||Since their inception in the late 80's by Bart Kosko, Fuzzy Cognitive Maps (FCMs) have been widely accepted by the scientific community. However, the most interesting pieces of research reported in the literature are rather applicative with a few papers devoted to elaborating the FCM foundations. Recently, researchers from Hasselt University proposed a new approach named Short-term Cognitive Networks (STCNs) as an alternative to classic FCMs, which allows performing simulations on the basis of previously defined expert knowledge, where weights may have a causal meaning or not. The accuracy and transparency of this model encouraged us to investigate a new approach that allows STCN-based model to handle symbolic situations since real-world problems are often described with imprecise information that is difficult to evaluate objectively. The proposal focuses on four main challenges: 1) how to handle symbolic information attached to the concepts' activation values and the relations between them, 2) how to unify the information when it comes from different experts, 3) how to estimate the weight set from data when experts are not available and 4) how to characterize the inference process for a problem instance. Towards the end, we can obtain a symbolic neural system that is more transparent to human experts, which can be used in a wide variety of application problems.