An artificial neural network (ANN), often just called a "neural
network" (NN), is a mathematical model or computational
model based on biological
neural networks. It consists of an interconnected group of
artificial neurons
and processes information using 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 during the
learning phase.
(The term "neural network" can also mean biological-type systems.)
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. There is no precise agreed definition
among researchers as to what a neural network is, but most would agree that it
involves a network of simple processing elements (neurons), which can exhibit
complex global behavior, determined by the connections between the processing
elements and element parameters. The original inspiration for the technique was
from examination of the central nervous system and the neurons (and their axons,
dendrites and synapses) which constitute one of its most significant
information processing elements (see Neuroscience). In a neural network model,
simple nodes (called variously "neurons", "neurodes",
"PEs" ("processing elements") or "units") are
connected together to form a network of nodes — hence the term "neural
network." While a neural network does not have to be adaptive per sé, its
practical use comes with algorithms designed to alter the strength (weights) of
the connections in the network to produce a desired signal flow.
These networks are also similar to the biological neural networks in the
sense that functions are performed collectively and in parallel by the units,
rather than there being a clear delineation of subtasks to which various units
are assigned (see also connectionism). Currently, the term Artificial Neural
Network (ANN) tends to refer mostly to neural network models employed in statistics,
cognitive psychology and artificial
intelligence. Neural network models
designed with emulation of the central nervous system (CNS) in mind are a
subject of theoretical neuroscience (computational neuroscience).
In modern software implementations of artificial neural networks the
approach inspired by biology has more or less been abandoned for a more
practical approach based on statistics and signal processing. In some of these
systems neural networks or parts of neural networks (such as artificial neurons)
are used as 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,
however, have in common is the principle of non-linear, distributed, parallel
and local processing and adaptation.