LISSOM is a biologically more realistic implementation of the SOM idea, where the weight change neighborhood is determined through competition and collaboration mediated by lateral connections (instead of a global supervisor), and weights are changed based on Hebbian learning and renormalization (instead of Euclidean distance). LISSOM was developed as a first step towards modeling biological maps (see the visual cortex page), but it also has useful properties in its own right. It is capable of self-organization roughly similar to the SOM model, but because the lateral connections decorrelate the activation patterns on the map, they form a better internal representation for visual patterns such as those in handwritten digit recognition.