Reorganizing the package and added documentation

.gitignore

@@ -0,0 +1 @@
+doxygen

ClusterPrefab.cs

@@ -1,116 +0,0 @@
-using System.Collections.Generic;
-using UnityEngine;
-
-[CreateAssetMenu(menuName = "Passer/Cluster")]
-public class ClusterPrefab : ScriptableObject {
-    // The ScriptableObject asset from which the runtime object has been created
-
-    [SerializeReference]
-    public List<Nucleus> nuclei = new();
-
-
-    public virtual Nucleus output => this.nuclei[0] as Nucleus;
-
-    public List<Nucleus> _inputs = null;
-    public virtual List<Nucleus> inputs {
-        get {
-            if (this._inputs == null) {
-                this._inputs = new();
-                foreach (Nucleus receptor in this.nuclei) {
-                    if (receptor is Nucleus nucleus) {
-                        // inputs have no incoming synapses yet.
-                        if (nucleus.synapses.Count == 0)
-                            this._inputs.Add(nucleus);
-                    }
-                }
-            }
-            return this._inputs;
-        }
-    }
-    private List<Nucleus> _outputs = null;
-    public List<Nucleus> outputs {
-        get {
-            if (this._outputs == null)
-                RefreshOutputs();
-            return this._outputs;
-        }
-    }
-    public void RefreshOutputs() {
-        this._outputs = new();
-        foreach (Nucleus nucleus in this.nuclei) {
-            if (nucleus is Neuron neuron && neuron.receivers.Count == 0)
-                this._outputs.Add(nucleus);
-        }
-    }
-
-    public Nucleus GetNucleus(string nucleusName) {
-        foreach (Nucleus nucleus in this.nuclei) {
-            if (nucleus.name == nucleusName)
-                return nucleus;
-        }
-        return null;
-    }
-
-    // Call this function to ensure that there is at least one nucleus
-    // This is an invariant and should be ensured before the nucleus is used
-    // because output requires it.
-    public void EnsureInitialization() {
-        nuclei ??= new List<Nucleus>();
-        if (nuclei.Count == 0)
-            new Neuron(this, "Output"); // Every cluster should have at least 1 neuron
-    }
-
-    public void GarbageCollection() {
-        HashSet<Nucleus> visitedNuclei = new();
-        foreach (Nucleus output in this.outputs)
-            MarkNuclei(visitedNuclei, output);
-        //Debug.Log($"Garbage collection found {visitedNuclei.Count} Nuclei");
-        this.nuclei.RemoveAll(nucleus => visitedNuclei.Contains(nucleus) == false);
-    }
-
-    public void MarkNuclei(HashSet<Nucleus> visitedNuclei, Nucleus nucleus) {
-        if (nucleus is null)
-            return;
-
-        if (nucleus.parent != null && nucleus.parent.prefab != this)
-            visitedNuclei.Add(nucleus.parent);
-        else
-            visitedNuclei.Add(nucleus);
-        if (nucleus.synapses != null) {
-            HashSet<Synapse> visitedSynapses = new();
-            foreach (Synapse synapse in nucleus.synapses) {
-                if (synapse != null && synapse.neuron != null) {
-                    visitedSynapses.Add(synapse);
-                    if (synapse.neuron is Nucleus synapse_nucleus)
-                        MarkNuclei(visitedNuclei, synapse_nucleus);
-                }
-            }
-            nucleus.synapses.RemoveAll(synapse => visitedSynapses.Contains(synapse) == false);
-        }
-        if (nucleus is Neuron neuron && neuron.receivers != null) {
-            HashSet<Nucleus> visitedReceivers = new();
-            foreach (Nucleus receiver in neuron.receivers) {
-                if (receiver != null && receiver != null) {
-                    visitedReceivers.Add(receiver);
-                    visitedNuclei.Add(receiver);
-                }
-            }
-            neuron.receivers.RemoveAll(receiver => visitedReceivers.Contains(receiver) == false);
-        }
-    }
-
-    public virtual void UpdateNuclei() {
-        foreach (Nucleus nucleus in this.nuclei)
-            nucleus.UpdateNuclei();
-    }
-
-    public int GetNucleusIndex(Nucleus receiver) {
-        int ix = 0;
-        foreach (Nucleus nucleus in this.nuclei) {
-            if (receiver == nucleus)
-                return ix;
-            ix++;
-        }
-        return -1;
-    }
-}

