I am also having a similar issue to this.
First, I can see that the redefinition of the network with the second use of selforgmap() will completely discard any training performed by the first call to train().
However removing this redefinition of your network is not sufficient to solve the issue.
Having taken the time to read the manual I also experimented with the use of adapt() instead of train() this is supposed to perform one step of the training process for the purpose of not batch processing data, which works fine with NARX and other feedforward MLP, but with SOM it seems to reset the network in the same way that train() does.
Digging even deeper I started to experiment with calling the learning function learnsomb() directly with the intend of manually applying the delta weight matrix that it returns, but I've struggled to find a suitable input for A.
After some reverse engineering of the MATLAB code I've finally run out of steam and I've come here to find answers.
Here the code I have so far:
% lifted from debugging MATLAB code
% feval(learnFcn,net.IW{i,j}, ...
% PD{i,j,ts},
% IWZ{i,j},
% N{i},
% Ac{i,ts+numLayerDelays},
% t,
% e,
% gIW{i,j},
% gA{i},
% net.layers{i}.distances,
% net.inputWeights{i,j}.learnParam,
% IWLS{i,j});
w = net.IW;
d = net.layers{1}.distances;
a = net(P); % this is not right :(
LP = net.inputWeights{1}.learnParam;
[dW,ls] = learnsomb(w,P,[],[],a,[],[],[],[],d,LP,[]);
