Here we compute the mean and standard deviation across seeds. Model Robustness score Baseline 100% MTL with real responses 109% MTL with predicted responses (MTL-Monkey) 118% MTL with shuffled predicted responses (MTL-Shuffled) 98% Table 3: Comparing our MTL model co-trained on predicted neural responses -MTL-Monkey in the paper-to the MTL model co-trained directly on real monkey V1 responses. We computed the robustness score of each model after averaging the accuracies of 3 seeds per model for each corruption type in TIN-TC and normalizing against the baseline test accuracies, i.e. the baseline score is 100%. We find that we can obtain a general increase in robustness when using real neural data. However, co-training on predicted neural responses improves the robustness of the models even more.