ClusterPrefab.cs.meta

@@ -1,2 +0,0 @@
-fileFormatVersion: 2
-guid: 60a957541c24c57e78018c202ebb1d9b

Editor/BrainEditorWindow.cs

@@ -3,6 +3,8 @@ using UnityEditor;
 using System.Collections.Generic;
 using System.Linq;
 
+namespace NanoBrain {
+
     // Simple DAG data model
     [System.Serializable]
     public class DagNode {
@@ -363,3 +365,5 @@ public class BrainEditorWindow : EditorWindow {
         }
 
     }
+
+}

Editor/BrainPickerWindow.cs

@@ -3,6 +3,8 @@ using UnityEngine;
 using System;
 using System.Linq;
 
+namespace NanoBrain {
+
     public class ClusterPickerWindow : EditorWindow {
         private Vector2 scroll;
         private ClusterPrefab[] items = new ClusterPrefab[0];
@@ -64,3 +66,5 @@ public class ClusterPickerWindow : EditorWindow {
             EditorGUILayout.EndHorizontal();
         }
     }
+
+}

Editor/ClusterInspector.cs

@@ -4,10 +4,8 @@ using UnityEditor;
 
 using UnityEngine;
 using UnityEngine.UIElements;
-// #if UNITY_MATHEMATICS
+
-// using Unity.Mathematics;
+namespace NanoBrain {
-// using static Unity.Mathematics.math;
-// #endif
 
     [CustomEditor(typeof(ClusterPrefab))]
     public class ClusterInspector : Editor {
@@ -1100,3 +1098,5 @@ public class NeuroidLayer {
         public int ix = 0;
         public List<Nucleus> neuroids = new();
     }
+
+}

Editor/DAGWindow.cs

@@ -4,6 +4,8 @@ using UnityEditor;
 using System.Collections.Generic;
 using System.Linq;
 
+namespace NanoBrain {
+
     // Simple DAG data model
     // [System.Serializable]
     // public class DagNode
@@ -21,8 +23,7 @@ using System.Linq;
     //     public int toId;
     // }
 
-public class DAGEditorWindow : EditorWindow
+    public class DAGEditorWindow : EditorWindow {
-{
         List<DagNode> nodes = new List<DagNode>();
         List<DagEdge> edges = new List<DagEdge>();
 
@@ -39,14 +40,12 @@ public class DAGEditorWindow : EditorWindow
         int draggingNodeId = -1;
 
         [MenuItem("Window/DAG Viewer (LR, Circles)")]
-    public static void ShowWindow()
+        public static void ShowWindow() {
-    {
             var w = GetWindow<DAGEditorWindow>("DAG Viewer (LR)");
             w.minSize = new Vector2(500, 300);
         }
 
-    void OnEnable()
+        void OnEnable() {
-    {
             labelStyle = new GUIStyle(EditorStyles.label);
             labelStyle.alignment = TextAnchor.MiddleCenter;
             labelStyle.normal.textColor = Color.white;
@@ -58,8 +57,7 @@ public class DAGEditorWindow : EditorWindow
             ComputeLeftToRightLayout();
         }
 
-    void CreateSampleGraph()
+        void CreateSampleGraph() {
-    {
             nodes.Clear();
             edges.Clear();
 
@@ -79,8 +77,7 @@ public class DAGEditorWindow : EditorWindow
             edges.Add(new DagEdge() { fromId = 4, toId = 6 });
         }
 
-    void OnGUI()
+        void OnGUI() {
-    {
             HandleInput();
 
