I hope people on the categories list find this interesting: At the International Joint Conference on Neural Networks (IJCNN05) in Montreal last week, I presented our joint work with Sandia National Laboratories in research on category theory applied to neural networks. We demonstrated improved performance with a modification to a standard artificial neural architecture in generating a multispectral image from satellite data. To create the modified architecture, we added a neural representation of limit cones to the standard architecture and used these to exert fine control over the network operation. An information-theoretic measure we used, to compare the image we generated with the category-theoretic modification to the image generated by the unmodified standard architecture, increased by a factor of two with the category-theoretic modification. An ``eyeball comparison'' of images also shows a clear improvement. We believe this is the first application of category theory directly in an engineering application (while at Boeing, another colleague and I had demonstrated its application to the synthesis of engineering software). The accompanying paper is in the Proceedings of IJCNN05. It doesn't have the information-theoretic result (obtained after the paper was submitted); I put that in the presentation. Also, we only show a simple modification just to illustrate the use of limits (the limits here are just products); the actual architecture is just a bit more complex and includes coproducts. A paper we will be submitting to some journal will have the missing material. Of course, if you want more information I will be glad to hear from you. Mike Healy mjhealy@ece.unm.edu