Self-organizing computational models
with specific intracortical connections can
explain many functional features of visual
cortex, such as topographic orientation and
ocular dominance maps. ... This article
introduces two techniques that make large simulations
First, we show how parameter
scaling equations can be derived for
laterally connected self-organizing models.
These equations result in quantitatively equivalent
maps over a wide range of simulation
sizes, making it possible to debug small simulations
and then scale them up only when
Second, we use parameter
scaling to implement a new growing map
method called GLISSOM, which dramatically
reduces the memory and computational
requirements of large self-organizing networks.
See paper for more and details.
Bednar JA, Kelkar A, Miikkulainen R (2004) Scaling self-organizing maps to model large cortical networks. Neuroinformatics 2:275-302 [PubMed]