Abstract
The agenda of fuzzy neurocomputing focuses on the development of artifacts that are both adaptable ( so any learning pursuits could be carried out in an efficient manner) and interpretable ( so that the results are easily understood by the user or designer). The logic is the language of interpretable constructs. Neural architectures offer a flexible and convenient setting for learning. The study conveys a message that a suitable combination of logic incorporated into the structure of a specialized neuron leads to interpretable and elastic processing units one can refer to as fuzzy neurons. We investigate the main categories of such neurons and elaborate on the ensuing topologies of the networks emphasizing a remarkably rich landscape of logic architectures associated with the use of the logic neurons.