Bednar, James A.; Kelkar, Amol; Miikkulainen, Risto Modeling large cortical networks with growing self-organizing maps. (English) Zbl 1007.68806 Neurocomputing 44-46, 315-321 (2002). Summary: Self-organizing computational models with specific intracortical connections can explain many features of visual cortex. However, due to their computation and memory requirements, it is difficult to use such detailed models to study large-scale object segmentation and recognition. This paper describes GLISSOM, a method for scaling a small RF-LISSOM model network into a larger one during self-organization, dramatically reducing time and memory needs while obtaining equivalent results. With GLISSOM it should be possible to simulate all of human V1 at the single-column level using existing supercomputers. The scaling equations GLISSOM uses also allow comparison of biological maps and parameters between individuals and species with different brain region sizes. Cited in 1 Document MSC: 68U99 Computing methodologies and applications 68T05 Learning and adaptive systems in artificial intelligence 92C20 Neural biology Keywords:Self-organization; Cortical modeling; Vision; Orientation maps; Growing networks Software:GLISSOM PDFBibTeX XMLCite \textit{J. A. Bednar} et al., Neurocomputing 44--46, 315--321 (2002; Zbl 1007.68806) Full Text: DOI