Neuronless Knowledge Processing in Forests
Name
applsci-10-02509-v3.pdf
Size
3.4 MB
Format
Adobe PDF
Checksum (MD5)
bf52e3735762f35ef8b59641f2469e32
Author(s)
Segev, Aviv
Curtis, Dorothy
Balili, Christine
Jung, Sukhwan
Date Issued
April 5, 2020
Journal
Applied Sciences
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Segev, Aviv, et al. “Neuronless Knowledge Processing in Forests.” Applied Sciences 10, 7 (April 2020): 2509.
Version
Final published version
Abstract
Neurons are viewed as the basic cells that process and transmit information. Trees and neurons share a similar structure and neurotransmitter-like substances. No evidence for structures such as neurons, synapses, or a brain has been found inside plants. Consequently, the ability of a network of trees to process information in a method similar to that of a neural network and to make decisions regarding the usage of resources is unperceived. We show that the network between trees is used for knowledge processing to implement decisions that prioritize the forest over a single tree regarding forest use and optimization of resources, similar to the processes of a biological neural network. We found that when there is resection of a network of trees in a forest, namely a trail, each network part will try optimizing its overall access to light resources, represented by canopy tree coverage, independently. This was analyzed in 323 forests in different locations across the US where forest resection is performed by trails. Our results demonstrate that neuron-like relations can occur in a forest knowledge processing system. We anticipate that other systems exist in nature where the basic knowledge processing for resource usage is performed by components other than neurons. Keywords: knowledge processing; trees; neuron activity; forest network
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/app10072509
