Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 8: Accept
review_content: The paper proposes an original approach to predict the function of groups of neurons in the V1 cortex based on their invariance to well designed rotation invariant CNN filters. The design of these features is funded by the observation that specific ganglion cell types have rotation and scale invariant responses to visual stimuli. 
The method is very clearly explained and the evaluation on an publicly available dataset looks promising. The clustering Figure 6 in particular is very insightful. 
The paper could have been more impactful if a comparison with a ground truth was built. The issue is clearly that ground truth is hard to establish for this type of problems but biological observations and annotations of cell types can be available (unfortunately not public as far as I know). 
I would also be curious to know how such a method can be applied to a blind patient whose retina does not react to visual stimuli. Is there a biological function that will still preserve such invariance properties which allow to find structure in the data?
review_title: Official Blind Review #1876

final_score: rating: 8: Accept
review_content: In this study, the authors develop a method to cluster cells in primary visual cortex (V1) based on the cells' responses to natural images. The method consists in three steps: 
- fit a rotation-equivariant convolutional neural network model to V1 cells (previously described in Ecker et al. 2019)
- align all cells by choosing the rotation for each cell that minimizes overall distance between cells in feature space, so that the clustering is mostly blind to the orientation of the filters.
- cluster the cells using a Gaussian mixture model (GMM).

Although I find this article mostly well-written and the topic important, I cannot recommend acceptance because (1) the study does not make a significant contribution to our understanding of V1, (2) the main innovation in ML presented (alignment method) is quite specific and will thus not likely be of interest for the general audience of ICLR:

(1) An important question in visual neuroscience is whether V1 cells form discrete functional clusters as opposed to a continuum. Another related question is whether these functional clusters correspond to distinct cell types characterized by specific wiring patterns, gene expression and/or morphology.
The analyses performed do not answer any of these two questions:
- the clustering model (GMM model) is not compared statistically to other models that would assume a continuous structure in the data (e.g. cells form a sparse continuous manifold in feature space). Although clusters do appear in the t-SNE visualization, this visualization does not provide statistical evidence that cells indeed form distinct clusters.
- The correspondence of the proposed clusters to cell types with specific wiring patterns, gene expression and/or morphology is not established. To establish this correspondence would require further experiments, as acknowledged by the authors: "To systematically classify the V1 functional cell types, these proposals need to be subsequently examined based on a variety of biological criteria reflecting the different properties of the neurons and the prior knowledge about the experiment".

(2) The alignment method, which consists in rotating the cells in feature space so that orientation is not a factor for subsequent clustering, is quite specific to the problem studied and likely not of interest for the general ICLR audience.


Additional feedback:

- Title: ROTATION-INVARIANT CLUSTERING OF FUNCTIONAL CELL TYPES IN PRIMARY VISUAL CORTEX 
=> "functional cell types" is not adequate here, since the article does not establish the existence of functional cell types. Could be replaced with "cell responses".

- Abstract: We apply this method to a dataset of 6000 neurons and provide evidence that discrete functional cell types may exist in V1.
=> this sentence is misleading, since no evidence for functional clusters is provided.

- "Thus, the network has learned an internal representation that allows constructing very similar functions in multiple ways"
=> To avoid the caveat of redundant features, the authors could try to add a dimensionality bottleneck on feature space before readout.

- "Small values of β incur a small cost for poor reconstructions resulting in small optimised values of T and over-smoothed aligned readouts."
=> A simulated annealing procedure (progressive increase of T during learning) could potentially allow the use of larger β values here (i.e. less distortion of the filter).

- The alignment procedure could lead to the emergence of spurious structure in the clustering. It would be important to control for this potential artifact by running the procedure on an unstructured synthetic dataset.

- It is possible that the MEIs within clusters look more similar than they actually are, since the cells are fitted from the same common bank of features. It would be useful but maybe difficult to control for this.

- It would be interesting to test the clustering procedure on a shuffled version of the readout weights (shuffle across features and V1 cells), so as to keep sparsity but not any other structure. Does the t-SNE map look less clustered? Is the GMM fit qualitatively different?

- Fig1(2): add legend/caption. what are the ellipses?












review_title: Official Blind Review #3

final_score: rating: 8: Accept
review_content: The authors present a rotation-invariant representation of a CNN modeling the V1 neurons and a pipeline to cluster these neurons to find cell types that are rotation-invariant. Experimental validation is performed on a 6K neuron dataset with promising results. 
The paper is well postulated.

Below are comments about the work:

1. In Figure 2, what does 1 x feature + 2 x another_feature mean?
2. In Equation 3, why was the ‘square’ of error differences not used? 
3. In the clustering approach, how is the number of mixtures set for the GMM? How stable is the model to different number of mixtures?
4. In Figure 6: are Blocks 5 and 13 the same clusters (since they are of the same color) or is it that the colourmap use did not have 100 colors? 
5. In the ‘network learned redundant features’, Sentence 1: why do the authors say ‘similar MEIs’. The 16 neurons rendered in both blocks look different. 
6. It will be informative to know how the number of clusters vary based on the correlation threshold used to collapse 100 clusters to a lower number. Are the clusters still functionally distinct for varying thresholds? Further why is MEI confusion matrix only shown for 13 groups?
review_title: Official Blind Review #2

Meta-review: This paper is enthusiastically supported by all three reviewers. Thus an accept is recommended.
Decision: Accept (Talk)