Design of a New Neuro-Generator with a Neuronal Module to Produce Pseudorandom and Perfectly Pseudorandom Sequences

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper designed a neuro-generator based on the PRNG technology. The results have been verified through FPGA experiments. There is no doubt that this is a very meaningful work, but there are some issues to consider in the revised version.

 

1. The quality of the pictures in the article is not high, it is suggested that the author modify. For example, Fig.1,4-8, and 15.

2. What is the purpose of Table 2? In my opinion, these are the basics.

3. Is this random number generator generating chaos? If yes. It is suggested to use bifurcation diagrams, Lyapunov exponents, and phase plots to further analyze the chaotic characteristics of the proposed model.

4. It is recommended that the authors provide a more comprehensive introduction of the latest research works in PRNG-based models, such as A 4D Trigonometric-Based Memristor Hyperchaotic Map to Ultra-Fast PRNG; A triple-memristor Hopfield neural network with space multi-structure attractors; Designing a novel image encryption algorithm based on a 2D-SCLC hyperchaotic map; A Universal Variable Extension Method for Designing Multiscroll/Wing Chaotic Systems; and so on.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The article proposes an interesting approach to random number generation. Although the idea and the concept are indeed fascinating, I have some concerns that need to be addressed to understand the solidity of the work. In details:

1) In the abstract, and later on in the paper, authors mention that final applications of the circuits may be "biological Systems" but this is not supported at all in the text by evidence.

2) although the proposed approach for random number generation is surely unconventional, the authors need to better clarify the novelty and improvement against state of the art. What are the advantages concerning the well-established state-of-the-art solutions (e.g. 10.3390/app11083330)

3) The reason for the generation of the clock with the Neuronal module is still not completely clear to me, please clearly indicate it within the text.

4) The simulations snapshot reported in Figure 10 are completely meaningless, please substitute them with a description of the simulation carried out and the achieved results.

5) Why did you choose the specific FPGA board? what is the power consumption of the circuit? and the achieved throughput of random numbers? please compare also these results against state-of-the-art solutions. You can refer also to 10.3390/electronics12030723 for details on the metrics to be used in the comparison.

 

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors addressed only a few of my concerns:

1) It is not clear any link at all with the biological system: I suggest either explaining better why the proposed circuit can be used in such an application or removing the reference. 

2) ok

3) No clarification has been provided

4) Figures 9 and 10 are still not acceptable: many of the reported signals are not mentioned in the description and the image is very blurry. I suggest considering removing them and just leaving textual explanation or maybe drawing a schematization of the simulation carried out

5) the indicated reason appears a bit weak. 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 3

Reviewer 2 Report

Comments and Suggestions for Authors

Thanks for your reply, my previous comments have been addressed