PCOMPBIOL-D-26-00095

An in silico framework for modeling and analyzing in vivo neuron-network mechanisms

PLOS Computational Biology

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Academic Editor

PLOS Computational Biology

Hugues Berry

Section Editor

PLOS Computational Biology

Journal Requirements:

1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full.

At this stage, the following Authors/Authors require contributions: Bjorge Meulemeester, Arco Bast, Maria Royo, Rieke Fruengel, and Marcel Oberlaender. 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

Reviewer #1: This manuscript presents the In Silico Framework (ISF), an open-source environment for generating and analyzing ensembles of biologically detailed neuron-network models. The central conceptual contribution, shifting the focus from single “tuned” models to diverse ensembles that capture biological degeneracy, is a timely and significant step for computational neuroscience. The integration of multi-scale data to achieve “model consensus” is a powerful approach for making testable in vivo predictions. The framework is technically impressive and well-demonstrated through its application to the rat barrel cortex. I support publication, as ISF aligns perfectly with the scope of PLOS Computational Biology. However, the manuscript would benefit from several specific clarifications to help potential users understand the practical deployment and limitations of the tool.

Minor Revisions / Clarifications:

Data Requirements & Accessibility:

While the manuscript notes that statistical methods can be used to generate neuropil models as an alternative to dense EM reconstructions, many researchers may lack such resources or pre-established statistical models. Could the authors more explicitly define the “minimum viable data” required to use ISF? Even a short schematic outlining required versus optional inputs would greatly increase accessibility for potential adopters.

Computational Cost:

To help the community assess the feasibility of adopting ISF, it would be helpful to provide approximate estimates of computational requirements (e.g., CPU-hours, peak memory, and storage) for a representative workflow. Even order-of-magnitude estimates would be informative for groups without access to large-scale computational infrastructure.

Troubleshooting “Model Failure”:

The authors state that if no model reproduces an in vivo observation, the generation process requires further refinement. This is a critical juncture for any user. Could the authors expand on the recommended diagnostic workflow in such cases? For instance, how might a researcher systematically distinguish between a missing biological mechanism versus an insufficiently explored parameter space or overly restrictive empirical constraints? Providing guidance on this would strengthen the methodological transparency of the framework.

Mathematical Form of Reduced Models:

The model reduction section remains somewhat abstract. While GLMs are mentioned in the example application, it is unclear what “minimal set of parameters” means in the general case. Does ISF provide a library of reduction methods (e.g., GLMs, kernel-based approaches, specific filter architectures), or is the user expected to define the mathematical form entirely? A clearer description of the interface for implementing reduced models, and what is provided out of the box, would significantly improve clarity in this important conceptual component of the framework.

Overall, I believe this manuscript represents a valuable and timely contribution. With clarification of the above points, it will serve as an important resource for researchers interested in ensemble-based, mechanistic modeling of in vivo neural activity.

Reviewer #2: Summary:

The manuscript by Meulemeester et al. introduces the In-Silico Framework (ISF) for iterative investigation of single neuron function, bridging in silico predictions with in vivo observations. The proposed framework is a valuable contribution to the field, as it offers a way to integrate the expanding body of experimental data to advance mechanistic understanding of neural activity in vivo. The authors provide compelling examples of the framework’s application based on their prior research. However, the generalizability of the framework is not immediately clear from the manuscript or the provided code. That said, the manuscript is well-written and visually engaging, with clear illustrations. The accompanying code is publicly available, well-documented, and includes helpful tutorials.

Positive aspects:

1. The proposed workflow effectively bridges in silico simulations and in vivo observations, demonstrating a well-structured and systematic approach. It incorporates rigorous sensitivity analysis and parameter exploration, enabling targeted manipulations and identifying key mechanisms of neuronal computations.

2. The provided examples are highly compelling and provide solid evidence of the predictive power of the framework.

2. The visuals produced by the package are of high quality.

3. Performance considerations are addressed by the use of scalable tools such as Dask, ensuring efficiency and applicability to large datasets.

Major comments:

1. I recommend balancing the content of the Introduction and Discussion sections. The authors might follow the suggested outline or shorten the Introduction in a different way:

¶1: Motivate the need for an iterative in silico–in vivo approach.

¶2: Summarize the current state of the art and highlight what is missing.

¶3: Introduce the framework and emphasize its novelty.

Comparisons with top-down models, as well as specific examples, might be better suited for the Discussion section (see the following comment). Along the same lines, consider revising the Abstract to provide a more concise overview of the work. Currently, it follows a simular structure to the Introduction. The decision to incorporate these stylistic changes is left at the authors' discretion.

