Paper:
Although both our brain and deep neural networks (DNNs) can perform high-level sensory-perception tasks such as image or speech recognition, the inner mechanism of these hierarchical information-processing systems is poorly understood in both neuroscience and machine learning. Recently, Morcos et al. (2018) examined the effect of class-selective units in DNNs, i.e., units with high-level selectivity, on network generalization, concluding that hidden units that are selectively activated by specific input patterns may harm the network's performance. In this study, we revisit their hypothesis, considering units with selectivity for lowerlevel features, and argue that selective units are not always harmful to the network performance. Specifically, by using DNNs trained for image classification (7-layer CNNs and VGG16 trained on CIFAR-10 and ImageNet, respectively), we analyzed the orientation selectivity of individual units. Orientation selectivity is a low-level selectivity widely studied in visual neuroscience, in which, when images of bars with several orientations are presented to the eye, many neurons in the visual cortex respond selectively to a specific orientation. We found that orientation-selective units exist in both lower and higher layers of these DNNs, as in our brain. In particular, units in the lower layers become more orientationselective as the generalization performance improves during the course of training of the DNNs. Consistently, networks that generalize better are more orientationselective in the lower layers. We finally reveal that ablating these selective units in the lower layers substantially degrades the generalization performance, at least by disrupting the shift-invariance of the higher layers. These results suggest to the machine-learning community that, contrary to the triviality of units with highlevel selectivity, lower-layer units with selectivity for low-level features can be indispensable for generalization, and for neuroscientists, orientation selectivity can play a causally important role in object recognition.

Review:
COMMENTS RELATED TO REVISION:
The new analysis that has been added takes a step towards getting at the relationship to invariance. This is a positive. In general comments, the authors state as important contributions: 
"1) to the debate on the harmfulness/importance of selectively activated units in DNNs [1-3] by presenting concrete examples where selectivity is important for generalization, and 2) to the neuroscience community, where, although orientation selectivity has been extensively analyzed for these 60 years since [4], its functional importance in a natural environment has remained unanswered."
On point 2, while this provides an example of an artificial network where orientation plays an important role, it's a stretch to generalize this to conclusions concerning functional importance in neuroscience.
One point 1, I agree that this paper takes steps in the right direction, but ultimately the overall conclusions still feel as though they are a natural and implied consequence of limiting orientation selectivity. It is noted that  orientation selectivity is "not just a superficial byproduct of object recognition, but is causally indispensable for object recognition". The idea that selectivity for oriented edges is indispensible for object recognition again is a conclusion that feels as though it would be shocking if this were not true. The notion of it being a superficial byproduct of object recognition presupposes that the purpose of the system is to recognize objects. Again, it would be surprising if there were vestigial features in the network that are learned, but play no important role - especially among early layers. 
I think the paper might be strengthened by re-working this second point to more strongly establish the causality the authors claim. 
ORIGINAL COMMENTS:
This paper presents interesting analysis and an ablation study on orientation selectivity in neural networks. This is analyzed with respect to generalization performance in decisions made. Overall, the paper is well written and interesting. However, I have a number of comments / concerns as described below:
Positives:
- The paper presents an in depth analysis of the role of orientation selectivity in neural hierarchies. This style of analysis is sorely lacking and fits the theme of learning representations
- The paper itself is well written and quite polished
- The authors have taken great care to rule out any possible confounds through experiments that are not identical to the main claims or objective (specifically the study of section 3.3)
To address:
- The only concern I have (and it is somewhat significant), is the notion of "generalization". This is a rather loaded term, and it is not clear on cursory inspection where this generalization comes from. Is it a function of invariance among higher layers to scene geometry? What are the fundamental underpinnings of these observations outside of correlation to orientation selectivity.
- It seems almost a tautology that removing orientation selectivity would impair performance. I would be more satisfied, and render a higher rating if I felt there was a "smoking gun" with respect to evidence. The conclusion is convincing, but the reasoning comes across as somewhat vague. With that said, it is also understood that this is a non-trivial matter to address and perhaps this paper is an important first step.