Fix dangling clusters

Editor/ClusterEditor.cs

@@ -19,6 +19,8 @@ namespace NanoBrain.Unity {
             view.currentCluster ??= clusterPrefab.cluster;
             view.currentNucleus = clusterPrefab.cluster.defaultOutput;
             view.selectedOutput = view.currentNucleus;
+
+            clusterPrefab.cluster.Cleanup();
         }
 
         public override void OnInspectorGUI() {
@@ -395,24 +397,26 @@ namespace NanoBrain.Unity {
                 .Where(synapse => synapse.neuron != null)
                 .Select(synapse => synapse.neuron);
 
-            IEnumerable<Nucleus> nuclei = cluster.cluster.nuclei
+            Nucleus[] nuclei = cluster.cluster.nuclei
-                .Except(synapseNuclei);
+                .Except(synapseNuclei).Distinct().ToArray();
             IEnumerable<string> nucleiNames = nuclei
                 .Select(n => {
                     int idx = n.name.IndexOf(':');
                     return idx < 0 ? n.name : n.name[..idx];
-                })
+                });
-                .Distinct();
 
             string[] names = nucleiNames.ToArray();
             EditorGUILayout.BeginHorizontal();
             selectedConnectNucleus = EditorGUILayout.Popup(selectedConnectNucleus, names);
             bool connecting = GUILayout.Button("Connect", GUILayout.Width(80));
             EditorGUILayout.EndHorizontal();
-            if (connecting) {
+            if (connecting && selectedConnectNucleus >= 0 && selectedConnectNucleus < nuclei.Length) {
-                Nucleus nucleus = nuclei.ElementAt(selectedConnectNucleus);
+                Nucleus nucleus = nuclei[selectedConnectNucleus];//nuclei.ElementAt(selectedConnectNucleus);
                 if (nucleus is Neuron neuron)
                     neuron.AddReceiver(this.view.currentNucleus);
+                else if (nucleus is Cluster clusterToConnect) {
+                    clusterToConnect.defaultOutput.AddReceiver(this.view.currentNucleus);
+                }
                 this.view.currentCluster.Refresh();
             }
             return connecting;

Runtime/Scripts/Core/Cluster.cs

@@ -711,6 +711,7 @@ namespace NanoBrain {
         /// </summary>
         /// This can be used to recalculate derived properties after the set of nuclei has been changed
         public void Refresh() {
+            Cleanup();
             // This should not be needed, but somehow somewhere the parent is changed...
             foreach (Nucleus nucleus in this.nuclei) {
                 nucleus.parent = this;
@@ -719,6 +720,16 @@ namespace NanoBrain {
             RefreshComputeOrders();
         }
 
+        public void Cleanup() {
+            foreach (Nucleus nucleus in this.nuclei.ToArray()) {
+                if (nucleus is not Cluster cluster)
+                    continue;
+                List<Nucleus> receivers =  cluster.CollectReceivers();
+                Debug.Log($"cluster receiver count = {receivers.Count}");
+                if (receivers.Count == 0)
+                    this.nuclei.Remove(nucleus);
+            }
+        }
     }
 
 }

Runtime/Scripts/Core/Neuron.cs

@@ -363,6 +363,7 @@ namespace NanoBrain
         #region Combinator
 
 #if UNITY_MATHEMATICS
+        [NonSerialized]
         public float3 combinationValue;
 
         /// <summary>
@@ -742,6 +743,35 @@ namespace NanoBrain
             Debug.Log($"Updated weight: {error.magnitude} {error} {scaledOutput} {synapse.weight}");
         }
 
+        public void BackPropagationWithLoss(Synapse synapse, Vector3 loss, float learningRate)
+        {
+            // loss is a derivative of error
+            // Backpropagation = loss * d(combinator)
+
+            Vector3 delta2;
+            switch (activator)
+            {
+                case ActivationType.Linear:
+                    // Derivative of this (f'()) would be 1.
+                    delta2 = loss * 1;
+                    break;
+                case ActivationType.Power:
+                    delta2 = loss * (2 * this.combinationValue);
+                    break;
+                case ActivationType.Reciprocal:
+                    delta2 = loss * (-1 / (this.combinationValue * this.combinationValue));
+                    break;
+                default:
+                    delta2 = loss;
+                    break;
+            }
+
+            Vector3 scaledOutput = Vector3.Scale(delta2, synapse.neuron.outputValue);
+            float deltaWeight = Mathf.Abs(scaledOutput.x) + Mathf.Abs(scaledOutput.y) + Mathf.Abs(scaledOutput.z);
+            synapse.weight += learningRate * deltaWeight;
+            Debug.Log($"Updated weight: {loss.magnitude} {loss} {scaledOutput} {synapse.weight}");
+        }
+
         #endregion Back propagation
 
         /// <summary>