Derivation of a novel efficient supervised learning algorithm from cortical-subcortical loops

Although brain circuits presumably carry out powerful 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 a subset of functions of cortical-subcortical 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 non-standard 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.