The activation frequency self-organizing map
Schlagworte: self-organization, Hebbian learning, non-radial neighborhood, DDC: 004 (Data processing, computer science, computer systems)
AbstractIn the self-organizing map (SOM), the best matching units (BMUs) affect neurons as a function of distance and the learning parameter. Here we study the effects in SOM when a new parameter in the learning rule, the activation frequency, is included. This parameter is based on the relative frequency by which each neuron is included in each BMU's neighborhood, so there is an individual memory (synapse strength) of the activation received from each neuron. The parameter leads to non-radial influence areas for BMUs, what is a more realistic feature observed in the brain cortex which modifies the map formation dynamics, including the fact that the weight vector for BMU may not be the closest one to the input stimulus after weight adaptation. Also, two error measures are lower for the maps trained with this model than those obtained with SOM, as shown in several experiments with six data sets.