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Sort of a conceptual question here. I was implementing a SOM algorithm to better understand its variations and parameters and got curious about one bit: the BMU (best matching unit == the neuron that is more similar to the vector being presented) is chosen as the neuron that has the smallest distance in feature space to the vector. Then I update it and its neighbours.

This makes sense, but what if I used more than one BMU for updating the network? For example, suppose that the distance to one neuron is 0.03, but there is another neuron with distance 0.04. These are the two smallest distances. I would use the one with 0.03 as the BMU.

The question is, what would be the expected impacts on the algorithm if I used more than one BMU, for example, selecting for update all neurons for which the distance is up to 5% more than the minimum distance?

I am not asking for code, and can implement it to see what happens, I am just curious to see if anyone have any insight on the pros and cons (except additional complexity) of this approach.

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