Our full account of an application of colimits and limits to improving upon a standard neural architecture is soon to appear in the journal Neurocomputing. In case this interests you, a preprint is obtainable from my website, http://www.ece.unm.edu/~mjhealy , or else contact me for a copy. The blurb: Applying Category Theory to Improve the Performance of a Neural Architecture Michael J. Healy, Richard D. Olinger, Robert J. Young, Shawn E. Taylor, Thomas P. Caudell, and Kurt W. Larson Abstract: A recently-developed mathematical semantic theory explains the relationship between knowledge and its representation in connectionist systems. The semantic theory is based upon category theory, the mathematical theory of structure. A product of its explanatory capability is a set of principles to guide the design of future neural architectures and enhancements to existing designs. We claim that this mathematical semantic approach to network design is an effective basis for advancing the state of the art. We offer two experiments to support this claim. One of these involves multispectral imaging using data from a satellite camera. [For admin and other information see: http://www.mta.ca/~cat-dist/ ]