Self-organization of orientation maps due to external stimuli in the primary visual area of the cerebral cortex is studied in a two-layered neural network which consists of formal neuron models with a sigmoidal output function. A cluster learning rule is proposed as an extended Hebbian learning rule
โฆ LIBER โฆ
A filter based neuron model for adaptive incremental learning of self-organizing maps
โ Scribed by Mauro Tucci; Marco Raugi
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 471 KB
- Volume
- 74
- Category
- Article
- ISSN
- 0925-2312
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In this paper, a neural network model, the hypercolumn model (HCM), which is applicable to general image recognition, is proposed. The HCM is a combination model of hierarchical self-organizing maps (HSOM) and neocognitron (NC); it resolves the disadvantages of both the HSOM and the NC, and inherits