Review Reports - Algorithm for Describing Neuronal Electric Operation

Round 1

Reviewer 1 Report (New Reviewer)

Comments and Suggestions for Authors

The author introduces an updated algorithmic view of neuronal electrical operation, presenting a unified computational framework that emphasizes temporal factors such as propagation delays and the alignment of input timing. The approach is conceptually interesting as the algorithm integrates electrical charges iteratively and makes their upper limits depend on previous results, and updates synaptic weights dynamically.

However, while the paper offers a valuable conceptual model that addresses several current challenges in neuron simulation, it does not fully resolve these issues. The work lacks the technical and physiological detail needed for accurate biophysical modelling. For example, although it critiques oversimplified neuronal models, the paper does not incorporate essential mechanisms such as ion-channel kinetics, dendritic cable theory, or morphological compartmentalization. This omission limits the model’s applicability to detailed neurophysiological simulation. Empirical validation also appears limited, with only a single illustrative example (Figure 6). These concerns may be acceptable depending on the intended purpose of the algorithm; therefore, it would be helpful if the author clearly stated the scope and limitations of the model.

Minor concerns:

A careful proofreading is recommended to remove repeated text and fix missing elements. For example, the sentence in Lines 1160–1161 is duplicated, and both images are not displayed in Table 3.
It would be helpful to include a summary table comparing the strengths and limitations of the proposed model relative to existing models, as the current description remains somewhat vague.

Author Response

>Thanks for reviewing my paper. The changes are uploaded in a *.traced.pdf file.
The author's replies are edited in the questions and comments, beginning, per paragraph,
with a '>' ket symbol.

>It is a challenging task to explain how the 'unified thermodynamic/electrical model'
can be described in terms of a 'net electrical' model.
A generally accepted formalism exists only for electric RC circuits. Fortunately, its conditions are about 98% consistent with the biological case. The 'ordinary' (disciplinary) thermodynamics is not suitable at all
for describing neuronal operation, but it significantly affects the description.
Describing the underpinning science significantly exceeds the frame of describing the algorithm.
The unified model, with a complete physical discussion, is under publication,
and it is available as a preprint.

The approach is conceptually interesting as the algorithm integrates electrical charges iteratively
and makes their upper limits depend on previous results, and updates synaptic weights dynamically.

> Not necessarily. The used approach enables embedding biophysical model details
to any depth. The paper containing biophysical details is 70 pages long,
the technical and mathematical aspects are 60 pages, and the cross-disciplinary
physics is 40+30 pages. They are all separate publications, under 'Publishing' and 'In press'.
The present paper focuses solely on algorithmic aspects.

For example, although it critiques oversimplified neuronal models,
the paper does not incorporate essential mechanisms such as ion-channel kinetics,
dendritic cable theory, or morphological compartmentalization.

>The model was constructed in the spirit of Johnston&Wu:
"Despite the extraordinary diversity and complexity of neuronal morphology and synaptic connectivity, the nervous systems adopts a number of basic principles". Our model implements those basic principles,
with the internal ability to embed concrete mechanisms. This is computationally
effective, and in our object-oriented programming style, the implementation requires
only overloading one or more of the lower-level functionalities; that is,
replacing the generic mechanism with the required ion-channel kinetics
requires a couple of lines of programming (deriving a new neuron type with the
requested functionality) and recompiling the code. Overloading the code may require
extra computing time and parameters, but it does not limit the use of the framework.
Instead, any concrete mechanism, or a combination of them, can be implemented.
It is a different question, however, how the time-unaware concepts can be mapped
to the time-aware ones.

