Simple neural netork simulations like those presented in this project attempt to model some facet of intelligence and learning. Scientists studying artificial neural networks seek to find properties of the systems they create that are emergent . That is, properties of the system that evolve into something that seems to be greater than the sum of the interacions of its parts. In truth, there is nothing magical, or even emergent about the properties of neural networks. It is merely a problem associated with the rigid contemporary notion of computational logic.
Neural networks use a fuzzy logic that is sometimes more statistical or probablistic than concrete.
Modern computers by necessity do not use such inaccurate and error-prone principles. But it is this logical fuzziness that makes the properties of neural networks seem emergent. One of the most confusing properties of neural network knowledge is its encoding within the network. Knowledge is encoded in the relationships between, not within, the neurons themselves. When a neural network is trained, its trainer seeks to find the best possible way each neuron should interact with every other, in order for the desired knowledge to be manifested in the network.