How do I impose a sparcity constraint when training a simple neural network?

I am training a feedforward network, and would like to know how response properties change when I add an additional constraint on the error function. Specifically, I want to give a cost to neural activation to enforce sparcity.

To be clear: I know what the maths is, but I don't know how to get it working in the Matlab implementation.

I have tried editing nncalc.perform, as described here (https://uk.mathworks.com/matlabcentral/answers/64319-neural-network-how-to-use-a-custom-performance-function) by changing the

e=gsubtract(t,y);

line, but it doesn't seem to work.

The neural net setup is very basic:

% example net settings
net=feedforwardnet([10 10 5]);
net.performFcn= 'mse';
net=init(net);
net.trainFcn = 'trainlm';
net.trainParam.epochs=100;
net.trainParam.showWindow = 0;
net.trainParam.max_fail=5;

% some sample training data:
x=[ 13     2     5     0    13     3     4     0;
     6    15    12     0     6    16    13     0;
     2    14    14     0    14    15    14     0;
     5     4    11     0     5     5    11     0;
    12     9     9     0    11    10    10     0];

R=[NaN       NaN   -0.0010;
   NaN         0       NaN;
   NaN    2.0000       NaN;
   NaN       NaN   -0.0010;
   -0.0010       NaN       NaN];

net=train(net,x', R' );

Any help with this would be very much appreciated!