  A novel approach for supervised classification is presented which sits at the
intersection of machine learning and dynamical systems theory. At variance with
other methodologies that employ ordinary differential equations for
classification purposes, the untrained model is a priori constructed to
accommodate for a set of pre-assigned stationary stable attractors. Classifying
amounts to steer the dynamics towards one of the planted attractors, depending
on the specificity of the processed item supplied as an input. Asymptotically
the system will hence converge on a specific point of the explored
multi-dimensional space, flagging the category of the object to be eventually
classified. Working in this context, the inherent ability to perform
classification, as acquired ex post by the trained model, is ultimately
reflected in the shaped basin of attractions associated to each of the target
stable attractors. The performance of the proposed method is here challenged
against simple toy models crafted for the purpose, as well as by resorting to
well established reference standards. Although this method does not reach the
performance of state-of-the-art deep learning algorithms, it illustrates that
continuous dynamical systems with closed analytical interaction terms can serve
as high-performance classifiers.
