PONE-D-24-16549CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision makingPLOS ONE

Dear Dr. Vich,

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, improve justification of your model and approach to address questions raised by the reviewers.  How did you calibrate the model parameters? Discuss availability of electrophysiological data for calibration.

Please, provide a detailed description of the model framework including a summary or table that summarizes parameter open for change. What features are set by the design and what features can be changed. Is the number of neurons given in the supplements constant or extra neurons or populations can be added? What model formalisms are used for individual neurons, integrate-and-fire or conductance-based Hodgkin-Huxley? Do the authors use their own integration libraries or are they relying on other platforms, such as the NEURON simulation environment or the NetPyNE platform? Explain the design decisions/choices more thoroughly, and whether one could easily interface agents/environment software with standard modeling platforms mentioned above.

Please, review and consider discussing suggested articles

https://pubmed.ncbi.nlm.nih.gov/36943842/

https://pubmed.ncbi.nlm.nih.gov/32040519/

https://pubmed.ncbi.nlm.nih.gov/28408878/

In introduction, please provide some references for this statement:

"Although there are a wealth of biologically realistic simulations of cortical and non-cortical pathways in the literature, these are often designed to address very narrow behaviors and lack flexibility for testing predictions across multiple experimental contexts."

"We note that the inclusion of a biologically-realistic, dopamine-based learning mechanism, in contrast to the error gradient and backpropagation schemes present in standard artificial agents, represents an important feature of the model in CBGTPy."

Methods:

Is there a reason Ray was chosen rather than the more standard Mpi4py or MPI?

Fig. 3 - are there individual spiking neurons modeled or is this a rate-based model? Provide more details of the neuronal circuits and how they are simulated. Is the NEURON or BRIAN simulator used? Or is a custom-written neuronal simulator used?

Please, clarify which learning algorithm is primarily used in CBGTPy.

Section 3.3 - Why are excitatory/inhibitory currents considered optogenetic rather than electrical or other forms of stimulation?

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 [MC, JB, TV and JER are partly supported by National Institutes of Health (https://www.nih.gov) awards R01DA053014 and R01DA059993 as part of the CRCNS program.  CG and CV are supported by the PCI2020-112026 project, and CV is also supported by the PCI2023-145982-2, both funded by MCIN/AEI/10.13039/501100011033 (https://www.ciencia.gob.es/site/MICINN/aei) and by the European Union "NextGenerationEU"/PRTR (https://next-generation-eu.europa.eu/) as part of the CRCNS program. CG is also supported by the Conselleria de Fons Europeus, Universitat i Cultura del Govern de les Illes Balears (https://www.caib.es/sites/participacio/ca/l/conselleria_de_fons_europeus_universitat_i_cultura/) under grant FPU2023-008-B. The authors assert that these sponsors did not influence the study design, data collection and analysis, decision to publish, or preparation of the manuscript.].  

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Additional Editor Comments (if provided):

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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: Partly

Reviewer #3: Yes

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

Reviewer #1: N/A

Reviewer #2: N/A

Reviewer #3: N/A

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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

Reviewer #3: 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

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: The manuscript “CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making” presents a biologically relevant, extendable model of the cortico-basal gaglia-thalamo-cortical loop. This model is both tunable to reproduce decision-making tasks and has biological realism to gain insights into the mechanisms of decision making at play.

I appreciate versatility of the model. However, it’s not clear what features are set by the design and what features can be changed. In particular, is the number of neurons given in the supplements constant or extra neurons or populations can be added? I would appreciate a summary or table that summarizes this.

Another problem is how the lack of uniqueness of solution may affect their validity. System reconstruction is an inverse problem that has multiple solutions. In large tunable models, fitting procedures may end up with completely unphysiological or otherwise implausible parameter sets. These models fit better if provided not only behavioral, but also electrophysiological data to restrict the solutions. This is not the case for this model as far as I understand. Another way of modeling is much more restrictive models, where the number of neural groups and connections are minimal (or close to that) for a particular task. A couple of papers I can mention here are actually very relevant to the subject of this manuscript, but not cited (https://pubmed.ncbi.nlm.nih.gov/36943842/ https://pubmed.ncbi.nlm.nih.gov/32040519/ https://pubmed.ncbi.nlm.nih.gov/28408878/ ). However, in the current manuscript, it’s somewhere in the middle. Thus, it poses two problems: First is the lack of unique solution I mention before and/or second, the need to calibrate a vast number of parameters that are not tunable (or plastic). These parameters require data for their calibration, which may not be available. I suggest the authors discuss how these issues can be addressed in this model. The two given examples may serve for this purpose as well: how the parameters were calibrated?

The manuscript is very well written and organized, and I think a minor revision addressing the above comments will suffice.

