In the classical neuron model, inputs are continuous real-valued quantities.
However, in many important domains from the real world, objects are
described by a mixture of continuous and discrete variables, usually
containing missing information and uncertainty. In this paper, a
general class of neuron models accepting
heterogeneous inputs in the form of mixtures of continuous (crisp
and/or fuzzy) and discrete
quantities admitting missing data is presented. From these, several
particular models can be derived as instances and different neural
architectures constructed with them. Such models deal in a natural way
with problems for which information is imprecise or even missing. Their
possibilities in
classification and diagnostic problems are here illustrated by experiments with
data from a real world domain in the field of environmental studies. These
experiments show that such neurons can both learn and classify complex data
very effectively in the presence of uncertain information.