Understanding AForge SOM implementation

Question

Hello neural enthusiasts‎ out there, i am a little bit confused about the SOM Learning Algorithm in AForge. I figured out that the implementation assumes the most common case, a 2 dimensial SOM.

When i take a look at other SOM Graphics in the web, it figures out, that the position of the neuron changes over time. Similar neurons are put together.

I took a look at the source code and found out that the position of the neurons in the map is some kind of fixed. It is:

int wx = neuronIndex % width;
int wy = neuronIndex / width;

Is this just another Type of SOM with fixed possitions, or am i missinterprating something? I also thought that mainly you want to get an informational graphic out of a SOM, but there are no public available methods to retrieve the position of a Neuron.

Answer 1

Not familiar with AForge, but....

EDIT: First I was thinking that the weights are 2D and taught to resemble a grid, but this is even more educated guess: The moving grid of neurons you've seen is still not the SOM's nodes. The position of a SOM node is constant. The SOM is taught to abstract some dataset and a Sammon's mapping is likely used as a visualization method for the nodes' weights. The result is something like this and can probably be confused with the original SOM's lattice in which the nodes or "neurons" never move.

Note that this is still just an educated guess.