Complexity is a double-edged sword for learning algorithms when the number of available samples for training in relation to the dimension of the feature space is small. This is because simple models do not sufficiently capture the nuances of the data set, while complex models overfit. While remedies such as regularization and dimensionality reduction exist, they themselves can suffer from overfitting or introduce bias. To address the issue of overfitting, the incorporation of prior structural knowledge is generally of paramount importance. In this work, we propose a BRAIN strategy for learning, which enhances the performance of traditional algorithms, such as logistic regression and SVM learners, by incorporating a graphical layer that tracks and learns in real-time the underlying correlation structure among feature subspaces. In this way, the algorithm is able to identify salient subspaces and their correlations, while simultaneously dampening the effect of irrelevant features. This effect is particularly useful for high-dimensional feature spaces.