Concise connections, delicate decision-making and particular paths combine to create neural networks. The concept of neural networks originally received its name due to the functions of the human brain. The human brain consists of billions of neurons that create connections to each other based on previous experiences for overall better awareness and functionality of that brain. For example, the act of catching a ball requires the activation of the correct path of neurons in the brain that activate based on all previous experiences of reacting to a thrown object. Because each of the billion neurons can unite to form a different path, the combinations quickly become very large and complex.
Transitioning the idea to computing, tree data structures are often used to simulate the same processes that occur in the human brain. Inputs often start at one node of the system and quickly traverse across other nodes of data to create meaningful, memorized connections by applying weights correlating to the paths used. These weights are considered in all following traversal calculations, which can be utilized as a very powerful tool in computing.
Transitioning the idea to computing, tree data structures are often used to simulate the same processes that occur in the human brain. Inputs often start at one node of the system and quickly traverse across other nodes of data to create meaningful, memorized connections by applying weights correlating to the paths used. These weights are considered in all following traversal calculations, which can be utilized as a very powerful tool in computing.