Learning divisive normalization in primary visual cortex
Fig 3
Performance comparison of our models fitted to the data from Cadena and colleagues [14] relative to the gap between the best shallow model—a subunit one layer convolutional neural network (CNN)—and the deeper data-driven state-of-the-art three-layer CNN [14].
Non-specific divisive normalization (DN) accounts for 41% of this gap, while specific DN improves it up to 52%. Absolute values in terms of percentage of explainable variance explained (FEV) on the right (mean over the ten best models selected in terms of validation set accuracy, see main text for details). Error-bars (black) indicate the standard error of the mean. Model performance is significantly different between each model class (pairwise Wilcoxon signed rank test on best models in terms of validation accuracy: p < 0.024, N = 166 neurons, family-wise error rate α = 0.05 using Holm-Bonferroni correction).