The expression 'Neural Networks' refers traditionally to a class of
mathematical algorithms that obtain their proper performance while they
'learn' from examples or from experience. As a consequence, they are
suitable for performing straightforward and relatively simple tasks like
classification, pattern recognition and prediction, as well as more
sophisticated tasks like the processing of temporal sequences and the
context dependent processing of complex problems. Also, a wide variety
of control tasks can be executed by them, and the suggestion is
relatively obvious that neural networks perform adequately in such cases
because they are thought to mimic the biological nervous system which is
also devoted to such tasks. As we shall see, this suggestion is false
but does not do any harm as long as it is only the final performance of
the algorithm which counts. Neural networks are also used in the
modelling of the functioning of (sub- systems in) the biological nervous
system. It will be clear that in such cases it is certainly not
irrelevant how similar their algorithm is to what is precisely going on
in the nervous system. Standard artificial neural networks are
constructed from 'units' (roughly similar to neurons) that transmit
their 'activity' (similar to membrane potentials or to mean firing
rates) to other units via 'weight factors' (similar to synaptic coupling
efficacies).
