## 1 Introduction  
Neurons are viewed as the basic cells that process and transmit information. Trees and neurons share similar structure and neurotransmitter-like substances [1; 2]. No evidence for structures such as neurons, synapses, or a brain has been found inside plants [3]. Signal processing by trees has been known since Salix stickensis trees were found to be sensitive to and respond to signals generated by trees attacked by tent caterpillars and webworms [4]. Interplant communication can occur from leaves of one species of plant to leaves of another species to activate the expression of defensive genes [5]. N-(17-hydroxylinenol)-L-glutamine was found to be a key compound in communication between leaves of corn seedlings eaten by beet armyworms and in calls for help to the enemy of their enemy, parasitic wasps [6]. Furthermore, species in ecological communities show the ability to build complex webs of communication based on plant fungus [7; 8]. Communication of plants through airborne signals has been analyzed [9; 10]. In addition, plants have memory defined by reversible changes in behavior (plasticity) that has been altered by experience [11]. Previous work has shown changes in neural substrates of memory [12] based on predictions of rewarding events using adaptive optimization control [13]. However, there is a lack of knowledge about the mechanisms that lead to plant communication that are beneficial for both plant emitter and receiver [10]. A roadmap towards a functional paradigm for learning and memory in plants has been proposed [14]. From the seven requirements proposed to substantiate plant learning and memory, one aspect included information processing which was in initial stages and needed additional clarification. Another requirement aspect proposed was independent internal representation, which is currently theoretical and needs further investigation. Here, instead of viewing each plant as an independent mechanism, we view the forest as a system that is processing information and has representation of the knowledge being processed. We show how viewing a forest as a network rather than as separate tree entities can help explain the benefit of communication as resource optimization. It is suggested that each tree's goal of survival is directed by the forest trying to optimize its resources, which in our case is access to sunlight. Comparison of a forest network to a neural network shows interesting similarities in both the physical structure and some of the functional properties of both networks. We define Knowledge Processing as the analysis of information intended to try to optimize the use of available resources. Animals are viewed as having the ability to process knowledge at different levels in order to use their available resources. To bypass the philosophical argument of whether animals or trees \"think\", we analyze the use of resources needed to survive. In the case of animals, neuron brain activity allows the processing of the knowledge of available resources. Since trees lack neurons but can build a communication network [15], their use of the resources required for survival, including light, nutrients, and water, can be analyzed. We chose to analyze the communication between trees in a forest that tries to optimize the use of light. The analysis is inspired by epilepsy resection, the action performed on neurons when they exhibit high neuron activity. In epilepsy resection, a part of the brain, a set of neurons, is removed to allow the neurons residing in the surrounding area to become less active and resume normal neuron activity [16]. Here we try to imitate the idea behind this procedure and look at forests where a section of trees has been removed. The idea is that resection causes nearby neurons (trees) to become less active, or rather, less efficient at processing knowledge. This means \"resume normal activity\" in hyper-acting neurons, but \"falls down to abnormal activity\" in trees.