Scientific Computing: Artificial Neural Network

Artificial Neural Networks are computational models that are capable of machine learning and pattern recognition.  They were inspired by the animal central nervous system. Today, they are used in many fields and have been well-established in any discipline, especially medicine.  They can extract information from raw data and then build computer models for medical decision-making.
Now you know what they are capable of doing but what exactly are they? They  are basically systems of connected neurons that compute values from inputs by pushing information through the network.  According to the Wiley Online Library, these models contain computing nodes that operate as nonlinear summing devices.  The nodes are interconnected by weighted connection lines, and the weights are adjusted when data are presented to the network.  If successfully trained, the artificial neural networks can perform tasks such as classifying an object, predicting an outcome value, recognizing a pattern, and completing a known pattern.  

Since applications of artificial neural networks serve such broad spectrum of fields, they help validate research in those areas.  For example, medical decision support in medical applications.  These networks must be understood thoroughly before being used. Check out this list in which neural networks have been successfully applied.