> The model in functional rather then morphological.
Of course, it is functional. There are different levels of abstraction,
which need different detailedness of implementation. Our goal was
to produce a time-aware "grey box" functionality of a neuron at input/output level,
without being too restrictive. The events are the only fixed points.
For example, anatomically, the axon belongs to the neuron membrane,
while from the point of view of information handling, the membrane is the sender,
the axon is the transfer channel; furthermore, the AIS is the encoder that forms
the signal which tranfers the encoded information.
Compartmentalization
is needed only when using "instant interaction" for the "fast ionic current" to imitate
the effect of the "slow ionic current" that our model handles differently.
Compartmentalization, in our approach, is just another (and in our view, less appropriate)
approach to consider (to compensate for) the finite speed of ions.
Using cable theory is a generally accepted fallacy. It is based on continuous amplitude
loss (decrease of voltage) based on leaking current, which is not the case for axonal
current amplitude and current. Unfortunately, when using appropriately chosen parameters,
it can produce speed values similar to the conduction velocity. The effect is also known
in technical electricity for strong currents (also the 'skin effect', that the charge carriers
flow near the outer surface), but the physical background is entirely different.
That discussion is valid only for electrons moving freely
in the field of the gridpoints of the solid. Ions in electrolites have fundamentally different
charge transmission mechanisms. They propagate as a wave front rather than a flow against resistance.
The issue is discussed in the author's biophysics paper.

Actually, more major interdependent parts should be discussed:
- the cross-disciplinary discussion of the underlying physical theory
- the new mathematics because of the oscillator type
- the time-aware biological operation from the point of view of computing
- the thermodynamic effect, due to the dual features of ions
- the computer architectural aspects of large-scale tasks, intensive communication, and time-aware implementation
- the algorithm implementing the above new features
Altogether, a new neuroscience book rather than a paper.

This omission limits the model’s applicability to detailed neurophysiological simulation.
> On the contrary, it enables embedding any mechanism, provided that those details
can be described in scientific terms, see above.

Empirical validation also appears limited, with only a single illustrative example (Figure 6).
> One more figure added, comparing HH's line shape with the one from the present simulation.
The task of the paper is to present the algorithm implementing the model,
rather than to validate the model (it is done in the biophysics paper).

These concerns may be acceptable depending on the intended purpose of the algorithm;
therefore, it would be helpful if the author clearly stated the model's scope and limitations.
> A concise summary of the physical model is added in a new section 6.1, and a paper
describing the model's details is in review in the European Biophysics Journal.
The model has no limitations when used to describe scientifically reasonable concepts.

Minor concerns:

A careful proofreading is recommended to remove repeated text and fix missing elements.
For example, the sentence in Lines 1160–1161 is duplicated,
> Sorry for that. My wireless mouse sometimes repeats some operations,
> this time a paste operation. Thank you for noticing that.

and both images are not displayed in Table 3.
>Sorry, they are downloaded from another website, and they are in .jpg form.
My own figures are in .pdf/.svg, so I packed only them, and the editorial compiler
did not give an error message.

It would be helpful to include a summary table comparing the strengths and limitations
of the proposed model relative to existing models, as the current description remains somewhat vague.
> The existing models use mathematical formulas without physical content
or in the best case, mathematical formulas grasped from an arbitrarily
chosen discipline of science, and can answer questions neither about phenomena
belonging to another discipline, nor about energy consumption, efficiency of
neuronal computation, information representation and processing, learning, and association.
The best model, proposed by Hodgkin and Huxley, was not more than a mathematical
description of their observations, and their underlying physical processes
were admittedly incorrect.
The proposed model uses a genuine cross-disciplinary science background,
and can describe all kinds of biological phenomena using science concepts,
without leaving controversies, mysterious phenomena, and open questions.
It can provide a full description of neuronal processes
and grasp the point at which the non-living matter
starts to show "signs of life", that is, turns into "living matter".
The subject of the present paper is just to show how an algorithm can implement
the model, and the task to describe the model in terms of science
remains in a biophysical publication.
Due to the different concepts, such a summary table would contain
items "not applicable" in one of the columns, in most lines.

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

This paper deals with the algorithm for describing and implementing neuronal electric operations while time handling in biology-targeting computations allowing the accuracy in simulating high-speed electronic circuits. A simplified ’net electrical’ model for neuronal operation is proposed keeping in mind the timing constraints and compared it to the almost exclusively used ’net electrical’ Hodgkin-Huxley model. My observations for this work are as follows:

The abstract is not clear and the contribution is aptly written.
The introduction in sense of computing and implementation should be presented in the tabular form for quick literature survey.
The structure of the paper should be presented in at the end of the introduction section.
The heading of the section 3 should be changed and keep it precise.
The Figures below Table 1 are missing.
What about the initial conditions for Eq. (3) and (4)? Without this information, it is difficult to assume the performance of the model.
What does the orange line denote in third plot in Figure 5?
Also Figure 5 should be clearly justified and explained.
Figure 6 is incomplete as there are some data points which are not scaled at y-axis and it make confusion.
The introduction of complexity in terms of some notations say 'Big O' could enhance the attention of readers form the computing side.

