Sources of asymmetric randomness

In view of discussing the genuine roots of the connectionist paradigm we toss in this paper the non symmetry features of the involved random phenomena. Reading these features in terms of intentionality with which we drive a learning process far from a simple random walk, we focus on elementary processes where trajectories cannot be decomposed as the sum of a deterministic recursive function plus a symmetric noise. Rather we look at nonlinear compositions of the above ingredients, as a source of genuine non symmetric atomic random actions, like those at the basis of a training process. To this aim we introduce an extended Pareto distribution law with which we analyze some intentional trajectories. With this model we issue some preliminary considerations on elapsed times of training sessions of some families of neural networks.