Neural network, also known as a parallel dispersed processing network, is a computing paradigm that is freely modeled after cortical structures of the brain. It consists of consistent processing elements called nodes or neurons that work together to create an output function. The output of a neural network relies on the collaboration of the individual neurons within the network to operate. Processing of information by neural networks is typically done in parallel rather than in series (or sequentially) as in earlier binary computers or Von Neumann machines. Since it relies on its member neurons together to perform its function, a unique property of a neural network is that it can still achieve its overall function even if some of the neurons are not functioning. In other words it is robust to tolerate error or failure. (see fault tolerant) Additionally, neural networks are more readily adaptable to fuzzy logic computing tasks than are Von Neumann machines.