PCSY-D-24-00044

Autonomous and Ubiquitous In-node Learning Algorithms of Active Directed Graphs and Its Storage Behavior

PLOS Complex Systems

Dear Dr. Wei,

Thank you for submitting your manuscript to PLOS Complex Systems. After careful consideration, we feel that it has merit but does not fully meet PLOS Complex Systems'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.

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We look forward to receiving your revised manuscript.

Kind regards,

Jaya Sreevalsan-Nair, Ph.D.

Academic Editor

PLOS Complex Systems

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Additional Editor Comments (if provided):

This paper proposes a model of memory mechanism in a biological neuronal network using directed graphs, with nodes having self-adaptation properties. The nodes also use local information for decision making. The paper proposes a parallel distributed information access algorithm based on the node scale of the graph, thus realizing global node collaboration for resource utilization through local mechanisms. The experiments on network capacity, fault tolerance and robustness, has yielded results indicating better performance in sparser network structures.

There are a few recent works that have not been cited which could potentially lead to interesting discussions here:

* Wei, H. and Li, F., 2023. The storage capacity of a directed graph and nodewise autonomous, ubiquitous learning. Frontiers in Computational Neuroscience, 17.

* Senk, J., Kriener, B., Djurfeldt, M., Voges, N., Jiang, H.J., Schüttler, L., Gramelsberger, G., Diesmann, M., Plesser, H.E. and van Albada, S.J., 2022. Connectivity concepts in neuronal network modeling. PLOS Computational Biology, 18(9), p.e1010086.

The need to mention these studies is also to bring in more connections to the biological aspects of the problem statement.

There are concerns raised by reviewers on lack of biological significance (R2), insufficient presentation of evaluation and validation (R2), missing distribution-based representation of probabilistic behavior (R3), gaps in the writing and presentation (R1, R2, R3).

We hope the reviewers' and afore-mentioned comments help to improve the manuscript. Thank you for your contribution to PCSY.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Complex Systems’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Complex Systems does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this paper, the authors delve into how biological neuronal networks encode, store, and retrieve information, aiming to model the brain’s memory system, which they model as active directed graphs. At the same time, they also propose a parallel distributed information access algorithm based on the node scale of active directed graph. Experimental results show that the proposed method is effective and has better performance in terms of network capacity, fault tolerance and robustness. In general, the idea makes sense, the paper is very well written, and the evaluation is well made.

However, there are still some issues in this article that need to be explained clearly. Here below are my comments.

(1) The performance indicators of the vertical axis in Figures 8 to 10 need to be specified.

(2) It is best to cite references for other comparison methods in Table 4.

(3) The layout of the paper needs to be further improved, and each paragraph needs to be aligned at both ends.

(4) The references section needs to cite recent work, especially research from the last 3 years.

(5) Can the authors describe how the experimental samples for section 3 were obtained? Is there a correlation between the 1000 samples in Table 2?

(6) In Section 2.1, connected subgraphs are considered as a resource. Why could subgraphs serve as the physical manifestation of memory in directed graphs? Why is information stored in a directed graph as a subgraph?

Reviewer #2: Biological justification and feasibility:

- Provide stronger biological grounding for model assumptions and parameters

- Cite relevant experimental findings to support model choices

- Discuss limitations and alternative biological mechanisms

- Evaluate model's biological realism and feasibility under neurophysiological constraints

Evaluation and validation:

- Compare model's behavior and predictions against empirical memory data and phenomena

- Validate model's cognitive plausibility and explanatory power beyond artificial benchmarks

- Quantify statistical significance of performance differences across conditions

- Report means and standard deviations of metrics across multiple simulation runs

- Perform sensitivity analyses on key model hyperparameters

- Compare model's performance against relevant baselines and benchmarks

Clarity and presentation:

- Proofread and edit the text to correct grammatical errors and improve readability

- Provide more background information and explanations of technical concepts

- Improve figures and tables to be more self-explanatory and visually compelling

- Provide more complete and precise descriptions of methods and algorithms

- Expand introduction and conclusion to better motivate significance and implications of the wo

Reviewer #3: 1. Lots of inspiring concepts and result. Therefore, basically Yes.

2. If I understand the results correctly, the capacity the network

behaves on probability and should have some kind of distribution.

Therefore, please show them not by a single deterministic digit but with some kind of distribution information.

3. Yes.

4. Please keep consistency of notation between the figures, where nodes (network vertices) are

denoted by capital alphabets beginning from A,

and the other parts like the algorithm description or formulae where

nodes are denoted by lowercase u, v, ...

5. Would like to see a hardware implementation base on this concept. Please consider as the future study.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Autonomous and Ubiquitous In-node Learning Algorithms of Active Directed Graphs and Its Storage Behavior

PCSY-D-24-00044R1

Dear Professor Wei,

We are pleased to inform you that your manuscript 'Autonomous and Ubiquitous In-node Learning Algorithms of Active Directed Graphs and Its Storage Behavior' has been provisionally accepted for publication in PLOS Complex Systems.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team. 

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.

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

Best regards,

Jaya Sreevalsan-Nair, Ph.D.

Academic Editor

PLOS Complex Systems

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This paper proposes a model of memory mechanism in a biological neuronal network using directed graphs, with nodes having self-adaptation properties. The nodes also use local information for decision making. The paper proposes a parallel distributed information access algorithm based on the node scale of the graph, thus realizing global node collaboration for resource utilization through local mechanisms. The experiments on network capacity, fault tolerance and robustness, has yielded results indicating better performance in sparser network structures.

All reviewers are satisfied with the changes made. Please include the description of STDP in p3, l51, as suggested by R3, in the camera-ready proof.

Thank you for your contribution to PCSY.

Reviewer Comments (if any, and for reference):

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

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2. Does this manuscript meet PLOS Complex Systems's publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: I don't know

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4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Complex Systems does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Authors have addressed all my concerns. Good work.

Reviewer #2: Thank you for incorporating the suggested changes. The revised paper looks really strong. Thank you!

Reviewer #3: In P3, L51, new term STDP is introduced without description, and not sure it is common enough to skip description. Other than that, all comments have been addressed.

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7. 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.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: None

Reviewer #3: No

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