Dear Prof Nogaret,

Thank you very much for submitting your manuscript "Estimation of neuron parameters from imperfect observations" for consideration at PLOS Computational Biology.

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Sincerely,

Hermann Cuntz

Associate Editor

PLOS Computational Biology

Daniele Marinazzo

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: This fairly theoretical paper describes a process to estimate parameters of neuron models. The method is applied using surrogate data obtained from running a model of a rostral ventrolaterial medulla neuron. It contains several techniques to find back the global optimal estimate of the parameters of the model in question.

I think the paper is fairly well written, and rigorously describes the math behind the method.

However, in general I have two problems with the paper in its current form.

Firstly, although the method is described very rigorously, I find the results obtained rather less convincing. The method is only applied on a single model. For the results of that single model not many statistics are used. Quite some results are based on a single observation (as an example I'd give Fig 5). It would be good to show that the results are more robusted over more models and/or more trials.

Secondly, a big drawback of this paper is that it is based on surrogate data. Over the years many theoretical papers have been published with methods to optimize neuron parameters, but not many of these are actually used in real situations. The reason is that there is a world of difference between surrogate data and real experimental data. Just adding some noise to the surrogate data doesn't really represent the real world problems. Therefore I'd strongly suggest that the authors consider at least to add an example where they apply their method to real experimental data. I do understand that of course in that case some analysis can't be performed, because one doesn't know the original parameters, but it still allows for an analysis of number of solutions found, etc.

line 102: using a least-square error function has as big drawback that it is very sensitive to tiny shifts in timings. E.g. if an AP is shifted by a couple of ms, it's exact shape could still be the same, but the error could be huge. Could you discuss this in the paper, and how this could be solved.

line 193: at the moment you're adding noise to the voltage. I'd also suggest adding noise to the current, which would reflect reality better and which could lead to some shift of e.g. the AP timings etc.

line 279: it's not very clear to me how this percentage was obtained. As I mentioned above, I also think these numbers are too precise and for a particular case, I'd rather see mean/std of these over a couple of trials, use cases.

line 331: to say 'systematic' it has to be quantified better

line 352: typo: missing function 'of' a biological

line 415: I think, based on the fact that this is very theoretical work, it is far too much a stretch to start draw conclusion from this about brain function

line 515: about the adaptive time step approach in general. The NEURON simulator has an adaptive time step approach that does exactly this in a more advanced way (i.e. decrease the time step at times when more data points are necessary). It would make sense to reference and discuss that method.

About the choice of parameters. You do tune a lot of parameters at the same time. Especially the kinetic parameters can in generally be obtained from separate experiments / literature. It might make sense to discuss if it's better to fit everything at the same time, or break things up in different components.

Reviewer #2: Comments to authors attached as pdf.

Reviewer #3: Comments see attached document

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes: Robert P Gowers

Reviewer #3: No

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Attachments

Attachment

Submitted filename: paper-review-1.pdf

Attachment

Submitted filename: main.pdf

Dear Prof Nogaret,

We are pleased to inform you that your manuscript 'Estimation of neuron parameters from imperfect observations' 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,

Hermann Cuntz

Associate Editor

PLOS Computational Biology

Daniele Marinazzo

Deputy Editor

PLOS Computational Biology

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Please make sure to address the final concerns of Reviewer #2.

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 addressed all the remarks.

Reviewer #2: Review uploaded as attachment.

Reviewer #3: Placeholder

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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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: Yes: Robert Gowers

Reviewer #3: Yes: Alexander J Stasik

Attachments

Attachment

Submitted filename: reviewer2-second-review.pdf