text: The authors ask when the hidden layer units of a multi-layer feed-forward neural network will display selectivity to object categories. They train 3-layer ANNs to categorize binary patterns, and find that typically at least some of the hidden layer units are category selective. The number of category selective ("localist") units varies depending on the size of the hidden layer, the structure of the outputs the network is trained to return (i.e., one-hot vs distributed), the neurons' activation functions, and the level of dropout-induced noise in the training procedure. Overall, I find the work to hint at an interesting phenomenon. However, the paper as presented uses an overly-simplistic task for the ANNs, and the work is sloppily presented. These factors detract from my enthusiasm. My specific criticisms are as follows: 1) The binary pattern classification seems overly simplistic a task for this study. If you want to compare to the medial temporal lobe's Jennifer Aniston cells (i.e., the Quiroga result), then an object recognition task seems much more meaningful, as does a deeper network structure. Likewise, to inform the representations we see in deep object recognition networks, it is better to just study those networks, instead of simple shallow binary classification networks. Or, at least show that the findings apply to those richer settings, where the networks do "real" tasks. 2) The paper is somewhat sloppy, and could use a thorough proofreading. For example, what are "figures 3, ?? and 6"? And which is Figure 3.3.1? 3) What formula is used to quantify the selectivity? And do the results depend on the cut-off used to label units as "selective" or not (i.e., using a higher or lower cutoff than 0.05)? Given that the 0.05 number is somewhat arbitrary, this seems worth checking. 4) I don't think that very many people would argue that the presence of distributed representations strictly excludes the possibility of some of the units having some category selectivity. Consequently, I find the abstract and introduction to be a bit off-putting, coming off almost as a rant against PDP. This is a minor stylistic thing, but I'd encourage the authors to tone it down a bit. 5) The finding that more of the selective units arise in the hidden layer in the presence of higher levels of noise is interesting, and the authors provide some nice intuition for this phenomenon (i.e., getting redundant local representations makes the system robust to the dropout). This seems interesting in light of the Quiroga findings of Jennifer Aniston cells: the fact that the (small number of) units they happened to record from showed such selectivity suggests that many neurons in the brain would have this selectivity, so there must be a large number of category selective units. Does that finding, coupled with the result from Fig. 6, imply that those "grandmother cell" observations might reflect an adaptation to increase robustness to noise?