What happens to the network performance when net.perfor​mParam.nor​malization = 'standard' is set?

I have use the following database to try to figure out my question above.

[x, t] = bodyfat_dataset;   Q = size(x, 2);   Q1 = floor(Q * 0.90);   Q2 = Q - Q1;
ind = randperm(Q);   ind1 = ind(1:Q1);   ind2 = ind(Q1 + (1:Q2));
x1 = x(:, ind1);   t1 = t(:, ind1);   x2 = x(:, ind2);   t2 = t(:, ind2);

net = feedforwardnet(10);
numNN = 10;   NN = cell(1, numNN);   perfs = zeros(1, numNN);
for i = 1:numNN
  fprintf('Training %d/%d\n', i, numNN);
[ NN{i} tr{i}] = train(net, x1, t1);
  y2 = NN{i}(x2);
end

If I set:

net.performParam.normalization = 'standard'
net.performFcn = 'mse'

This is what a found out:

perform(NN{i},y2{i},t2) = mse(NN{i},y2{i},t2, 'normalization', 'standard') % for a run perf = 0.0855

but it is NOT equal to: mse(NN{i},y2{i},t2) = mse(y2{i},t2) = mse(y2{i}-t2) = (sum(power((y2{i}-t2),2)))/length(t2) % for the same run = 48.224

For the initial training performance values

tr{i}.perf(1)

I get values ranging from 0.5656 up to 9.3844. So if errors are normalized to [-2,2] using the above command why tr.perf starts from values greater then 4.

If I sent net.performParam.normalization = 'none' % default net.performFcn = 'mse'

All of the above mse calculations are equal:

perform(NN{i},y2{i},t2) = mse(NN{i},y2{i},t2) =   mse(y2{i},t2) = mse(y2{i}-t2) = (sum(power((y2{i}-t2),2)))/length(t2)

and I get initial performance values ranging from 191.7 up to 431.72.

So for sure there is some time of normalization on the performance but I can't figure out how to get the output of perform() when net.performParam.normalization = 'standard' is set.

I have tried to normalize the inputs and targets with mapminmax, and/or zscore and I have never get

 perform(NN{i},y2{i},t2) = (sum(power((y2{i}-t2),2)))/length(t2)

what are the background operations of perform() when net.performParam.normalization = 'standard' is set?

Thanks!