Comments on the Quality of English Language

The author needs to fix the typos and punctuations and other grammatical errors.

Author Response

>Thanks for reviewing my paper. The changes are uploaded in a *_traced.pdf file.
The author's replies are edited into the questions and comments, beginning, per paragraph,
with a '>' ket symbol.

This paper deals with the algorithm for describing and implementing neuronal electric operations
while time handling in biology-targeting computations, allowing the accuracy
in simulating high-speed electronic circuits. A simplified ’net electrical’ model
for neuronal operation is proposed, keeping in mind the timing constraints
and compared to the almost exclusively used ’net electrical’ Hodgkin-Huxley model.
My observations for this work are as follows:

The abstract is not clear, and the contribution is aptly written.
>I hope to significantly improve both.
It is a challenging task to explain how the 'unified thermodynamic/electrical model'
can be described in terms of a 'net electrical' model.
Fortunately, its conditions are about 98% consistent with the biological case.
A generally accepted formalism exists only for electric RC circuits.
The 'ordinary' (disciplinary) thermodynamics is not suitable at all
for describing neuronal operation.
Describing the underpinning science significantly exceeds the frame of describing the algorithm.
The unified model, with a complete physical discussion, is under publication,
and it is available as a preprint.

The introduction in the sense of computing and implementation should be presented
in the tabular form for quick literature survey.
>Fixed, the introduction is reformatted.

The structure of the paper should be presented in at the end of the introduction section.
> Thank you, fixed.

The heading of the section 3 should be changed and keep it precise.
>Fixed.

The Figures below Table 1 are missing.
>Fixed. Sorry, it was a packing problem.

What about the initial conditions for Eq. (3) and (4)? Without this information,
it is difficult to assume the performance of the model.
> Fixed; see the text in the MS

What does the orange line denote in third plot in Figure 5?
> Fixed in the caption.

Also Figure 5 should be clearly justified and explained.
> Changing environment 'figure' to 'algorithm changed numbering of figures.

Figure 6 is incomplete as there are some data points which are not scaled at y-axis and it make confusion.
> Fixed; a thermodynamic interpretation added

The introduction of complexity in terms of some notations say 'Big O' could enhance
the attention of readers form the computing side.
> Unfortunately, the interpretation of the complexity of technical operations strongly, and in a non-predictable way
(by orders of magnitude, see the publications by D'Angelo), deviates from the interpretation
of mathematical complexity. The technical complexity depends not only
on the architecture of the computers but also on the computing task itself.
No simple 'Big O' notation can be introduced; the dependence is strongly non-linear.
Please see the author's publication on supercomputers and ANNs for the form of the dependence.

Round 2

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

The responses are ok.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comments to Authors:

The recommendation of this reviewer is “Accept after minor revisions”, the following suggestions are for the authors’ reference.

(1) The structure of the article should be brought in line with the generally accepted MDPI structure. The manuscript does not need the section of “Contents”.

(2) It is recommended that Algorithms 1 to 5 be inserted into the manuscript in the form of figures.

(3) The discussion of the algorithm's performance (e.g., computational efficiency, scalability) is cursory. There is no clear benchmarking against existing neuronal simulators to demonstrate its advantages or limitations. The handling of synaptic plasticity and learning mechanisms is not well-defined. The manuscript mentions the importance of timing in synaptic interactions but fails to provide a concrete model for how these processes are implemented in the algorithm.

(4) The results are not presented in a way that clearly distinguishes between biological and technical time scales. The discussion of "grid time" and "heartbeat time" is confusing and not well-aligned with the figures. The manuscript's conclusion is weak and does not adequately summarize the key findings or address the limitations of the proposed algorithm.

Author Response

(1) The structure of the article should be brought in line with the generally accepted MDPI structure. The manuscript does not need the section of “Contents”.

> The "Contents" is intended for the comfort of the reviewer, and is removed for the "final" version. Fixed.

