Paper: Manipulating dropout reveals an optimal balance of efficiency and robustness in biological and machine visual systems (ICLR-2024)

Reviewer comment (Experiments):
In Figure 1, I am not sure why dropout was only implemented in fc6 and fc7 in model training? In a typical training, dropout was applied to all layers.

Author response:
In the PyTorch implementation of AlexNet, dropout is applied only in layers fc6 and fc7 by default. As such, we used this default architecture for all experiments reported here. For future publication, we have begun a more systematic exploration of dropout applied to different groups of layers (or across the entire model), and the corresponding impact on model representations and alignment to biological visual systems.

Reviewer comment (Experiments):
I am wondering about the results of fc7 using the similar analysis in Figs. 2&3.

Author response:
We have now added new analyses (Appendix A.3) showing the results of our main analyses using layer fc7 rather than fc6.

Reviewer comment (Reproducibility):
In Figure 3, how many voxels and how many fc6 units are used? I suspect that the numbers are very different.

Author response:
The brain data from the 8 NSD subjects has an average dimensionality of 34,195 within the large-scale occipitotemporal cortex (OTC) ROI, and layer fc6 of AlexNet has 4,096 units. While these values do differ by an order of magnitude, our RSA procedure is quite standard for comparing brain sector and DNN layer representations. We have added these quantifications of voxel and unit counts to Section 2.4 of the paper.

Reviewer comment (Experiments):
The original study by Stringer, 2019 only recorded neurons in mouse V1. However, the ROI used here included several low- and high-level cortex. Do you expect the eigenspectrum to be different across visual areas in humans. Actually, I would say that this is the novel point to make compared to simply replicating the analyses in Stringer, 2019.

Author response:
First, we now more clearly note that the human OTC ROI encompasses high-level visual cortex only (Section 2.4). Lower-level visual regions such as V1-V4 are not included.  We now emphasize the novelty of our results, which are not simply a replication of Stringer et al., 2019.

Second, we have now taken preliminary steps to replicate our analysis of OTC using human V1 data. Indeed, the average rate of the human V1 eigenspectrum decay (alpha) is 1.48, while the average alpha for human OTC is 1.13. We now detail several new analyses on human V1 in a new Appendix 4. This provides an initial clue that the reviewer’s suggestion of studying eigenspectra in different areas may indeed yield productive insights. We are now working on training models with varied dropout at earlier stages (e.g. in the first block) to further meaningfully extend our paradigm to human V1.

Reviewer comment (Writing):
The paper would benefit from more clarity and better organization, as it was somewhat difficult to understand key components. For instance, the definition of “lesioning” (i.e., pruning neurons in an NN layer), which is central to the paper’s message, is found in a figure caption (Figure 2) but should appear in the main paper.

Author response:
We have clarified this method in the main text in the revised manuscript. Thank you.

Reviewer comment (Experiments):
It is not clear how the layer for lesioning (relu6) was selected or whether results will still hold if a different layer were selected.

Author response:
True. We now clarify that we focused on lesioning fc6 during inference as a first probe, and have now added a new Appendix section A.3 with the results for lesioning layer fc7. (We note that lesioning fc6 and relu6 have the same functional consequence in our implementation, so we now refer to the fc layers rather than the relu layers for our lesioning results.)

Reviewer comment (Experiments):
Results are reported using a single architecture (AlexNet), and may not generalize to other neural networks. Also, the statement in the beginning of the conclusion “schemes within a family of deep neural network (DNN)” needs to be adapted to better reflect the type of networks considered. Specifically, here “family” seems to refer not to architecture variations, but to changes in the level of dropout within an architecture.

Author response:
True. We raise this in the limitations sections. 

We have attempted to clarify this sentence: "In this work, we have instantiated the theoretical tension between sparse and distributed coding schemes with controlled regularization over a set of deep neural network (DNN) models trained on object recognition."

Reviewer comment (Experiments):
It is unclear why human responses were compared to network responses using different datasets (as discussed in section 2.4, human fMRI was obtained using the Natural Scenes Dataset and compared to results on ImageNet using networks). Authors should either explain the motivation for not running networks on the Natural Scenes Dataset to match human fMRI, or report results using a single dataset.

Author response:
True. We thought characterizing the eigenspectra of the model layer with in-distribution images made sense but we very much see your point. So, we now also report a new analysis using the same 515 COCO images from the fMRI study to compute and compare the eigenspectra in the model layer and brain data.  The ImageNet and COCO probe sets yield nearly identical results (mean difference in alpha value of 0.02 over the 10 models’ fc6 layers).  These results are now reported in Appendix A.2.

Reviewer comment (Experiments):
It would be useful to understand the effect of dropout on the trajectories from representational trajectory analysis reported in Figure 1B. Specifically, is it dropout that induces the highest variation in the fc layers, or the underlying network architecture?

Author response:
The network architectures are all the same across these plots, so the variation in their representational trajectories is due to differences in dropout rates during training.