The goal of the neural network is to solve problems in the same way that the human brain would, although several neural networks are more abstract. Modern neural network projects typically work with a few thousand to a few million neural units and millions of connections, which is still several orders of magnitude less complex than the human brain and closer to the computing power of a worm.
New brain research often stimulates new patterns in neural networks. One new approach is using connections which span much further and link processing layers rather than always being localized to adjacent neurons. Other research being explored with the different types of signal over time that axons propagate, such as Deep Learning, interpolates greater complexity than a set of boolean variables being simply on or off.
Neural networks are based on real numbers, with the value of the core and of the axon typically being a representation between 0.0 and 1.
An interesting facet of these systems is that they are unpredictable in their success with self learning. After training, some become great problem solvers and others don't perform as well. In order to train them, several thousand cycles of interaction typically occur.
Like other machine learning methods – systems that learn from data – neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are hard to solve using ordinary rule-based programming.