2. The Discussion section would benefit from a more focused comparison with existing modeling software. Currently, it reads as a general overview of the neuronal modeling field challenges rather than a targeted analysis of the proposed framework. The framework has both advantages and limitations compared to existing software. A comparison with relevant software and modeling approaches would provide valuable context for readers:

Dura-Bernal et al., 2019 https://doi.org/10.7554/eLife.44494 (NetPyNe),

Dai et al., 2020 https://doi.org/10.1371/journal.pcbi.1008386 (BMTK),

Reva et al., 2023 https://doi.org/10.1016/j.patter.2023.100855,

Roy and Narayanan 2023 https://doi.org/10.1113/JP283539,

Makarov et al., 2025 https://doi.org/10.7554/eLife.103324.3 (DendroTweaks),

Wybo 2025 https://doi.org/10.1101/2025.06.26.661734 (NEAT),

Deistler et al., 2025 https://doi.org/10.1038/s41592-025-02895-w (Jaxley).

3. The “neuron-network” term is potentially misleading, as it may imply that networks of multicompartmental biophysical neurons are explicitly modeled within the framework. To avoid confusion, I suggest replacing the “neuron-network models” with “network-embedded neuron models” throughout the manuscript. This term is already used by the authors in the Methods and tutorials. Additionally, comparisons with multiscale network models, such as those described in Billeh et al. (2020) and Markram et al. (2015), may further contribute to this ambiguity and should be addressed carefully to maintain relevant scope.

4. The Results and Methods sections don't clearly explain what the reduced model is. Is it still a biophysical model, or an abstract mapping from input to output? Does it involve morphological abstraction as a point neuron? The manuscript needs to clearly explain what results from the reduction.

5. Consider making the code more configurable and less reliant on hardcoded data to ensure that the framework generalizes well to other models. Including model-specific code directly into the core packages raises questions about how well this pipeline extends to other models:

- in_silico_framework/biophysics_fitting/L5tt_parameter_setup.py

- in_silico_framework/singlecell_input_mapper

- in_silico_framework/visualize/voltage_trace_visualizer.py

Do not mix examples and the core package code:

- in_silico_framework/biophysics_fitting/hay

- in_silico_framework/mechanisms

Replace hardcoded functions with flexible, parameterized alternatives:

- in_silico_framework/single_cell_parser/cell_modify_functions/soma_current_injection.py

- in_silico_framework/biophysics_fitting/setup_stim.py

In tutorials, consider using preprocessed data to focus on the framework's functionality rather than data preparation:

- scale_apical_morph_86

Model-specific configurations in in_silico_framework/config are the correct approach, they are easy to replace and clearly communicate to the user what needs to be adjusted for a custom model. Consider separating configurations from source code elsewhere.

This list of examples isn't exhaustive, but rather it is what became evident from an initial review of the codebase. If generalizability is already ensured, remove the hardcoded parameters to avoid giving a false impression.

Minor comments:

1. The suggested workflow largely relies on BluePyOpt, making it important to clarify the framework’s added value by comparing its data management capabilities to those of BluePyOpt.

2. The manuscript doesn't clarify which parameters can be modified and optimized e.g., whether distributions and kinetic parameters of ion channels can be changed, or only uniform channel conductances. The tutorials contain this information, but it might be beneficial to add it in the manuscript.

3. How would a channel distribution in oblique dendrites of a CA1 neuron be set, for example, given that a morphology file specifies them as part of the apical dendrite?

4. The purpose of encapsulating Python packages under the Interface class (such as I.sns, I.shutil, I.os) is unclear. Consider making it more straightforward when relying on external packages, or provide utility functions to simplify setting up plots, paths, etc.

5. Please clarify the rationale for using .hoc for morphology files instead of .swc. Additionally, what advantages does the custom .hoc reader provide compared to NEURON's default implementation?

6. The documentation would benefit from a simpler introductory tutorial. The configurations and setup might be better suited for a separate tutorial. An ideal introductory tutorial would clearly demonstrate the high-level workflow and core concepts of the framework. Splitting the code into smaller chunks with additional explanations between sections could improve clarity.

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Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Dear Prof. Dr. Oberlaender,

We are pleased to inform you that your manuscript 'An in silico framework for dissecting the mechanistic origins of in vivo recorded neuronal activity' has been provisionally accepted for publication in PLOS Computational Biology.

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Academic Editor

PLOS Computational Biology

Hugues Berry

Section 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 #2: The authors have improved the quality of the manuscript and the source code and have successfully addressed the comments from the previous round of revisions. In its current state, the In Silico Framework (ISF) has the potential to improve neuronal models and generate testable predictions for in vivo experiments, and it should be further refined through community feedback. I therefore support publication of the manuscript in PLOS Computational Biology as a valuable contribution to the field.

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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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Reviewer #2: No