Complex Dynamical Behavior of Locally Active Discrete Memristor-Coupled Neural Networks with Synaptic Crosstalk: Attractor Coexistence and Reentrant Feigenbaum Trees

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper investigates the complex dynamical behavior of locally active discrete memristor coupled neural networks. The topic is interesting and timely, while some minor issues should be further clarified before acceptance.

1. What are the main research gaps in the field of complex dynamical behavior analysis of memristor coupled neural networks?

2. Considering there are many research papers referring to the dynamical behavior analysis of memristor coupled neural networks the contributions are suggested to highlight again. Meanwhile, authors are suggested to discuss these different research papers in the revised version.

3. Some related works published within three years (10.1109/TCE.2023.3263672; 10.1109/TCSVT.2023.3275708) are suggested to add in the revised version.

4. The limitation and the future directions are suggested to discuss (if possible, authors can set a separate part) in the revised version.

Author Response

Thank you for your kindly help. Please check the attachments.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

In this work, the authors proposed a new discrete model on neural networks to discuss the complex dynamic behavior of DMCAN graphs by coupling two Aihara neurons with two locally active discrete memristors. Overall, it provides a meaningful work. I would like to recommend accepting this manuscript if the authors can fully address the following concerns:

 

(1) 2.1. Model of the memristor: The author provided equations for the mathematical model, but the explanation of the physical meanings of variables and parameters in the model is not detailed enough. It is recommended to add a thorough explanation of the physical meanings of each variable and parameter and their selection criteria. For example, the specific sources and adjustment basis of a, b, c.

(2)       In the analysis of multi-steady state phenomena and dynamic behavior, the initial conditions and parameter settings have a significant impact on the results, but the rationality and selection process of these settings are not explained in detail in the work.

(3)       The authors need to provide the process of calculating the Lyapunov exponent and bifurcation diagram.

(4)   I believe that the numerical simulation and discrete model can be meaningful for memristor coupling, however, can you give some results of comparison of actual devices and your model, and possible references are as follows. Mater. Horiz., 2024, 11, 2886, 10.1039/d4mh00064a; Nano Lett. 2024, 24 (12), 3581-3589, 10.1021/acs.nanolett.3c04073; Nat Commun 2024, 15, 4318, 10.1038/s41467-024-48399-7.

(5)       The conclusion mentions the application of DMCAN maps in various fields, so its application should be further demonstrated in the introduction.

(6)       Language and grammar. This paper must be edited.

Comments on the Quality of English Language

In this work, the authors proposed a new discrete model on neural networks to discuss the complex dynamic behavior of DMCAN graphs by coupling two Aihara neurons with two locally active discrete memristors. Overall, it provides a meaningful work. I would like to recommend accepting this manuscript if the authors can fully address the following concerns:

 

(1) 2.1. Model of the memristor: The author provided equations for the mathematical model, but the explanation of the physical meanings of variables and parameters in the model is not detailed enough. It is recommended to add a thorough explanation of the physical meanings of each variable and parameter and their selection criteria. For example, the specific sources and adjustment basis of a, b, c.

(2)       In the analysis of multi-steady state phenomena and dynamic behavior, the initial conditions and parameter settings have a significant impact on the results, but the rationality and selection process of these settings are not explained in detail in the work.

(3)       The authors need to provide the process of calculating the Lyapunov exponent and bifurcation diagram.

(4)   I believe that the numerical simulation and discrete model can be meaningful for memristor coupling, however, can you give some results of comparison of actual devices and your model, and possible references are as follows. Mater. Horiz., 2024, 11, 2886, 10.1039/d4mh00064a; Nano Lett. 2024, 24 (12), 3581-3589, 10.1021/acs.nanolett.3c04073; Nat Commun 2024, 15, 4318, 10.1038/s41467-024-48399-7.

(5)       The conclusion mentions the application of DMCAN maps in various fields, so its application should be further demonstrated in the introduction.

(6)       Language and grammar. This paper must be edited.

Author Response

Thank you very much for your kindly help. Please check the attachment

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

I read with high interest the paper entitled "Complex Dynamical Behavior of Locally Active Discrete Memristor Coupled Neural Networks with Synaptic Crosstalk: Attractor Coexistence and Reentrant Feigenbaum Trees"

This paper is well organized and should be published after minor revisions.

1) Acronyms must be introduced before being used. What is DMCAN?

2) Figure captions (all figures) should be more informative. It should be possible to understand the images directly by observing them without reading the text. In particular, for figure 3 it is not possible to understand anything from the caption.

3) Line 147: “The parameters k1=0.65, k2=0.11, a=1, b=0.8, c=0.05 and the initial values x0=0.2, 147 y0=-0.12 are set.” —> How were they chosen? Please explain these passages.

4) Line 171: The showed parameters have been set how?

Author Response

Thank you very much for your kindly help. Please check the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

accept