Dear Sexton,

Thank you very much for submitting your manuscript "Spike-timing dependent plasticity compensates for neural delays in a multi-layered network of motion-sensitive neurons" 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.

Reviewers appreciated the presentation of the scientific problem but raised several questions which require a major revision. Please update your manuscript and provide a response to the reviewers in order to consider a novel submission.

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:

[1] A letter containing a detailed list of your responses to the 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).

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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,

Laurent Udo Perrinet

Guest Editor

PLOS Computational Biology

Thomas Serre

Section Editor

PLOS Computational Biology

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Reviewers appreciated the presentation of the scientific problem but raised several questions which require a ajor revision. Please update your manuscript and provide a response to the reviewers in order to consider a novel submission.

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: Overall I found the paper very interesting and quite well conceived. My remarks are mostly about minor details. This work uses a simple neuronal model (no homeostasis, inhibition...) but demonstrates the ability of STDP learning for compensating for delays during motion perception. Their related work mentions no similar work in that domain. The result part is thorough, but could benefit from some more quantitative analysis as it can be difficult to gather all the information from the plots only. All the results are presented on simulated inputs. It would be a great addition to try the model on real inputs or a standard motion dataset.

My major concerns are on the related work part. It presents very few papers on the subject except biological papers that focus on the biological effect of motion-lag compensation.

There is some literature missing on the subject of learning motion representations with spiking neural networks, such as:

- Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception

Federico Paredes-Vallés, Kirk Y. W. Scheper, Guido C. H. E. de Croon

- Learning heterogeneous delays in a layer of spiking neurons for fast motion detection

Antoine Grimaldi and Laurent U Perrinet

- Spike timing-based unsupervised learn-ing of orientation, disparity, and motion representations in a spiking neural network.

T. Barbier, C. Teulière, and J. Triesch

Even though those papers do not focus specifically on the work of motion lag compensation, they present important mechanisms for motion tuning. If this is the only model that specifically implements a motion lag compensation framework, I would mention this explicitly.

I also have a few specific remarks as well as general questions that are of lesser concern:

line 139: Delays in biological model are not necessarily constant for each neuron. What problems, if any, do you expect for a model with variable delays ? What could be done to address such problems?

Line 210: The training occurs for around 20 seconds. This seems like a very short time compared to real biological training time. Please comment.

Fig 5: It is difficult to see the difference in shift between the 4 plots. Please provide a quantitative analysis.

You address the motion tuning as a dual layer phenomenon (lower cells encode position, higher cells encode motion). Have you thought about lower cells with synapses of various delays in order to encode motion instead? This could be worth discussing.

Reviewer #2: Following Fu et al (2004) observations, and Burkitt et al model (2021), the authors propose a model of receptive field shift in a feed forward neural network, under spike timing dependent plasticity (STDP). The model is composed of six layers of simple neuronal integrators with stochastic firing, and fixed axonal delays betwen layers. The neurons are organized on a ring, with a spatially-dependent connectivity. The first layer is stimulated with a moving stimulus at constant speed. The weights evolve under STDP with some compensatory (normalizing) mechanisms. The authors show a consistant shift of the receptive field of the neurons in the direction opposite to the motion, that accumulates over the layers. This non-supervised mechanism is shown to partly compensate the axonal delays accumulated over the layers.

The plasticity mechanism proposed only relies on a simple weight normalization, and remains blind to the true speed of the stimulus (overestimation for low velocities, underestimation for high velocities, with an "optimum" at around 0.8 cycle/s in fig. 9). As explained in the discussion, this mechanism alone is not enough to implement a true temporal alignment.

More problematically, the way the authors pretend their model may reflect the flash-lag effect (FLE) illusion is an overstatement, and the figure 2 is clearly misleading to that prospect. One can not pretend to propose a mechanistic model of the flash lag effect by using different weights for different stimuli (!).

In conclusion, the authors have provided a timely analysis of an important receptive field shift effect that is shown to cumulate over layers. However, the authors tend to oversell their results and falsely pretend that it compensates for the time delays (this is only true in a tiny interval), and may explain more high-level features like the flash lag effect.

For that reason, the paper can not be accepted in the present writing. Both the introducion and the descussion need to be rewritten to reflect the limited scope of the results.Figure 2 is clearly misleading and should be removed. Also fig. 8 should be removed for it suggest to a distracted reader that the temporal alignment is effective at all speed. Moreover, a new figure should clearly quantify, on a layer by layer basis, the shift in temporal alignment in function of the stimulus speed (for fig.9 is quite difficult to read from that perspective)

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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: Emmanuel Daucé

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.

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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

Dear Sexton,

Thank you very much for submitting your manuscript "Spike-timing dependent plasticity partially compensates for neural delays in a multi-layered network of motion-sensitive neurons" 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.

Thanks for the revision of your paper. Feedback from reviewers suggest minor corrections to account for further comments.

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,

Laurent Udo Perrinet

Guest Editor

PLOS Computational Biology

Thomas Serre

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:

Thanks for the revision of your paper. Feedback from reviewers suggest minor corrections to account for further comments.

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: We thank the authors for their careful revision of the manuscript. All our concerns have been addressed satisfactorily. We think the manuscript is now ready for publication.

Reviewer #2: (no attachment)

I appreciated the effort made by the authors in significantly reworking the introduction and conclusions of this paper, which has gained clarity compared to the previous version. While clarifying their model and results, it seems to me that in their conclusions, the authors do not sufficiently discuss certain important limitations.

Firstly, the proposed model uses a large number of synaptic relays and layers of neurons for the processing of visual data. As stated, each speed selectively activates a sub-network and inhibits the rest. How can such a system claim to process a large number of directions and speeds (beyond those analyzed in the paper)? It appears highly improbable that the visual system dedicates a specialized system to each direction/speed, considering that the number of considered layers (six synaptic contacts) can account for the entirety of rapid visual processing (Thorpe et al., 1996). Would it be possible to do away with these specialized sub-networks to achieve more efficient and economical visual processing?

Secondly, the case of static inputs is superficially addressed. With the proposed model, temporal alignment is fine for targets with a speed of 0.8 cycles/s, but in principle, it should also be fine for stationary targets. Can the authors confirm whether a static target would be correctly represented in all layers, just like a moving target? Does the widening of the receptive field with layers for low speeds pose a problem? If so, how can it be overcome? How can a system be proposed that accurately processes both static and moving targets across layers?

Please elaborate on your conclusions and discussion regarding these two points.

Minor point: Figure 2 remains unclear and should be improved, perhaps by reversing the black/white contrast. In the figure caption, it is difficult to understand how many layers were used to measure the temporal shift (layer 2 and above??). Some axes lack units.

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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: No: I may be wrong, but it seems that the code of the model was not made available.

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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: No

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 Sexton,

We are pleased to inform you that your manuscript 'Spike-timing dependent plasticity partially compensates for neural delays in a multi-layered network of motion-sensitive neurons' has been provisionally accepted for publication in PLOS Computational Biology.

We would like to thank you for your updated manuscript which correctly answered the comments of reviewers. I appreciated the availability of the code despite the fact that I could not test it myself. Note that the reference to the conference paper "Grimaldi A, Perrinet LU. Learning hetero-synaptic delays for motion detection in a single layer of spiking neurons. 2022." is now published as "Grimaldi A, Perrinet LU. Learning heterogeneous delays in a layer of spiking neurons for fast motion detection. 2023. Biological Cybernetics."

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,

Laurent Udo Perrinet

Guest Editor

PLOS Computational Biology

Thomas Serre

Section Editor

PLOS Computational Biology

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