Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: The authors employ RNN models under different conditions to model MEC cells with both well-understood (e.g., place cells) and poorly-understood (heterogeneous cells) tuning properties. By training the network to minimize the place cell-mediated path integration, the network performed incredibly well in explaining MEC-cell dynamics. By removing neurons in the model, the network's path-integration performance was found to be at least as dependent on the model's heterogeneous neurons as on the stereotypical ones. The authors then finish by introducing a reward-based foraging model, again finding responses that match experimental results very well.
main_review: By testing several different neural network models with different loss functions, the authors provide a strong argument in support of heterogenous MEC cells' functionality as key to path integration, in spite of a lack of clear tuning properties. They have integrated experimental data from different animals together well, and have demonstrated that neural network models can provide a key roll in understanding biological neurons and neuronal networks.

limitations_and_societal_impact: Yes
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: By adopting 'similarity transform' methods, this study compares the predictability of different computational models of MEC neurons. The best model not only can explain the firing patterns of grid cells but also that of heterogeneous cells. These findings further allow them to examine the role of heterogeneous cells which has not been clearly identified with experimental approaches. This study suggests that the heterogeneous cells might play critical roles in path integration even more than what grid cells contribute. This manuscript is well-written, and the results provide a theoretical foundation for future studies using a more experimental approach. I only have minor clarification questions and suggestions to embrace a broader readership such as neuroscientists. 
main_review: 1. What does it mean by that the place cell activity is predicted by the network trained in a different size arena at the MEC level? Are neurons (units) identified as grid-cells in one environment more likely to be grid-cells in the larger environments as if expanding the toroidal attractor manifold? If it is the case, how does the level of gridness / heterogeneity changes from one to the other environment? This may be hard to test in experiments since velocity inputs are different even in the same environments but it might be able to test within the computational models.

2. Can the model also predict that the reward attracts the firing fields of the grid cells, as the original data have shown? Is there any possibility that differences in the rate maps between reward+ and reward- conditions stem from different profiles of velocity (such as the animals approaching the rewards rapidly and staying longer) not by the reward itself? I see that the authors argued this a bit, but can your results suggest that this reward-associated predictability is not fully caused by the velocity profiles only but by using the reward input layer?

3. How can we interpret the finding that the place cell activity is better predicted by path integration than the actual coordinate? Is it because neighboring place cells can encode two locations that are actually far from each other? Meanwhile, the high inter-animal consistency might suggest that the place cells mapping might not be totally random but reflect some spatial properties. It would be very helpful if the authors include what the results suggest and how the readers can interpret these findings.

4. Some previous evidence suggests that place cells are independently formed from the grid cells, and often they are stabilized earlier than the grid cells (while many others support the idea of the contribution of grid cells on the place cells). Is this finding compatible with those previous studies (e.g. those studies did not take into account the effects of heterogeneous cells)? Does this study suggest that the place cells are fully dependent on and driven by the MEC neurons? If not, some limitations may need to be included.
limitations_and_societal_impact: Yes.
review_title: null

final_score: rating: 6: Marginally above the acceptance threshold
review_content: summary: The authors address two important questions regarding the non-grid cells in MEC. First, is their response consistent, and not just noise? Second, can they be used as criteria for testing models of MEC functionality? The second question allows to probe the functional significance of these cells, and the effect of incorporating them on various model properties.
The authors’ main tool is a linear mapping from a population of cells into a single target cell. This is done using rate maps of cells, and cross validation on the spatial bins. Several existing models are compared, with one model singled out as corresponding best to experimental data.
The functional relevance of grid and non-grid cells is evaluated using ablation studies on the model, where path integration ability is used as a measure. Generalization of the models is assessed using either different arenas or a reward-biased setting.


main_review: **EDIT** *After carefully reading all the reviews and author responses, I am raising my score to a 6.*


