Neuronless Knowledge Processing in Forests

Round 1

Reviewer 1 Report

The authors have stated clearly the objective of this article. I think this article has the potential of being accepted.

Author Response

The reviewer's comments to the previous version of the paper were addressed. No further comments were made.

Reviewer 2 Report

I enjoyed you paper. It seems very interesting. Please look at my comments:

 

1) Please elaborate more at the intro why and how there can be a connection between human neurons and forest structure. Most importantly what is the specific importance of it.

 

2) How can spatial analysis tool produce the results for the forests? Please provide more info for the fitting or any losses. 

 

3) Conclusion needs improvement to incorporate more the connection between neurons and how the proposed tool resembles a similarity. 

Author Response

1) Please elaborate more at the intro why and how there can be a connection between human neurons and forest structure. Most importantly what is the specific importance of it.

The following was added to the Introduction Section lines 85-93:

The similarity between human neurons transmitting knowledge and forest trees transmitting knowledge can be seen from the network structure and the network behavior. The ability to process the knowledge regarding the available resources while optimizing their use appears to be similar. Furthermore, each processing unit, neuron or tree, seems to prioritize the resource needs of the network rather than its own needs.

The importance of the results is in the ability to analyze neuron-like behavior in an environment which processes knowledge much slower than human neurons. The forest environment, representing nature-inspired knowledge processing, has similar characteristics to brain neuron processing but is easier to manipulate and control.

2) How can spatial analysis tool produce the results for the forests? Please provide more info for the fitting or any losses. 

The following was added to the Method Section lines 114-117:

Kriging provides information on the spatial autocorrelation of the dataset, the forest canopy coverage. We use spatial resolution of a 30m² cell covered by tree canopy. Fitting and loss of tree canopy coverage values were produced using a Random Forests regression.

3) Conclusion needs improvement to incorporate more the connection between neurons and how the proposed tool resembles a similarity. 

An new Conclusion Section was created. The following was added to the Conclusion lines 282-288:

The communication between neurons in the brain is similar to the communication of knowledge in forests when trying to optimize a decision. In our study, the results show the optimization on maximizing the canopy over the entire forest as a process of sharing knowledge between all trees. Each interference in one part of the network is transmitted, and the entire network attempts to overcome the loss and reoptimize the use of resources, in this case sunlight. Similarly, neurons in the brain can be viewed as trying to optimize the use of available resources.

Round 2

Reviewer 2 Report

Thanks for addressing my comments. Paper is ready to be published. 

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

1. I suggest the authors present more clearly the objective of this manuscript. The authors seemed to propose an innovative method of developing an artificial intelligence model. If so, I suggest the authors add more content to the Discussion section to demonstrate how the results of this manuscript are used to develop an artificial intelligence model. If not, the authors may define more clearly the objective of this paper. Readers of the succeeding manuscript may misunderstand the objective of it.
2. Please check the formats of references. The format of reference [5] is different from the journals' references. The journal's name is abbreviated. The same problem is found in reference [18].
3. Please specify the units of all data in Table 1.

Reviewer 2 Report

n/a

I have serious issues with lines 168-171. Why on earth did they need to perform "severe crush spinal cord" injuries on mice to analyze a forest? The title of the paper was interesting to me but surely there are past studies that could have been used to justify the results of the forest findings.