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. 2019 Mar 7;9:3791. doi: 10.1038/s41598-019-40535-4

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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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Prediction of the CNN for V1 neurons. (a) Comparison of the response predictions among various encoding models: the L1-regularised linear regression model (Lasso), L2-regularised linear regression model (Ridge), SVR, HSM, and CNN. Data are presented as the mean ± s.e.m. (N = 2455 neurons). (b) Cumulative distribution of CNN prediction similarity. Neurons with a CNN prediction similarity ≤0.3 (indicated as the red arrow) were removed from the following RF analysis. (c) Distributions of actual responses and predicted responses of the neuron with the best prediction similarity in a plane (top) and the neuron with the median prediction similarity in a plane (bottom). Each dot in the right panel indicates data for each stimulus image. Solid lines in the right panels are the linear least-squares fit lines. Only data for 200 images are shown.