A cortically inspired learning model

Abstract

We describe a biologically plausible learning model inspired by the structural and functional properties of the cortical columns present in the mammalian neocortex. The strength and robustness of our model is ascribed to its biologically plausible, uniformly structured, and hierarchically distributed processing units with their localized learning rules. By modeling cortical columns rather than individual neurons as our fundamental processing units, we get hierarchical learning networks that are computationally less demanding and better suited for studying higher cortical properties like independent feature detection, plasticity, etc. Another interesting attribute of our model is the use of feedback processing paths to generate invariant representation to robustly recognize variations of the same patterns and to determine the set of features sufficient for recognizing different patterns in the input dataset. We train and test our hierarchical networks using synthetic digit images as well as a subset of handwritten digit images obtained from the MNIST database. Our results show that our cortical networks use unsupervised feedforward processing as well as supervised feedback processing to robustly recognize handwritten digits. © 2012 Springer-Verlag GmbH Berlin Heidelberg.