We address the problem of measuring the degree of hemispheric organization and asymmetry of organization in a computational model of a bihemispheric cerebral cortex. A theoretical framework for such measures is developed and used to produce algorithms for measuring the degree of organization, symmetry, and lateralization in topographic map formation.

The performance of the resulting measures is tested for several topographic maps obtained by self--organization of an initially random network, and the results are compared with subjective assessments made by humans. It is found that the closest agreement with the human assessments is obtained by using organization measures based on sigmoid--type error averaging. Measures are developed which correct for large constant displacements as well as curving of the hemispheric topographic maps. (Also cross-referenced as UMIACS-TR-96-51)