Natural computation with connectionist systems

This paper presents the evolution of connectionist systems that leads into the so called Networks of Evolutionary Processors (NEPs) and it also shows a general approach to add a learning stage in NEPs. These networks have been prooft to be universal models that solve NP-problems in linear time. Most usual disadvantage is that a given NEP only can solve a given problem. NEPs with learning stages can be consired as a more general model to solve several problems, and they are a superclass of NEPs. Some theorems are shown in order to state the computational power of NEPs. First of all, Artificial Neural Networks are revisited (including multilayer perceptrons, Jordan-Elman networks and Time lagged networks), then Transition P Systems and NEPs are shown. Finally, a model of learing in NEPs with filtered connections is proposed.