CODE>g</CODE> of the error function with respect to the network weights (i.e.
including the hidden unit parameters). The error
function is sum of squares.
Each row of <CODE>x</CODE> corresponds to one
input vector and each row of <CODE>t</CODE> contains the corresponding target vector.
If the output function is <CODE>'neuroscale'</CODE> then the gradient is only
computed for the output layer weights and biases.

<p><CODE>[g, gdata, gprior] = rbfgrad(net, x, t)</CODE> also returns separately 
the data and pr