  In this paper we present the Widrow-Hoff rule and its applications to
language data. After contextualizing the rule historically and placing it in
the chain of neurally inspired artificial learning models, we explain its
rationale and implementational considerations. Using a number of case studies
we illustrate how the Widrow-Hoff rule offers unexpected opportunities for the
computational simulation of a range of language phenomena that make it possible
to approach old problems from a novel perspective.