(2) It is recommended that Algorithms 1 to 5 be inserted into the manuscript in the form of figures.

> Sorry, I used another 'float' environment, by not knowing the journal's convention. Fixed.

(3)a The discussion of the algorithm's performance
(e.g., computational efficiency, scalability) is cursory.
> See the new subsection on implications, in front of the summary

(3)b There is no clear benchmarking against existing neuronal simulators
to demonstrate its advantages or limitations.
> See the new subsection on implications, in front of the summary

(3)c The handling of synaptic plasticity and learning mechanisms is not well-defined.
> See the new subsection on implications, in front of the summary

(3)d The manuscript mentions the importance of timing in synaptic interactions
but fails to provide a concrete model for how these processes are implemented
in the algorithm.
> See the new subsection on implications, in front of the summary

(4)a The results are not presented in a way that clearly distinguishes
between biological and technical time scales.
> The paper discussed all results in terms of simulated (i.e., biological) time

(4)b The discussion of "grid time" and "heartbeat time" is confusing
and not well-aligned with the figures.
> See section 2.3

(4)c The manuscript's conclusion is weak and does not adequately summarize the key findings or address the limitations of the proposed algorithm.
>New paragraphs to the Summary added

Thank you very much for your insightful questions and comments.
It is for the first time that a professional peer reviewer
successfully attempted to fit the "new understanding" into
the "old understanding". The questions
helped me to make the paper more complete.

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript contains biological and physiological issues.

The computational parts has issues too.

I'll list only some of them.

"action potential" is never used to define what a spike is. Or the currents used to define it. Or why the AIS is important in a neuron. The term soma cannot be found in the entire manuscript and is not an optional in a neuron.

"That means when simulating a system with a firing rate at 500 Hz, the system must be prepared to have a time resolution below 20 μsec". What is the issue with that? There are neuronal simulators that can integrate equations with a time step of just 25 μsec and they produce realistic voltage/time traces.

"We consider that the neuron is in a resting ground state [78] due to an external
perturbation it passes into a transient state, and by issuing the obsolete ions, returns to
its resting state." What is it an "Obsolete ions"? Or a Resting "ground" state?

"Precise time measurement (in the sense that measured by its effect) at this scale can be carried out only by using special hardware devices; the "time slices" the ordinary computers use are at least an order of magnitude larger." Previously mentioned neuronal simulators and a common desktop CPU are more than enough to simulate and see the results of a synaptic stimulus with a delay of just 0.1ms.

"biological time that physiologists record when observing biologically meaningful events".

What is it the biological time? Chemistry, biology and physiology follow the standard international system of units. The scale is small but everything is based on ms or s. The same for the computation of a CPU.

"wall clock time that the programmer records when the computation reaches code parts 174
that simulate biologically meaningful events" Last time I checked, wall clock is called "real time" which is never described in the text. It follows the international system of units that describe how much time in reality has passed. To not be confused with the time passing in the actual simulation.

"The simplest one is to compare the total simulated time to the total wall-
clock time and assume that, on average, a constant factor relates the biological time to the
simulation time." This statement makes no sense.

“"spikes are processed as they come in and are dropped if the receiving process is busy over several delivery cycles" [38]. These are clear signs that, mainly due to the differing operating principles, the vast technical systems cannot handle such an amount and/or density of events that biological simulations need.” Nobody will ever build a neuronal network in which spikes are “dropped”. It would make the input incomplete and the output non understandable from other neurons. There are rules that cannot be bend to process information in a faster way.

The text between 1088 and 1154 is the same repeated two times.

Comments on the Quality of English Language

There are grammatical errors here and there. The manuscript contains many phrases that are difficult to understand.

Author Response

The manuscript contains biological and physiological issues.

The computational parts has issues too.

I'll list only some of them.