I think these are important and timely questions. The idea of comparing firing patterns across animals and between animals and models is a good one. It has been used in other studies, but I think not many compared between-animal fit as a reference point. Checking the interplay between fitting the entire population and functional aspects is also a promising approach.
Unfortunately, as I detail below, the methods and analyses used by the authors do not adequately answer the questions or support the conclusions of the paper.
The main tool used is consistency between animals and models. I found very little explanation of the motivation for this choice of metric, and it was also quite hard to understand the details of the process. The authors chose a linear mapping from a population of neurons into a single neuron. Crucially, the train and test data are rate maps and the test data are 50% of the bins, under a random choice. The rate maps are also smoothed with a 1-bin wide Gaussian. This means that the test data is not that different from the train data. Given this procedure, I was actually surprised that not all mappings resulted in very high scores (although the animal-to-animal scores were). I can only assume that something about the statistics of the maps under different architectures is responsible, but this is not detailed or explored. Furthermore, the authors ask on line 120 “What firing patterns of MEC cell populations are common across multiple animals?” This is a great question, but I don’t see how the analysis in the paper answers it. It could be that one animal has grid cells and another animal has stripe cells, but they can be linearly combined to give one another. 
Another major tool used is the gridness score. A threshold of 0.3 (typical for the literature) is used to divide the cells, and then compare the different populations. The distributions of gridness score, however, show a wide unimodal distribution with nothing special happening at 0.3. It would strengthen the conclusions if the authors check a range of thresholds, or even omit the cells with a gridness score close to 0.3 (for instance only analyze the bottom and top quadrants).
As for clarity – reading the figures was very challenging. The legends and captions do not provide a complete explanation, and the figures are extremely dense. 
There is related literature on the importance of heterogeneous cells that should be probably discussed: Meshulam et al, Neuron 2017; Stefanini et al, Neuron 2020
Specific points:
1.	Line 13. “that that”
2.	Lines 52-62. The choice for the similarity transform should be better explained.
3.	L100 “solves the neurons”. Not clear when reading this
4.	Supplementary: the transform is not explained clearly enough, given its centrality in the paper.
5.	Supplementary: All models have the same trajectory. Animals do not.
6.	Supplementary: 50% of the spatial bins is quite a dense sample. In general, using different trajectories, and actual responses of the neurons (instead of rate maps) might be a more fair comparison.
7.	L136: “between the population rate maps”. This should be stressed (using rate maps and not responses to specific trajectories), and introduced earlier in the paper
8.	Figure 1A : mirror text
9.	Figure 1C: the boundary at 0.5 is confusing. Not clear what it refers to.
10.	Figure 2: Extremely hard to read. The caption doesn’t provide all the information.
11.	L180: models are substantially different. How consistent is the pattern of figure 2 across different animals? Error bars are across 2861 units. It would be useful to see error bars for the 8 animals. Also – this can be quantified. What is the (spearman) correlation between the different networks across cells, across animals?
12.	Figure 3A caption. Ground truth is gray, not yellow.
13.	Figure 3A. The match between gridness distribution for an untrained network is puzzling. Is this due to initialization of the states? Is this due to some other property? Tuning of hyperparameters? 
14.	Figure 3A. The distribution of gridness in the Dordek et al paper is between 0.8 and 1.2. How come it is so different here?
15.	L212 “were is similar rank order”. Please quantify across animals.
16.	L217 “also also”
17.	L219 the -> they
18.	L223 grid score is explained here, but used in figure 1.
19.	L238 “KS=0.1 significance level”. Why is this level special?
20.	L238 “KS-distance was generally in line” – please quantify
21.	Fig 4A – It is very problematic to draw a regression line and state that “the empirical relationship is comparatively strong”
22.	L244. “which” typo
23.	L255. The motivation for this comparison is unclear.
24.	L306: “it is quite reasonable”. Perhaps. Perhaps not.
25.	The discussion is extremely short. Why do untrained networks have a “good” gridness distribution? Why are heterogeneous cells more important than grid cells? How does this relate to place cell literature? Mixed selectivity in general?

