  Although brain circuits presumably carry out useful perceptual algorithms,
few instances of derived biological methods have been found to compete
favorably against algorithms that have been engineered for specific
applications. We forward a novel analysis of function of cortico-striatal
loops, which constitute more than 80% of the human brain, thus likely
underlying a broad range of cognitive functions. We describe a family of
operations performed by the derived method, including a nonstandard method for
supervised classification, which may underlie some forms of
cortically-dependent associative learning. The novel supervised classifier is
compared against widely-used algorithms for classification, including support
vector machines (SVM) and k-nearest neighbor methods, achieving corresponding
classification rates --- at a fraction of the time and space costs. This
represents an instance of a biologically-derived algorithm comparing favorably
against widely used machine learning methods on well-studied tasks.
