Substance Use & Misuse, 33(2), 495–501, 1998 495 Copyright © Semeion 1998 Self-Recurrent Neural Network Massimo Buscema, Dr. Semeion Research Center of Sciences of Communication, Viale di Val Fiorita 88, Rome, Italy Feed Forward Artificial Neural Networks of the Back Propagation family have both a weakness and a strength in their makeup: their layer of hidden units encodes input vectors in a manner that is inclined to be distributed. This type of encoding is a strong point of these ANNs since it is a very efficient encoding system from a computational viewpoint. Even from a neuro-biological viewpoint, the memorisation system is plausible. But precisely because of its power, this type of input vector codification is practically uncontrollable. There are many ways through which hidden units encode input vectors. Which of them is the most efficient on the basis of the relationships that each input variable has with every other such variable? The ideal answer to this question consists in allowing the hidden layer to also encode its own codification of the input vector: (1) y fx  () generic ANN function with input x and output y vectors (2) y fgx  ( ( )) g(x ) is the output vector of the ANN’s hidden layer (3)   x x gx () x is the ANN’s input vector and x  is the new input vector (extended input) (4) y fgx gx   (( ( ))) Final Recurrent Equation Equation (4) makes provision for the ANN’s layer of hidden units to