Reviews: A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit

This work considers similarity-preserving objective functions for learning to classify inputs with a temporal dimension. The authors propose a modification of the Lie algebra formulation of Ruderman and Rao, where the algorithm maximizes the similarity of transformation of inputs that are nearby in time rather than comparing inputs at the same time directly. While the scores given were worthy of acceptance, the enthusiasm of reviewers both in the body of the reviews and in the discussion was somewhat muted. My impression is that there were two main reasons for this. Thus, while I see no reason to contradict the recommendation of the reviewers that the paper be accepted, we expect the reviewers to address these points (and the clarity of the paper in general) in the camera ready version of the paper.