Neural Networks
Neural maps and topographic vector quantization
Neural Networks
Self-Organizing Maps
Self-organizing maps with recursive neighborhood adaptation
Neural Networks - New developments in self-organizing maps
Principles and networks for self-organization in space-time
Neural Networks - New developments in self-organizing maps
Winner-Relaxing Self-Organizing Maps
Neural Computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Self-organizing neural projections
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
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Self-organizing map (SOM) has been studied as a model of map formation in the brain cortex. Neurons in the cortex present a refractory period in which they are not able to be activated, restriction that should be included in the SOM if a better description is to be achieved. Altough several works have been presented in order to include this biological restriction to the SOM, they do not reflect biological plausibility. Here, we present a modification in the SOM that allows neurons to enter a refractory period (SOM-RP) if they are the best matching unit (BMU) or if they belong to its neighborhood. This refractory period is the same for all affected neurons, which contrasts with previous models. By including this biological restriction, SOM dynamics resembles in more detail behavior shown by the cortex, such as non-radial activity patterns and long distance influence, besides the refractory period. As a side effect, two error measures are lower in maps formed by SOM-RP than in those formed by SOM.