limitations_and_societal_impact: Societal impact – not relevant.
Limitations: mentioned above.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The authors used linear regression to first demonstrate the robustness of non-grid-like responsive cells in experimental recordings of MEC cells. They then used the same regression to assess the similarity of responses derived from the goal driven training of RNNs to MEC responses. They found that responses derived from RNNS contained these non-grid-like responses and explained neural activity better than a low-rank decomposition of place fields.  These non-grid-like responses were crucial for path integration and likely explained the difference in similarity matching between goal-driven training and low rank decomposition.
main_review: There are significant pros and cons with this paper currently. It is written clearly and each part is well-motivated. Adequate controls were performed, and is fairly rigorous.  However, the central finding is simply that goal-based training provides a better fit to real data as compared to a specialized grid cell model. There are some hints that this gap in performance arises from responses that are not easily categorized (Figure 3+4), but there is little insight over the nature of these response types -- are they border cells, heading cells, mixed selectivity cells, etc. I would like to know what these response types are and why they improve similarity matching. I feel that a further exploration of these response types, as detailed in the comments, will greatly strengthen this paper, so I will await author responses.

Comments:

- The main contribution of the paper rests upon understanding cells that cannot be categorized classically into the typical navigation variables such as border / grid / head direction cells. However, there is a clear distinction between border / head direction cells and cells with heterogeneous of mixed selectivity (i.e. cells with both heading and grid responses). But are all of these types of responses treated as heterogeneous cells because they have low grid scores? If so, the definition supplied in the introduction does not agree with the definition of heterogeneous cells in the results section. Shouldn't border, heading, and heterogeneous cells be treated independently?

- It is suggested that "heterogeneous" cells, operationally defined as having low grid scores in figure 1, are responsible for the performance difference between RNN-based goal-driven training vs NMF (Figure 4C). If you take these cells out, is there less of a difference between NMF vs goal-driven approaches? How does the decrease in the performance gap compare to if border cells or head direction cells were taken out? Put another way, is the failure of NMF in reproducing neural responses simply because it can't reproduce border and heading responses, or because it can't reproduce mixed selectivity responses?

- It's unclear how many of these heterogeneous cells exist in the data. Even if this is reported previously it should still be shown here to provide context. Figure 1c also does very little justice in showcasing that heterogeneous cells constitute a unique functional population.

- I have some concerns over the notion of similarity. For one-to-one mapping, can neuron A1 in animal A be mapped, with replacement, to multiple neurons, B1 and B2, in animal B if A1 is the best match to B1 and B2? If this is allowed, then hypothetically, animal A, despite having only a single grid cell and 99 head direction cells, may actually serve as a good template / have a high similarity using ridge regression to animal B, which has 100 grid cells as long as a sufficient basis set of grid, head, border, etc cells exist in every recorded animal. Perhaps the current notion of similarity does not factor in differences in the relative proportion of unique responses across recorded animals? If so, the notion of similarity in the paper is different from a neuroscientists' notion of similarity when viewing population PSTHs of each animal, and may need some degree of qualification in the text.

- Some simple negative controls are needed to interpret similarity measures. For instance, similarity should not be high when regressing a classical grid response when exclusively using classical head direction cells, and vice versa and also for using other classical response types, such as border cells, etc. The similarity levels will also provide the reader with some baseline to evaluate what the numbers mean in figure 1B. 

- I may be reading figure 1C wrong, but it appears that target unit predictivity is marginally higher for het cells than grid cells when using a smaller fraction of source population. Doesn't this mean that het cells actually require fewer source cells to achieve high similarity scores?

- The legends to the different sub-bars in Figure 2 should be made clearer. 

- It's unclear to me what the inclusion of reward responses and Figure 5 adds to the central findings of paper.

 

limitations_and_societal_impact: Not applicable.
review_title: null

Meta-review: Dear Authors, 

congratulations on your paper being accepted at Neurips. Reviewers founds this a highly interesting and intriguing submission, with results that will likely be of interest to a wide range of researchers, and in particular researchers aiming to understand how diverse cells in the MEC underly the ability of animals to navigate. At the same time, this paper also triggered substantial discussion-- the primary concern by the reviewers was that this was a dense paper loaded with results, but low on detailed explanations, which made it challenging for the reviewers to assess the validity of the methods and results. During the discussion phase, extensive additional explanations and clarifications were given, and we decided that the paper -- with these additional explanations -- would be a great addition to Neurips. We urge you to ensure that these explanations also appear in the final paper (or, in most cases, in the supplement).

With best regards, your AC
Decision: Accept (Spotlight)