Neural Networks are of two kinds, Artificial and Biological Neural Networks. A large amount of work has gone into the task of developing artificial units which function like neurons and hence do away with their biological counterparts and the term Neural Networks is now used predominantly to describe Artificial Neural Networks. The units operate only on their local data and on the inputs they receive via the connections. Although still a budding science Neural Networks already find applications in a variety of fields. Computer professionals use it to find the properties of non-symbolic information processing and learning systems in general.
Engineers use it for signal processing and automatic control.
Neuro-physiologists use it to describe and explore medium level brain controls. Biologists use it for interpreting nucleotide sequences. Although theoretically, NNs can do anything that a normal digital computer can do, but practically, they are useful only for tasks like classification and function approximation which have a lot of training data but to which certain hard and fast rules cannot be applied. Unlike the brain, NN cannot be used to create data and information, which do not already exist in the training data. Thus, they cannot be applied to tasks involving the manipulation of symbols and memory.