Topographic Class Grouping with applications to 3D object recognition

The cerebral cortex uses a large number of top-down connections, but the roles of the top-down connections remain unclear. Through end-to-end (sensor-to-motor) multilayered networks that use three types of connections (bottom-up, lateral, and top-down), the new topographic class grouping (TCG) mechanism shown in this paper explains how the top-down connections influence (1) the type of feature detectors (neurons) developed and (2) their placement in the neuronal plane. The top-down connections boost the variations in the neuronal between class directions during the training phase. The first outcome of this top-down boosted input space is the facilitation of the emergence of feature detectors that are purer, measured statistically by the average entropy of the neuronspsila development. The relatively purer neurons are more ldquoabstract,rdquo i.e., characterizing class-specific (or motor-specific) input information, resulting in better classification rates. The second outcome of this top-down boosted input space is the increase of the distance between input samples that belong to different classes, resulting in a farther separation of neurons according to their class. Therefore, neurons that respond to the same class become relatively nearer. This results in TCG, measured statistically by a smaller within-class scatter of responses when the neuronal plane has a fixed size. Although these mechanisms are potentially applicable to any pattern recognition applications, we report quantitative effects of these mechanisms for 3D object recognition of center-normalized, background-controlled objects. TCG has enabled a significant reduction of the recognition errors.