The inspiration for neural networks came from examination of central nervous systems. In an artificial neural
network, simple artificial nodes, called "neurons", "neurodes", "processing elements" or "units", are connected
together to form a network which mimics a biological neural network.
Generally, it involves a network of simple processing elements exhibiting complex global behavior determined by
the connections between the processing elements and element parameters. Artificial neural networks are used
with algorithms designed to alter the strength of the connections in the network to produce a desired signal flow.
Neural networks are also similar to biological neural networks in performing functions collectively and in parallel
by the units, rather than there being a clear delineation of subtasks to which various units are assigned
In modern software implementations of artificial neural networks, the approach inspired by biology has been
largely abandoned for a more practical approach based on statistics and signal processing. In some of these
systems, neural networks or parts of neural networks (like artificial neurons) form components in larger systems
that combine both adaptive and non-adaptive elements. While the more general approach of such adaptive
systems is more suitable for real-world problem solving, it has far less to do with the traditional artificial
intelligence connectionist models. What they do have in common, however, is the principle of non-linear,
distributed, parallel and local processing and adaptation.
Neural networks offer a number of advantages, including requiring less formal statistical training, ability to
implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect
all possible interactions between predictor variables, and the availability of multiple training algorithms.These
advantages has enabled the application of ANN models to a huge number of fields.Some applications
include system identification and control (vehicle control, process control, natural resources management),
quantum chemistry, game-playing and decision making (backgammon, chess, poker), pattern recognition (radar
systems, face identification, object recognition and more), sequence recognition (gesture, speech, handwritten
text recognition), medical diagnosis, financial applications (automated trading systems), data mining,
visualization and e-mail spam filtering.
I don't know about you but Artificial Neural Networks sounds like an exciting field to explore.
source:Wikipedia, J Clin Epidemiol.
