Artificial neural networks

Computational models, which emulate biological neural networks. Artificial neural networks contain components and functions analogous to neurons, for example the processing element (nucleus), network node (soma), inputs (dendrites), output (axon) and signal weight (synapse), though without all of the layers of complexity of biology. Artificial neural networks are associative memory systems using inductive reasoning, self-organization and parallel processing similar to the human brain. They are driven by data, and function by scanning many case studies for common patterns. They can function despite the presence of ambiguity by using induction, associative memory, or fuzzy logic. An artificial neural network (ANN), also called a simulated neural network (SNN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network. In more practical terms neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.