Review for NeurIPS paper: High-contrast "gaudy" images improve the training of deep neural network models of visual cortex

Weaknesses: The main weakness of the work to me was the context in which the authors proposed and evaluated the use of gaudy images. Modeling neural activity using DNNs is of great importance in computational neuroscience. However, the authors motivate this specific method because they claim there is not enough neural data to train complex models on normal natural images, either straight from image to neural activity or from pre-trained DNN activities to neural activity. I find both of these claims misleading: there is quite a bit of work successfully training end-to-end deep networks from images to neural predictions even with limited experimental data. I especially object to the author's explanation that linear mappings are used between pre-trained DNNs and neural activity due to lack of data.