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. 2023 Oct 14;51(2):123–140. doi: 10.1007/s10928-023-09886-4

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© The Author(s) 2023

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — In panel A, the structure of an NN with a one-dimensional input, one hidden layer with five neurons, and a one-dimensional output is shown. Arrows denote multiplication with weights, plus signs denote addition of biases and ReLU and Id indicate the applied activation function from input to hidden layer and from hidden to output layer, respectively. In panel B, outputs of the neurons in the hidden layer and the final output of the NN in panel A is illustrated