THE COMPLEXITY OF LANGUAGE RECOGNITION BY NEURAL NETWORKS

Neural networks are frequently used as adaptive classifiers. This rese arch represents an attempt to measure the ''neural complexity'' of any regular set of binary strings, that is, to quantify the size of a rec urrent continuous-valued neural network that is needed for correctly c lassifying the given regular set. Our estimate provides a predictor th at is superior to the size of the minimal automaton that was used as a n upper bound so far. Moreover, it is easily computable, using techniq ues from the theory of rational power series in non-commuting variable s.