PyFibers: An open-source NEURON-Python package to simulate responses of model nerve fibers to electrical stimulation

PLOS Computational Biology

Dear Dr. Grill,

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 Jul 27 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,

Alain Nogaret, PhD

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

Additional Editor Comments:

The software builds upon libraries of the NEURON suite and innovations ought to be emphasized in the revision. As a "software article" the manuscript could be published as long as the novelty and examples of impact cases not addressed by existing libraries could be stated. In your revision please address additional reviewer comments.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The submission “PyFibers: An Open-Source NEURON-Python Package to Simulate Responses of Model Nerve Fibers to Electrical Stimulation” describes a well-developed Python package that models and simulates the activity of peripheral nerve fibers within the NEURON simulation environment. It carries the overcoming of several chronic obstacles in the field, such as the NEURON HOC language steep learning curve proprietary barrier, reproducibility issues, and simulation fiber over monolithic non-reusable code.

Recommendations for Improvement:

The mindset of continuously developing the software to address the gaps in usability and standardization is admirable. However, the scientific and methodological novelty does not seem to stand out in this particular case. The principal software engineering achievements that provide the core value are contained within the supporting algorithms and models, which have already been established in earlier literature. Instead, consider rephrasing the framing for the software’s contributions toward positioning it as enabling infrastructure that paves the way for innovation and reproducibility rather than a novel scientific finding.

In my opinion, the manuscript would be improved by including a comparison of performance metrics such as runtime, ease of use (lines of code), and accuracy between PyFibers and the traditional HOC methods. Quantitative benchmarks for various system configurations (e.g., with parallel simulations versus without) could assist potential users in assessing the system’s applicability to their research needs.

The manuscript could be enhanced by incorporating more PyFibers use cases in realistic experimental workflows such as incorporating data from electrode arrays and comparing it to electrophysiological recordings. Even a small illustrative example of alignment between PyFibers simulations and actual recordings like SFAPs would demonstrate the utility and impact of PyFibers in simulation.

Other tools like ASCENT or those built around HOC are mentioned, but a more thorough discussion would be helpful to identify relative benefits of PyFibers.

Several hyperlinks (to documentation, GitHub, or ReadTheDocs) are listed as placeholders (e.g. <

>) which disrupt narrative flow. These should be finalized to ensure reviewers and prospective users will navigate freely throughout the submission.

The biologically unrealistic parameters such as fiber diameters or absent mechanisms would be drastic user errors for PyFibers. The paper does not talk about how these edge cases would be handled. It would be better with at least minimal mentions of these gaps.

The choice of fiber models is an important one. Although it is nice to see models from prior work being used, some reasoning behind the rationale for model selection could be discussed. Will there be community-wide mechanisms for model submissions or for extending the model library?

Additional overview schematics, not only based on the simulation flow diagrams, would aid users in understanding the holistic interactions among fiber models, simulation protocols, and the analysis tools and their workflows, thus aiding user comprehension. The visualizations provided are clear and informative. Make sure that high-resolution versions are included in the final submission. Specifically, within figure legends, check for consistency in notation (e.g., Vm, vm, Vm(t)) and ensure uniformity across the manuscript.

Overall evaluation: This work makes a valuable and innovative contribution to the field of computational neuromodulation. It needs to be improved based upon the comments above.

Reviewer #2: The authors present an open-source Python software library, "PyFibers", designed to provide an efficient computational framework for simulating peripheral nerve fiber responses to electrical stimulation. The library is built upon the classic HOC-based NEURON environment and introduces a modular, extensible framework that includes a baseline of 10 experimentally validated fiber models. The proposed framework also features stimulation models, a user interface, documentation, tutorials, and unit testing. However, at this stage, the code is not yet publicly available.

The manuscript was submitted as a “software article” rather than a “research paper,” which is the appropriate classification for this work, and it has been reviewed accordingly.

Overall, I find the framework relevant and potentially impactful for computational neuroscientists who aim to simulate morphologically driven phenomena. The proposed system is well-structured, object-oriented, and cross-platform compatible, with apparent integration into existing pipelines such as ASCENT. The package is accompanied by usage guides and API documentation and includes support for both myelinated and unmyelinated fiber models. Importantly, the authors validated their implementations against previously published models.

Areas for Improvement

1. Benchmarking. The authors highlight a reduction in the lines of code required (which is reasonable), but they should also consider evaluating their framework in terms of quantitative performance metrics, particularly runtime efficiency. Comparing PyFibers to traditional HOC implementations on complex, large-scale morphological models would help demonstrate its practical usability and scalability.

2. Functional Use Cases. A primary weakness of the study is the absence of demonstrations involving realistic, functionally significant simulation scenarios. While the authors validate their models against previously published work, they do not showcase how PyFibers can be applied to novel, morphologically driven investigations. While this limitation is somewhat acceptable given the software-focused nature of the manuscript, a discussion on potential applications would significantly enhance the paper’s impact. For instance, referencing studies such as [https://doi.org/10.1371/journal.pcbi.1009754](https://doi.org/10.1371/journal.pcbi.1009754) and discussing how PyFibers could support similar simulations would enrich the discussion and emphasize the tool’s relevance.

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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: No

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PCOMPBIOL-D-25-00791R1

PyFibers: An open-source NEURON-Python package to simulate responses of model nerve fibers to electrical stimulation

PLOS Computational Biology

Dear Dr. Grill,

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 by Jan 11 2026 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,

Alain Nogaret, PhD

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

Additional Editor Comments:

Please address the further suggestions for improvement namely:

- Improve the readability of the Design and Implementation section by making it more succinct and accessible to non-programming readers.

- Briefly explain how future community contributions will be moderated or version-controlled.

- Ensure the consistency in notations (e.g., Vm vs. Vₘ, μm vs. um) throughout the figures and tables.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: Compared to the original submission, this revised manuscript is significantly better. The authors have now addressed all the previous criticisms by the reviewers and editors in a concise and clear manner. In the revised version, PyFibers is available as open-source, validated computational code, thus greatly enhancing access, reproducibility, and standardization for simulations involving nerve fibers.

Minor comments:

The Design and Implementation section could be tightened slightly to improve readability, particularly for non-programming readers. It would also help to briefly explain how future community contributions will be moderated or version-controlled. Lastly, ensure consistency in notation (e.g., Vm vs. Vₘ, μm vs. um) throughout the figures and tables.

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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

**********

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

[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.]

Figure resubmission:

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 Dr Grill,

We are pleased to inform you that your manuscript 'PyFibers: An open-source NEURON-Python package to simulate responses of model nerve fibers to electrical stimulation' 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.

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

Best regards,

Alain Nogaret, PhD

Academic Editor

PLOS Computational Biology

Daniele Marinazzo

Section Editor

PLOS Computational Biology

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Suggestions for minor improvements have been met in the revised version. The manuscript is suitable for publication.