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
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
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: comp.ai.neural-nets.
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