PONE-D-24-25575Non-synaptic plasticity enables memory-dependent local learningPLOS ONE

Dear Dr. Ferrand,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’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, edit to address concerns raised by the reviewers:

How do the trunk strength and eligibility traces behave over time? Please, discuss further the biophysical aspects of branch-strength plasticity. How would incorporation homeostatic changes impact the results? Would the performance of the model be impaired and to what extent?

In neuroscience, the synaptic tagging-and-capture model (Frey and Morris, 1997) of learning is a well studied phenomenological model which is also dependent on the interaction between an eligibility "tag" and protein synthesis for the long-term plasticity of memories to be consolidated. Do the authors consider any parallels between synaptic tagging and their own two-stage consolidation model?

Consider further discussion of experimental work by Disterhoft and modeling study by Janowitz and van Rossum who have shown that excitability changes are crucial for learning.

Overlaps have also been extensively studied with relation to temporally-related memories, and it has been found that temporally close memories tend to be stored in overlapping assemblies while distant memories do not (e.g. https://www.nature.com/articles/nature17955; https://www.cell.com/cell-reports/fulltext/S2211-1247(16)31401-2). Would it be possible to know whether the proposed learning framework results in overlapping representations for temporally close events (but not for temporally distant)? The authors should discuss the relevance of their observed overlaps to the studies of memory engram overlaps.

Minor corrections:

L276: Figure 3 legend: plastcitiy -> plasticity

L308: Figure 4 legend: simulus -> stimulus

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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

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

PLOS ONE 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

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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: I received this manuscript with already revisions included, I have asked the editorial office twice what the deal was. After all this is quite uncommon, especially in a 'you pay, we publish' journal such as Plos One.

The editor must be too busy...

In any case, I thought it was an excellent and well-written paper, and do not want to hold up publication.

Two small notes:

- The authors might be interested in tangentially older experimental work by Disterhoft who has shown that excitability changes are crucial for learning (later modelled by Janowitz and van Rossum).

- I was a bit confused by the Heaviside functions applied to h and a. These are already, AFAIK, >=0 , so the Heaviside function zeros out when a or h are exactly zero. That seems a bit odd and could be noise sensitive.

Reviewer #2: The paper explores the contribution of non-synaptic plasticity in

enabling memory-dependent local learning within neuronal networks.

According to their proposed model, pyramidal neurons regulate their

apical trunk excitability in a Hebbian manner, and the interplay

between synaptic and non-synaptic plasticity allows for effective

memory-dependent processing across various tasks, including memory

tests and question answering.

The authors have created a framework for memory consolidation that

relies on apical trunk strength plasticity for rapid information

storage and eligibility traces for synaptic plasticity. They derive

learning rules based on their model architecture, which allows for

local-only synaptic plasticity. They apply their model to different

learning tasks designed to test the effectiveness of local plasticity

involving delayed match-to-sample , radial maze and the bAbl

question-answering task. While the model shows adequate performance in

learning tasks (with some limitations), the main contribution of this

work is the development of a learning mechanism involving fast

intrinsic plasticity that is local, and thus biologically more

plausible.

The paper provides some interesting insights into mechanisms of learning.

The justification for their work and the analysis

of their results are well written and documented. While there are

limitations in the applicability of their framework, which are laid

out in the Discussion section, their model provides insights on

extra-synaptic local plasticity and is thus a valuable contribution to

the study of neuroscience and biologically plausible machine learning.

There are a few questions regarding the biological aspects of the

proposed learning model which I believe need to be addressed before

the publication of the paper:

1) How do the trunk strength and eligibility traces behave over time?

The authors' proposed algorithm involves permanent alterations to

branch strengths, however in real neurons these are modified by

homeostatic processes over time. As the branch strength modifications

are at the core of the authors' proposed learning framework, they

should discuss further the biophysical aspects of branch-strength

plasticity. What would be the impact on their results if they accounted for homeostatic changes? Would the performance of the model be impaired and to what extent?

2) The eligibility traces have a central role in regulating learning

in the proposed framework. The update rules for the eligibility traces

are rather complex as the authors admit, and it is not currently known

what could be the biophysical mechanisms that mediate such behaviors.

In neuroscience, the synaptic tagging-and-capture model (Frey and Morris, 1997) of learning

is a well studied phenomenological model which is also dependent on

the interaction between an eligibility "tag" and protein synthesis

for the long-term plasticity of memories to be consolidated. Do the

authors consider any parallels between synaptic tagging and their own

two-stage consolidation model?

3) The authors find that their model learns assembly representations

for entities in which overlaps represent associations between the

entities. Overlaps, however, have also been extensively studied with

relation to temporally-related memories, and it has been found that

temporally close memories tend to be stored in overlapping assemblies

while distant memories do not (e.g.

https://www.nature.com/articles/nature17955; https://www.cell.com/cell-reports/fulltext/S2211-1247(16)31401-2). Would it be possible to

know whether the proposed learning framework results in overlapping

representations for temporally close events (but not for temporally

distant)? The authors should discuss the relevance of their observed

overlaps to the studies of memory engram overlaps.

Minor corrections:

L276: Figure 3 legend: plastcitiy -> plasticity

L308: Figure 4 legend: simulus -> stimulus

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Reviewer #1: Yes: Mark van Rossum

Reviewer #2: No

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Non-synaptic plasticity enables memory-dependent local learning

PONE-D-24-25575R1

Dear Dr. Ferrand,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Gennady S. Cymbalyuk, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

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 #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

**********

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

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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 #2: Yes

**********

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

PLOS ONE 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 #2: 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 #2: The authors have addressed the points raised in the first review. They

have provided additional supplementary analysis which respond to the

points raised and they also provided additional discussion points. We

are thus pleased to recommend this paper for publication in its

current form.

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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.

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 #2: No

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