Abstract
In this paper, we introduce a new category of logic neurons-unineurons that are based on the concept of uninorms. As uninorms form a certain generalization of the generic categories of fuzzy set operators such as t-norms and t-conorms, the proposed unineurons inherit their logic processing capabilities which make them flexible and logically appealing. We discuss several fundamental categories of uninorms (such as UNI_OR, UNI_AND, and alike). In particular, we focus on the interpretability of networks composed of unineurons leading to several categories of rules to be exploited in rule-based systems. The learning aspects of the unineurons are presented along with detailed optimization schemes. Experimental results tackle two categories of problems such as: (a) a logic approximation of fuzzy sets, and (b) a design of associations between information granules where the ensuing development schemes directly relate to the fundamentals of granular (fuzzy) modeling.