PCOMPBIOL-D-25-00093

Continual familiarity decoding from recurrent connections in spiking networks

PLOS Computational Biology

Dear Dr. Zemliak,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript within 60 days May 30 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Rafal Bogacz

Guest Editor

PLOS Computational Biology

Joseph Ayers

Section Editor

PLOS Computational Biology

Additional Editor Comments :

The manuscript has been evaluated by 2 expert reviewers who appreciated the clarity of the work, and also made several useful suggestions on how the paper can be further imporved. I would particularly like to encourage the Authors to follow the suggestion of Reviewer 1, and investigate the ability of the model to discriminate familiarity for correlated patterns, because the correlation is present between activities of neurons providing visual input, and many previous models do not work for such correlated patterns. Therefore an ability of a model to discimante the familiarity of correlated patterns is strongly indicates if the model is likely to describe the principles underlying familiarity discrimination in the brain.

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At this stage, the following Authors/Authors require contributions: Viktoria Zemliak, Gordon Pipa, and Pascal Nieters. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that the reviews are uploaded as attachments.

Reviewer #1: Please see attached for my reviews.

Reviewer #2: In attachement

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

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes:  Jacques Sougné

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Attachments

Attachment

Submitted filename: PCOMPBIOL-D-25-00093.docx

Attachment

Submitted filename: ReviewPlos.docx

PCOMPBIOL-D-25-00093R1

Continual familiarity decoding from recurrent connections in spiking networks

PLOS Computational Biology

Dear Dr. Zemliak,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript within 30 days Sep 02 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Rafal Bogacz

Guest Editor

PLOS Computational Biology

Hugues Berry

Section Editor

PLOS Computational Biology

Additional Editor Comments :

Thank you for updating the manuscript so thoroughly according to Reviewers comments. Reviewer 2 checked the revisions, and you can see their comments below. Reviewer 1 was not available, so I checked the manuscript myself, and have a few comments listed below.

Minor:

l.89-90: “Li and Bogacz”-> “Li et al”. Please also note that a more recent version of reference 27 is available as an arxiv preprint – please update the reference to this newer version.

Figure 1C: This cartoon does not illustrate the correlation well – For the first panel, the correlation is -1 rather than 0. Please update the panels so the values of correlation match the cartoons.

l. 281: “HebbFF on the data of sparseness 0.0” – but definition of sparseness in line 188 suggests that 0.0 correspond to all pattern being identical, so the familiarity discrimination does not make sense. Please clarify.

lines 356 and 390 and 584: “Anti-Hebbian models” – Please make it more specific, e.g. “recurrent networks with anti-Hebbian plasticity”, to avoid confusion with the anti-Hebbian model (Bogacz et al. 2003), which have different properties.

Typos:

line 126: Mathematical symbol not properly displayed.

l.267-273: “HebFF”, “HebbFF”, “HEbbFF” – please use consistent spelling of the name of this model.

l.863: “Bogacz. &” -> “Bogacz &”

Journal Requirements:

1) We have noticed that you have uploaded Supporting Information files, but you have not included a list of legends. Please add a full list of legends for your Supporting Information files after the references list.

Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #2: The authors have done an impressive job both in extending their research and improving the readability. They really took into account reviewer comments, thanks.

I have no major comment to make, only a few minor remarks.

L126: the tau is in an invisible character

Figure 2 C The colors of LSTM and HebbFF curves are too similar to be discriminated. I had to read the text carefully to recognize which was which.

L526: in the paragraph “In Hebbian networks…” trace memory not race memory.

Page 27, I wonder if it would not be better to insert supplementary material inside the main text. The first time I read the last paragraph (L579-587), I didn't understand it until I dug into the supplementary information.

L751, first paragraph (see Table 2) and not (se Table 2).

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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 #2: Yes

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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 #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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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. Registration is free. 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 PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. If there are other versions of figure files still present in your submission file inventory at resubmission, please replace them with the PACE-processed versions.

Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit 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

Dear Ms Zemliak,

We are pleased to inform you that your manuscript 'Continual familiarity decoding from recurrent connections in spiking networks' 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,

Rafal Bogacz

Guest Editor

PLOS Computational Biology

Hugues Berry

Section Editor

PLOS Computational Biology

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Thank you for adapting the manuscript according to the comments.

In Figure 1C the values of correlation still do not correspond to the patterns, for example, the patterns labelled as 0.2 and 0.4 have actually negative correlation. When you submit your final version, please correct the patterns in this figure, so they match the correlation labels above them