Scaling Self-Organizing Maps To Model Large Cortical Networks (2001)
Self-organizing computational models with specific intracortical connections can explain many functional features of visual cortex, such as topographic orientation and ocular dominance maps. However, due to their computational requirements, it is difficult to use such detailed models to study large-scale phenomena like object segmentation and binding, object recognition, tilt illusions, optic flow, and fovea--periphery interaction. This paper introduces two techniques that make large simulations practical. First, a set of general linear scaling equations for the RF-LISSOM self-organizing model is derived and shown to result in quantitatively equivalent maps over a wide range of simulation sizes. Second, the equations are combined into a new growing map method called GLISSOM, which dramatically reduces the memory and computational requirements of large self-organizing networks. With GLISSOM it should be possible to simulate all of human V1 at the single-column level using existing supercomputers, making detailed computational study of large-scale phenomena possible.
Neuroinformatics:275-302, 2001.

James A. Bednar Postdoctoral Alumni jbednar [at] inf ed ac uk
Amol Kelkar Masters Alumni
Risto Miikkulainen Faculty risto [at] cs utexas edu

The LISSOM package contains the C++, Python, and Scheme source code and examples for training and testing firing-rate...