>> "action potential" is never used to define what a spike is. Or the currents used to define it. Or why the AIS is important in a neuron. The term soma cannot be found in the entire manuscript and is not an optional in a neuron.
-- I don't even grammatically understand what the reviewer claims? or want to ask? or missing?
I do not think that in a paper dealing with the algorithmic aspects of the
timing requirements of the neuron's operations, I have to attach a textbook chapter of
biological notions that otherwise have nothing to do with the abstract operations the paper discusses.
In the introduction, it is said:
"At the level of abstraction we use, one does not need to consider all biological details
since "despite the extraordinary diversity and complexity of neuronal morphology and
synaptic connectivity, the nervous systems adopt a number of basic principles" [14] and we
proceed along those basic principles, emphasizing the need for precise timing."
I do not think I must provide a paper describing applying a programming methodology
to an abstract electrical model, more biological details than the "cellular neurophysiology"
textbooks thinks sufficient.

>> "That means when simulating a system with a firing rate at 500 Hz, the system must be prepared to have a time resolution below 2000 μsec". What is the issue with that? There are neuronal simulators that can integrate equations with a time step of just 25 μsec and they produce realistic voltage/time traces.
-- I am afraid that the reviewer mismatches the time resolution and the integration time step.

>> "We consider that the neuron is in a resting ground state [78] due to an external
perturbation, it passes into a transient state, and by issuing the obsolete ions, returns to
its resting state." What is it an "Obsolete ions"? Or a Resting "ground" state?
I believe most readers are familiar with the resting state of a neuron. Otherwise, it is a ground state,
or stable state, in which it remains without perturbation for a long time;
in which it receives a perturbation and to which it returns after performing the transients.
A certain number of free ions generated that voltage,
and to return to the ground state (or resting state), the system must emit the obsolete ions.
It can be found in good elementary textbooks, such as Johnston & Wu, page 12.

>> "Precise time measurement (in the sense that measured by its effect) at this scale can be carried out only by using special hardware devices; the "time slices" the ordinary computers use are at least an order of magnitude larger." Previously mentioned neuronal simulators and a common desktop CPU are more than enough to simulate and see the results of a synaptic stimulus with a delay of just 0.1ms.
-- The simulators run on operating systems that have a "time slice", say 1/100 sec, plus the cores share several threads.
I am afraid that the reviewer mismatches the "precise time measurement" and the time delay of the simulated time, plus the CPU cycle time, plus the system scheduling time.

>> "biological time that physiologists record when observing biologically meaningful events".
>>What is it the biological time? Chemistry, biology, and physiology follow the standard international system of units. The scale is small but everything is based on ms or s. The same for the computation of a CPU.
-- I am afraid the reviewer mismatches the time units with measuring the time.
I am sorry, but the reviewer lacks the fundamental notions of time handling. May I call attention to
the excellent book about SystemC, which I cite as reference [26] in my paper. I cite here
the first few sentences of its chapter 5:
"As a SystemC simulation runs, there are three unique time measurements: wall-clock
time, processor time, and simulated time:
• The simulation’s wall-clock time is the time from the start of execution to
completion, including time waiting on other system activities and applications.
• The simulation’s processor time is the actual time spent executing the simula-
tion, which will always be less than the simulation’s wall-clock time.
• The simulated time is the time being modeled by the simulation, and it may be
less than or greater than the simulation’s wall-clock time. For example, it might
take 2 seconds by your watch (wall-clock time) to simulate 15 ms (simulated
time) of your design, but it may only take 1 second (processor time) of the CPU
because another program was hogging the processor."
I agree that not all people must be aware of those fine distinctions of
computing-related times, but I think that reviewers of a paper that
discusses a biology-related subject must be among the few selected ones.

Given that it is an introductory level textbook, I do not think I need to repeat
more. If the reviewer has the ambition to review a paper about simulating time-related activity,
with the demand of precisely aligning simulated events, I seriously suggest
considering at least skimming that chapter.

>> "wall clock time that the programmer records when the computation reaches code parts
that simulate biologically meaningful events" Last time I checked, wall clock is called "real time" which is never described in the text. It follows the international system of units that describe how much time in reality has passed. To not be confused with the time passing in the actual simulation.

>> "The simplest one is to compare the total simulated time to the total wall-
clock time and assume that, on average, a constant factor relates the biological time to the
simulation time." This statement makes no sense.
-- It has. A very serious concern that the reviewer does not understand. See the definitions the undergraduate
textbook provides, my utilization of the notion in the MS, and the blue text lines
in the algorithms. They are in blue to emphasize that real-life computers insert
non-payload but time-consuming tasks between the payload activities.