Reviewer #2: Overall, the authors present and describe their CBGTPy software framework, which allows simulating

the cortico-basal ganglia-thalamic circuits using Python. The authors describe the software

with a lot of detail on how to use it and show several example tasks that CBGTPy supports. Overall,

the tool will help researchers conduct modeling/simulation experiments to test their ideas on

circuit mechanisms contributing to decision making and learning. The higher level of detail

compared to artificial neural networks is a welcome benefit for the computational neuroscience

community to explore.

Although the tool is described adequately from the naive end-user point of view, there are many details

on how the neural network models are constructed that are important for expert computational neuroscientists.

For example, what models are used for individual neurons, integrate-and-fire or conductance-based Hodgkin-Huxley

models? Depending on the answer to that question, further details on the modeling framework are needed. Do

the authors use their own integration libraries or are they relying on other platforms, such as the NEURON

simulation environemnt or the NetPyNE platform? While I commend the authors if they developed their own

platform, of course, that could lead to software fragementation and bugs, many of which may have been

solved already with the more established packages. For that reason, I would ask the authors to

explain their design decisions/choices more thoroughly, and whether they could have simply interfaced

agents/environment software with standard modeling platforms mentioned above.

In addition to the above questions, I wonder whether the authors could further relate their

work to existing, recent models which explored biologically-inspired learning in more complex,

simulated environments. I am curious how difficult would it be to extend the CBGTpy framework to allow simulation of

arbitrary goal-oriented environments with agents embedded within them? The research community would benefit

from being able to interface CBGTPy with software packages such as OpenAI's Gym, where more complex sensory

stimuli and decision making would be interesting to explore. Touching on these considerations

would broaden the appeal of the software. There are in fact some

recent papers that use multi-layer cortical spiking neuronal networks and dopamine-inspired learning

to train agents to play video games and perform other behaviors. Comparison of the present work with

these models would be interesting and provide further context for the author's work:

Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning, 2022

Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning, 2022

Perhaps a naive suggestion, but some of the games mentioned might be cast into the n-choice tasks, since

there are limited choices at every time step of the game, and a reward may be produced based on decision.

However, games have more complex sensory stimuli, which CBGTPy might not be able to handle, unless

it were interfaced with additional neuronal networks that processed the stimuli. If this were not possible,

the downside is of course that more naturalistic style behaviors could not be modeled with the CBGTPy framework.

Detailed comments:

in introduction, please provide some references for this statement:

"Although there are a wealth of

biologically realistic simulations of cortical and non-cortical pathways in the literature,

these are often designed to address very narrow behaviors and lack flexibility for testing

predictions across multiple experimental contexts."

"We note that the inclusion of a biologically-realistic,

dopamine-based learning mechanism, in contrast to the error gradient and

backpropagation schemes present in standard artificial agents, represents an important

feature of the model in CBGTPy."

I agree this is important, and could shed light on how biological systems actually learn.

Methods:

Is there a reaosn Ray was chosen rather than the more standard Mpi4py or MPI?

Fig. 3 - are there individual spiking neurons modeled or is this a rate-based model? I would like

to hear more details of the neuronal circuits and how they are simulated. Is the NEURON or BRIAN

simulator used? Or is a custom-written neuronal simulator used?

Q-learning process - I was under the impression that biologically-inspired learning based on dopamine

system was used. But then the authors talk about Q-learning, which is a more abstract learning algorithm

lacking many biological details/realism. Can the authors clarify which learning algorithm is primarily

used in CBGTPy?

Section 3.3 - Why are excitatory/inhibitory currents considered optogenetic rather than electrical

or other forms of stimulation? It's true that as implemented, they are targeted towards a specific

population, but I would not call it "optogenetic", as it bears little similarity to real

optogenetic mechanisms.

Reviewer #3: The authors have introduced an extensible generative package for modeling cortico-basal ganglia-thalamic pathway (GBGT), called CBGTPy, which simulates neuron responses of various experimental tests, mimicking the functioning of the CBGT of mammalian brain. Although the models are simplifications of the real brain circuit, it allowed the authors to address real experimental questions concerning neural dynamics and behavior. They thus the package in a variety of real life applications, simulating environmental and/or external stimuli, showing the related modifications that these stimuli induced in the CBGT dynamics. The authors carried out an excellent job in describing the package and documenting its parameters. Furthermore, they demonstrated the simplicity in the sintax used for defining these models of brain circuit, which allowed theorists and experimentalists to test novel hypothesis without needing to be expert software developer.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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CBGTPy: An extensible cortico-basal ganglia-thalamic framework for modeling biological decision making

PONE-D-24-16549R1

Dear Dr. Vich,

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 #1: All comments have been addressed

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 #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

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 #1: Yes

Reviewer #2: Yes

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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 #1: Yes

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 #1: All comments has been addressed. I'm glad to recommend it for publication. I'm glad to recommend it for publication.

Reviewer #2: Thanks for addressing my concerns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . I added extra "." character since the character limit is 100 to 20000 Characters, and the minimum bound is enforced.

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Reviewer #1: No

Reviewer #2: No

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