artificial neural network

artificial intelligence (ANN, commonly just "neural network"

or "neural net") A network of many very simple processors

("units" or "neurons"), each possibly having a (small amount

of) local memory. The units are connected by unidirectional

communication channels ("connections"), which carry numeric

(as opposed to symbolic) data. The units operate only on

their local data and on the inputs they receive via the

connections.

A neural network is a processing device, either an

algorithm, or actual hardware, whose design was inspired by

the design and functioning of animal brains and components

thereof.

Most neural networks have some sort of "training" rule whereby

the weights of connections are adjusted on the basis of

presented patterns. In other words, neural networks "learn"

from examples, just like children learn to recognise dogs from

examples of dogs, and exhibit some structural capability for

generalisation.

Neurons are often elementary non-linear signal processors (in

the limit they are simple threshold discriminators). Another

feature of NNs which distinguishes them from other computing

devices is a high degree of interconnection which allows a

high degree of parallelism. Further, there is no idle memory

containing data and programs, but rather each neuron is

pre-programmed and continuously active.

The term "neural net" should logically, but in common usage

never does, also include biological neural networks, whose

elementary structures are far more complicated than the

mathematical models used for ANNs.

See Aspirin, Hopfield network, McCulloch-Pitts neuron.

Usenet newsgroup: news:comp.ai.neural-nets.

(1997-10-13)