>>“"spikes are processed as they come in and are dropped if the receiving process is busy over several delivery cycles" [38]. These are clear signs that, mainly due to the differing operating principles, the vast technical systems cannot handle such an amount and/or density of events that biological simulations need.”
>>Nobody will ever build a neuronal network in which spikes are “dropped”.
The reviewer likely overlooked the fact that I cited the sentence. The last author of the cited publication is Steve Furber,
the designer of the computer SpiNNaker, the Member of the Royal Academy, one of the leading scientist of the Flagship Project of the European Union in Julich, and the cited paper was published in one of the leading journals of neuroscience.
Why does the reviewer think that the authors, the EU, and the journal are lying?
Why he does not read the sentence in the cited report on an EU flagship project?

Real-life computers, especially the ones using massive communication,
behave differently. They are not mathematical objects; they are engineering constructs.
I would suggest to skim the cited reference, especially when it is a click away,
before claiming I lie in my paper. Please. No problem if the reviewer does not understand the
notions of computer technology (most of the colleagues with a mathematical background do not),
or if he steps back from reviewing a paper dealing with a subject he lacks the background to.

It is a problem, however, if he suggests rejecting a paper he lacks the background to.

>>It would make the input incomplete and the output non understandable from other neurons. There are rules that cannot be bend to process information in a faster way.
--Yes, I agree with the last two sentences. And I exactly say what the reviewer said in those last two sentences.

I am trying to explain that it was a nonsense solution. However, they did that.
I am trying to explain what are the rules that "cannot be bent" and illustrate what happens when one attempts to bend the rules. That is what my paper is about: the computing time, clock time and biological time are different.

The text between 1088 and 1154 is the same repeated two times.
-- Thanks for noticing that. A copy&paste error. Fixed.

Thanks for commenting my paper.

Reviewer 3 Report

Comments and Suggestions for Authors

The author discusses about the operation of biology and the physical/mathematical processes in living matter, which are difficult for algorithmic description. He highlights the question of simulating the networks operation in comparison with the operation of spiking artificial neural networks.

My following comments should be taken into consideration to revise the paper version.

It would be better to represent an electrical circuit, which has the description by equation (1), and show the input and output signals.
What values of R and C are expected to be in equation (1)?
What about the transient and steady-state processes in the electrical circuit described by equation (1).
To my mind, Summary should be expanded by means of including the obtained results in more detail.

Author Response

My following comments should be taken into consideration to revise the paper version.

It would be better to represent an electrical circuit, which has the description by equation (1),
and show the input and output signals.
> Table 1, with the good and wrong circuits, equations, and in/out signals inserted
Please consider, however, the limitations near the table

What values of R and C are expected to be in equation (1)?
> The equation is standard for electrical circuits, and it works with any parameters.
For neurons, the simulations used 1 GOhm and 100 pF when working
in the 100 pA and 10 mV range

What about the transient and steady-state processes in the electrical circuit described by equation (1).
> As the paper introduced, a neuronal cycle is composed of three stages, where different compositions of
currents and gradients are included, and from biological reasons, the neuron handles them differently.
Unlike in the classic model, a current flows if the membrane's potential is above the resting value.
That means that no leakage current exists in the resting state.
In the transient stage, the capacitive current (in the classic instant interaction speed picture), see
the figure with the gradient and the AP, actively forms the output voltage. It is a native feature
of the serial circuit to produce a voltage with opposite polarity. The shape of the input current defines
the shape of the output voltage. In the finite-speed interaction picture, the shape originates
from the finite size of the membrane, where the synaptic current has a finite speed (as defined by Stokes-Einstein).
See also https://doi.org/10.48550/arXiv.2507.11448 about how the concentrations and physical processes
produce and change the operating conditions.

To my mind, Summary should be expanded by means of including the obtained results in more detail.
> New paragraphs added to the summary. Thanks for suggesting that.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The author has replied to the comments of the previous round of review, and has made detailed revisions to the article, recommending acceptance.

Reviewer 2 Report

Comments and Suggestions for Authors

Line 16-17 "their network" It is missing the subject.

