Dear Mr. Zhu,

Thank you very much for submitting your manuscript "Learn to integrate parts for whole through correlated neural variability" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

Both reviewers appreciate the idea that the covariance structure of the network responses can be informative about stimuli. However, reviewer 1 feels that the relation to earlier similar ideas should be explored more explicitly. Also both reviewers agree a more detailed description of the method is required.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Fleur Zeldenrust

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

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Both reviewers appreciate the idea that that the covariance structure of the network responses can be informative about stimuli. However, reviewer 1 feels that the relation to earlier similar ideas should be explored more explicitly. Also both reviewers agree a more detailed description of the method is required.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This manuscript by Zhu et al is a very nicely written paper with a potentially powerful idea. They introduce the idea that the covariance structure of the responses of a populations of neurons can be very informative about the identity of a stimulus. It is shown that this covariance structure can be transformed into a rate code in the output layer of feed-forward spiking networks, and they showed clearly that covariance information can be important in certain cases, as illustrated in the orientation task and the bird categorization task. I would be happy to recommend it for publication once the points described below have appropriately addressed.

-My main comment is that the idea of using the covariance as way to transmit information and transform into rates is very similar to the one developed in the following 3 papers:

The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks | PLOS Computational Biology

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008127

Covariance-based information processing in reservoir computing systems | bioRxiv

https://www.biorxiv.org/content/10.1101/2021.04.30.441789v2.abstract

Covariance Features Improve Low-Resource Reservoir Computing Performance in Multivariate Time Series Classification | SpringerLink

https://link.springer.com/chapter/10.1007/978-981-16-9573-5_42

In these papers the authors also show that covariance coding can solve some challenging problems, such as audio recognition. The central idea seems to be very close to the one proposed in the paper, so a thorough comparison between the previous work and the current manuscript’s goals in the introduction and discussion is needed.

-Are not spike rates used too high? In Line 275, up to 1800 spikes per second? How the performance of the spiking network degrades with lower rates?

-In Fig. 4 it is unclear how feature maps are sampled to convert the static bird image into a temporal sequence. Are these features sampled one after the other, and from them the rate is computed, from which means and covariances are finally built.

-How the analytical expression for Eqs. 24-26 are found? For LIF neurons, expressions for Eq. 24 are well-known for the case of inputs with static mean and covariance, but the expression for the correlation in Eq. 26 can be complicated:

Phys. Rev. Lett. 96, 028101 (2006) - Auto- and Crosscorrelograms for the Spike Response of Leaky Integrate-and-Fire Neurons with Slow Synapses

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.96.028101

A more detail description of the required methods and comparison between different existing methods needs to be made.

-Is a linear approximation of the output correlation being used? This seems to be only valid, according to the previous literature cited above, when LIF neurons are driven suprathreshold, but not subthreshold.

-The layers described in Lines 502-503 are not very clearly explained. How normalization is performed?

-In Line 508, output responses x do not need to be Gaussian, specially if neurons are subthreshold. Why is this approximation good here? Alternatively, one could have sampled x’s from the forward transmission of inputs to the response neurons to get a better generative model for x. Would be any difference if using the second approach?

Minor

-Line 116: I would add “we define the first and second order moments…”

-Line 246: “The first two elements”, what two elements? It might be unclear.

-The term SNN has not been defined, as far as I can see.

-In Fig. 3, y label should be phase, not phrase. Same in Line 308.

Reviewer #2: The review is uploaded as an attachment.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes: Veronika Koren

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Attachments

Attachment

Submitted filename: review_plosCB2024.pdf

Dear Mr. Zhu,

Thank you very much for submitting your manuscript "Learning to integrate parts for whole through correlated neural variability" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Both reviewers are now happy with the content of the paper. Reviewer 2 asks for a very minor revision (a supplemental figure being moved to the results section). After the authors either do this, or let me know why they would prefer not to, this manuscript would be ready for publication.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Fleur Zeldenrust

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

Both reviewers are now happy with the content of the paper. Reviewer 2 asks for a very minor revision (a supplemental figure being moved to the results section). After the authors either do this, or let me know why they would prefer not to, this manuscript would be ready for publication.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have appropriately responded to my comments.

Reviewer #2: The authors have addressed my questions thoroughly and have made a number of revisions to the text that substantially improved the clarity of the paper. The main results are now clearly presented and important ideas are suitably outlined. While some open questions remain, for example about the biological plausibility of the operating regime of the network (the firing rates of single neurons in the brain are of the order of 1-100 Hz, but in the model they seem much higher), the paper provides a number of results and concepts that will likely be useful for further study of neural coding in biological and artificial networks.

I suggest the Fig. S2 to be placed in the Results section of the main part of the paper, as it provides a comprehensive analysis of the effect of the stimulus contrast on the performance and activity of the network.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Veronika Koren, Ph.D.

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Dear Mr. Zhu,

We are pleased to inform you that your manuscript 'Learning to integrate parts for whole through correlated neural variability' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Fleur Zeldenrust

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

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