Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)

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 practical. 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 needed. ... 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.

Model Type: Connectionist Network

Model Concept(s): Activity Patterns; Temporal Pattern Generation; Spatio-temporal Activity Patterns; Unsupervised Learning; Olfaction

Simulation Environment: Topographica (web link to model)

Implementer(s): Bednar, James [jbednar at]


Bednar JA, Kelkar A, Miikkulainen R. (2004). Scaling self-organizing maps to model large cortical networks. Neuroinformatics. 2 [PubMed]

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