. Neurons receive evoked synaptic activity, driven by a sound sequence (black) that randomly switches ON (green dashed lines) and OFF (red dashed lines). Excitatory ON (green) and OFF inputs are evoked by sound onsets. Inhibitory inputs are omitted from the model. d Final mean difference between ON and OFF CF (n = 100 cells) in excitation-only model (Ex only, orange). Adult excitation-inhibition results shown for comparison (grey)