Can you restrict neuron connections in encog?

I created an example where I remove several connections in a neural network. I then train it, and the removed connections do not come back the network is retrained around the missing connections. It is checked into the 2.4 examples. You can see it here as well.


package org.encog.examples.neural.xorpartial;

import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.Layer;
import org.encog.neural.networks.synapse.Synapse;
import org.encog.util.logging.Logging;
import org.encog.util.simple.EncogUtility;

/**
* Partial neural networks. Encog allows you to remove any neuron connection in
* a fully connected neural network. This example creates a 2x10x10x1 neural
* network to learn the XOR. Several connections are removed prior to training.
*/
public class XORPartial {

public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
{ 0.0, 1.0 }, { 1.0, 1.0 } };

public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };

public static void main(final String args[]) {

Logging.stopConsoleLogging();

BasicNetwork network = EncogUtility.simpleFeedForward(2, 10, 10, 1,
false);
network.reset();

// obtain some of the synapses that we wish to remove connections from
Layer inputLayer = network.getLayer(BasicNetwork.TAG_INPUT);
Synapse inputToHidden1 = inputLayer.getNext().get(0);
Synapse hidden1ToHidden2 = inputToHidden1.getToLayer().getNext().get(0);
Synapse hidden2ToOutput = inputToHidden1.getToLayer().getNext().get(0);

// remove the connection from input neuron 0 to hidden1 neuron 1.
network.enableConnection(inputToHidden1, 0, 1, false);

// remove the connection from hidden1 neuron 2 to hidden2 neuron 3.
network.enableConnection(hidden1ToHidden2, 2, 3, false);

// remove the connection from hidden2 neuron 3 to output neuron 4.
network.enableConnection(hidden2ToOutput, 3, 4, false);

NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);

EncogUtility.trainToError(network, trainingSet, 0.01);

System.out
.println("Training should leave hidden neuron weights at zero.");
System.out.println("First removed neuron weight:"
+ inputToHidden1.getMatrix().get(0, 1));
System.out.println("First removed neuron weight:"
+ hidden1ToHidden2.getMatrix().get(2, 3));
System.out.println("First removed neuron weight:"
+ hidden2ToOutput.getMatrix().get(3, 4));
System.out.println("Final output:");
EncogUtility.evaluate(network, trainingSet);
}
}