The major goal of this research is to develop general nonparametric methods for the estimation of entropy and mutual information, giving a unifying point of view for their use in signal processing and neural computation. In many real world problems, the information is carried solely by data samples without any other a priori knowledge. The central issue of "learning from examples" is to estimate energy, entropy or mutual information of a variable only from its samples and adapt the system parameters by optimizing a criterion based on the estimation.