Line 19 "considers neuronal current as charged ions". An ion is always charged. A cation (sodium na+ or calcium ca++) or an anion (chloride Cl-). Their passing the membrane generate currents as it was defined by Hodgkin and Huxley mathematical model in 1963. The same happens in reality too.

Line 22 "The algorithm that applies this model". Should be the opposite. The model contains an algorithm that does something.

Line 29 - 33. I had to check the phrase 3 times to understand that was taken from "somewhere". Ok the human brain project failed in the reconstruction of the whole human CNS into a computational model. But why it failed? Because finding electrophysiological data from humans can only be done in very specific cases. It was too ambitious but it did not failed for lacking of modelling instruments.

Line 50 - 55. It is a single long phrase and I do not agree with the content of line 52 because the premise is misplaced. Not a single biologist will ever believe that a single equation can reproduce all the activities of a neuron. But with multiple equations and good quality physiological data, to be used in those equations, it is possible to generate a model that can capture some of the electrical properties of a neuron. Has been done for the past 30 years so it isn’t a new concept. HH is rather old and needs to be improved but it still works fine.

Line 154 That frequency is not "normal". Usually a neuron respond to a synaptic input between 2-3 Hz up to 300 Hz. In specific cases it can reach even 1kHz. And by the way with a model made with the HH mathematical model it can reach that frequency without issues and can be solved on a normal desktop CPU.

Line 1032 - 1033. The charge carriers are slow (typically positive) ions, which are about 50,000
times heavier than electrons". Ionic current as "so slow" that a membrane can pass from steady state, spike, recovery, steady state in just 1ms. And, by the way, an ion is an atom in which an electron or more are either missing or in excess.

Line 1210 "Their followers never attempted" Maybe I’m missing something but the author is perhaps stating that all the models, based on HH formalism, built in the past 30 years, are all wrong? It is a bold statement.

Line 1212 - "No other simulator works with a correct physical background." Maybe because the concept is to simulate a physiological model? Which is a mix of biophysical and biochemical properties.

Line 1225- 1227. If for "Our model reproduces, with a high precision and without parametrization, how a biological neuron behaves" the author means the curves in figure 8 than this model does not works at all. That is not a physiological spike. Maybe it makes sense in physics but not in Neurophysiology. See comment about figure 8.

Line 1238 1242 "A neuron operates in tight cooperation with its environment (the fellow neurons)". A neuron can operate alone, it will be useless but it will be "fine". Of course, on the long run, without interacting with other neurons it could die. But the most important interaction is with Glial cells. They are the glue that allows neurons to operate.

Line 1248 "They receive multiple inputs at different offset times from the different upstream neurons and in different stages". There are neurons that respond only to synchronous bursts of input. Hundreds of input at the same time, in different location on the same dendritic tree. And the spatial integration is not an option. Or the backpropagation.

Line 1252 - "natively explains synaptic plasticity" Another bold statement. All types of synaptic plasticity? STP? LTP? STD? LTD? STDP? calcium dependent facilitation? etc.. all of them?

Line 1264 - Another bold statement. "In our perfect implementation". The author should separate the physiological aspect from the pure IT aspect. The IT part can be "perfect" but the physiological part begs to differ.

Figure 8. A synapse that fire at 0 instead of 1 is counterintuitive. A neuron will never generate a spike from -5 or -10mV (even during a strong current injection happens rarely) and it will be very diffult for the spike to go over 60mV since the reversal potential of the sodium channel is 60mV. Reaching it will makes the driving force of the sodium current equal to zero and so the current, which will start to go outward instead of inward. So a spike reaching over 110mV does not exist in reality.

To summarize, the author states that this "perfect physical model" can reproduce the activity of a neurons without fitting ionic channels, without a morphology and that the results are "perfect" compared to the results obtained by the "follower" of the erroneuros HH mathematical model. If it is so "perfect" why the only "section" mentioned is the AIS whereas a neuron has a soma, dendrites and an axon. A "follower" usually starts with a soma and a couple of ionic channels, before entering the dendritic and axosomatic realms. And since there are no neurons composed by only a section, at least in the real world, this "perfect" model does not match what can be done with a classic multi compartmental model made with HH and a morphology.

Comments on the Quality of English Language

The manuscript needs a complete revision since, in many cases, phrases are constructed in an awkward way.