This paper introduces a general class of neuron models, accepting heterogeneous inputs in the form of mixtures of continuous (crisp or fuzzy) numbers, linguistic information, and discrete (either ordinal or nominal) quantities, with provision also for missing information. Their internal stimulation is based on an explicit similarity relation between the input and weight tuples (which are also heterogeneous). The framework is comprehensive and several models can be derived as instances --in particular, two of the commonly used models are shown to compute a specific similarity function provided all inputs are real-valued and complete. An example family of models defined by composition of a Gower-based similarity with a sigmoid function is shown to lead to network designs (Heterogeneous Neural Networks) capable of learning from non-trivial data sets with a remarkable effectiveness, comparable to that of classical models.Peer ReviewedPostprint (author's final draft
