Venue: NeurIPS 2019 Workshop Neuro_AI

Reviews:

final_score: rating: null
review_content: importance_comment: Object recognition networks have been the benchmark model for a long time, but other options have not been explored in depth. This paper points out that networks with random weights fit mouse visual activity just as well as models trained to perform object recognition tasks, suggesting that the object recognition task itself is probably not an ethological comparison.
rigor_comment: Thoroughly thought-out model and fitting procedure.
clarity_comment: Very clear.
intersection_comment: Strong intersection.
comment: Would have liked to know more about which layers of the random network corresponded to neural activity, as a sort of benchmark of how many non-linear RELUs the pixels might need to go through before obtaining a representation that's as similar to neural activity as VGG.
review_title: well-constructed models and comparisons to large-scale neural recordings in mouse V1

final_score: rating: null
review_content: importance_comment: -- The surprisingly high power of randomly weighted DCNNs is a point that has popped up a couple of times in recent human fMRI / MEG work. The present paper makes the important case that random networks should be included as a matter of course in DCNN modelling projects, and sounds a note of caution about the field's temptation to over-interpret the particular features learned by high-performing trained networks.
rigor_comment: -- Comprehensive data measurement and modelling pipeline. Use of the same spatial transformer model with an interchangeable bank of input features is elegant. 
clarity_comment: -- Very well written. Figures exceptionally detailed and thoroughly labelled. Methods described clearly and in good detail.
intersection_comment: -- Mostly neuroscientific, but addresses the important topic of how models from machine learning can best be used in neuro research.
comment: -- Generally, great paper. Clear presentation of thorough work, exploring an important question.

-- Would have been great to include another Imagenet-trained architecture, since different architectures have widely varying macaque brain predictivity, and that of VGG16 is not particularly high (Schrimpf et al., 2018 BrainScore).

-- I'm not a big fan of the asterisks in Figures 3A and 3B used to indicate the best layers in various model tests. It doesn't provide any additional information to the data lines themselves, and it leads the reader to expect these indicate statistically significant comparisons.

-- Typo page 4 line 158: "pray" >> "prey"
review_title: Important question, well addressed, with an interesting result

final_score: rating: null
review_content: importance_comment: CNNs have been explored thoroughly with primate and human data. Given the extensive use of mice in neuroscience research it is very important to understanding how these models relate to those systems and ideally how to build a good model for mouse data.  
rigor_comment: The authors seem to take into account all the relevant complexities including image scale, RF size, running speed, etc. I would be interested to see how the results would differ if trying to train and predict on average responses over many trials of the same image, as this would be a more direct comparison to what is done in primate research.  
The random weight test was an important and insightful control to do.
clarity_comment: The presentation is mostly clear and straightforward. One point of confusion as to how the model was trained: the paper says "All components of the model are trained jointly end-to-end". I initially believe the VGG core wasn't trained, but this statement made that unclear. 
intersection_comment: This work directly compares a task-trained ML model to mouse data and so is directly at the intersection of ML and neuro.
review_title: Necessary work competently executed

Decision: Accept (Oral)