added some groundwork for recurrent neural networks

core/src/logic/World.java

@@ -241,6 +241,8 @@ public class World implements Runnable {
                     n = top[first].getBrain().breed(top[sec].getBrain().getMap());
                 } catch (Exception ex) {
                     // Should not happen
+                    Log.log(Log.ERROR, "Could not breed: " + ex.getMessage()
+                            + "\nIt is advised to restart the simulation after changing the brain's topology");
                     Logger.getLogger(World.class.getName()).log(Level.SEVERE, null, ex);
                 }
                 Creature ne = spawnCreature(n);

core/src/logic/neural/Brain.java

@@ -46,7 +46,7 @@ public class Brain {
             for (int j = 0; j < brainMap[i].length; j++) { // for each neuron
                 // skip input layer
                 if (neurons[i + 1][j] == null) {
-                    neurons[i + 1][j] = new Neuron(j, bias, this, brainMap[i][j]);
+                    neurons[i + 1][j] = new Neuron(i + 1, i, bias, this, brainMap[i][j]);
                 } else {
                     neurons[i + 1][j].setWeights(brainMap[i][j]);
                 }
@@ -63,7 +63,7 @@ public class Brain {
         for (int i = 0; i < neurons.length; i++) {
             for (int j = 0; j < neurons[i].length; j++) {
                 // create neuron
-                Neuron n = new Neuron(i, bias, this);
+                Neuron n = new Neuron(i, i - 1, bias, this);
                 neurons[i][j] = n;
                 Log.log(Log.DEBUG, "Adding Layer " + (i + 1) + " Neuron " + (j + 1));
             }

core/src/logic/neural/Neuron.java

@@ -15,7 +15,7 @@ public class Neuron {
     private NeuronCache cache;
     private float bias, output;
     private boolean isInputNeuron;
-    private int layer;
+    private int layer, receivesFromLayer;
     private Brain brain;
 
     /**
@@ -24,10 +24,12 @@ public class Neuron {
      *
      * @param layer the layer in which this neuron is positioned
      * @param bias the bias of this neuron
+     * @param receivesFromLayer the layer to read data from (negative for input
+     * neurons)
      * @param brain the brain which contains this neuron
      */
-    public Neuron(int layer, float bias, Brain brain) {
+    public Neuron(int layer, int receivesFromLayer, float bias, Brain brain) {
-        this(layer, bias, brain, null);
+        this(layer, receivesFromLayer, bias, brain, null);
     }
 
     /**
@@ -35,20 +37,27 @@ public class Neuron {
      * being the input layer, with given weights
      *
      * @param layer the layer in which this neuron is positioned
+     * @param receivesFromLayer the layer to read data from (negative for input
+     * neurons)
      * @param bias the bias of this neuron
      * @param brain the brain which contains this neuron
      * @param weights the weights to use to configure this neuron
      */
-    public Neuron(int layer, float bias, Brain brain, float[] weights) {
+    public Neuron(int layer, int receivesFromLayer, float bias, Brain brain, float[] weights) {
         this.brain = brain;
         this.layer = layer;
-        if (weights == null) {
+        this.receivesFromLayer = receivesFromLayer;
+        if (receivesFromLayer < 0 || layer == 0) {
+            isInputNeuron = true;
+        } else if (weights == null) {
             scramble();
         } else {
             this.weights = weights;
         }
+        if (!isInputNeuron) {
             cache = new NeuronCache(this.weights.length);
         }
+    }
 
     /**
      * Randomize the weights of this neuron
@@ -56,8 +65,8 @@ public class Neuron {
     private void scramble() {
         // init weights
         if (layer > 0) {
-            weights = new float[brain.getNeurons()[layer - 1].length];
+            weights = new float[brain.getNeurons()[receivesFromLayer].length];
-        } else { // layer 0
+        } else { // layer 0 or negative
             isInputNeuron = true;
             weights = new float[0];
         }
@@ -76,7 +85,9 @@ public class Neuron {
      * @return the output of this neuron.
      */
     public float compute() {
-        if(weights == null || weights.length == 0) isInputNeuron = true;
+        if (weights == null || weights.length == 0) {
+            isInputNeuron = true;
+        }
         if (isInputNeuron) {
             return output;
         }
@@ -93,7 +104,7 @@ public class Neuron {
                     Logger.getLogger(Neuron.class.getName()).log(Level.SEVERE, null, ex);
                 }
             } else {
-                Neuron n = brain.getNeurons()[layer - 1][i];
+                Neuron n = brain.getNeurons()[receivesFromLayer][i];
                 float v = n.compute() * weights[i];
                 a += v;
                 cache.put(i, v);