The number of rows in the Jacobian output by "defaultderiv" is the sum of the number of weights and biases for the network. For example, if you do this to create the network:
[x,t] = simplefit_dataset;
net = feedforwardnet(10);
net = train(net,x,t);
y = net(x);
perf = perform(net,t,y);
dwb = defaultderiv('de_dwb',net,x,t);
Now "dwb" is the Jacobian of errors with respect to the net's weights and biases. It is a 31x94 matrix. If you check out the following properties in the network:
you can see that "net.IW" contains a 10x1 matrix, "net.LW" contains a 1x10 matrix, and "net.b" contains a 10-element vector and a 1-element vector. The number of elements adds up to 31.
I hope this helps clarify the Jacobian.