             Rect rect = new Rect(0, 0, position.width, position.height);
@@ -92,8 +89,7 @@ public class DAGEditorWindow : EditorWindow
                          Matrix4x4.TRS(-origin, Quaternion.identity, Vector3.one);
 
             // Draw edges first
-        foreach (var e in edges)
+            foreach (var e in edges) {
-        {
                 var from = GetNodeById(e.fromId);
                 var to = GetNodeById(e.toId);
                 if (from == null || to == null) continue;
@@ -101,8 +97,7 @@ public class DAGEditorWindow : EditorWindow
             }
 
             // Draw nodes (circles)
-        foreach (var n in nodes)
+            foreach (var n in nodes) {
-        {
                 DrawNodeCircle(n);
             }
 
@@ -113,15 +108,12 @@ public class DAGEditorWindow : EditorWindow
             EditorGUILayout.BeginHorizontal(EditorStyles.toolbar);
             if (GUILayout.Button("Fit", EditorStyles.toolbarButton)) FitToView();
             if (GUILayout.Button("Layout LR", EditorStyles.toolbarButton)) ComputeLeftToRightLayout();
-        if (GUILayout.Button("Add Node", EditorStyles.toolbarButton))
+            if (GUILayout.Button("Add Node", EditorStyles.toolbarButton)) {
-        {
                 AddNode("N" + nodes.Count);
                 ComputeLeftToRightLayout();
             }
-        if (GUILayout.Button("Add Edge (selected->new)", EditorStyles.toolbarButton))
+            if (GUILayout.Button("Add Edge (selected->new)", EditorStyles.toolbarButton)) {
-        {
+                if (selectedNodeId != -1) {
-            if (selectedNodeId != -1)
-            {
                     var newNode = AddNode("N" + nodes.Count);
                     edges.Add(new DagEdge() { fromId = selectedNodeId, toId = newNode.id });
                     ComputeLeftToRightLayout();
@@ -130,13 +122,11 @@ public class DAGEditorWindow : EditorWindow
             EditorGUILayout.EndHorizontal();
         }
 
-    void HandleInput()
+        void HandleInput() {
-    {
             Event e = Event.current;
 
             // Zoom with scroll
-        if (e.type == EventType.ScrollWheel)
+            if (e.type == EventType.ScrollWheel) {
-        {
                 float oldZoom = zoom;
                 float delta = -e.delta.y * 0.01f;
                 zoom = Mathf.Clamp(zoom + delta, minZoom, maxZoom);
@@ -146,46 +136,38 @@ public class DAGEditorWindow : EditorWindow
             }
 
             // Pan with middle or right+ctrl drag
-        if (e.type == EventType.MouseDrag && (e.button == 2 || (e.button == 1 && e.control)))
+            if (e.type == EventType.MouseDrag && (e.button == 2 || (e.button == 1 && e.control))) {
-        {
                 pan += e.delta;
                 e.Use();
             }
 
             // Node dragging & selection (convert mouse to graph space)
             Vector2 graphMouse = ScreenToGraph(e.mousePosition);
-        if (e.type == EventType.MouseDown && e.button == 0)
+            if (e.type == EventType.MouseDown && e.button == 0) {
-        {
                 int hit = HitTestNode(graphMouse);
-            if (hit != -1)
+                if (hit != -1) {
-            {
                     selectedNodeId = hit;
                     draggingNode = true;
                     draggingNodeId = hit;
                     dragStart = graphMouse;
                     e.Use();
                 }
-            else
+                else {
-            {
                     selectedNodeId = -1;
                 }
             }
 
-        if (draggingNode && draggingNodeId != -1)
+            if (draggingNode && draggingNodeId != -1) {
-        {
+                if (e.type == EventType.MouseDrag && e.button == 0) {
-            if (e.type == EventType.MouseDrag && e.button == 0)
-            {
                     Vector2 graphDelta = e.delta / zoom;
                     var n = GetNodeById(draggingNodeId);
-                if (n != null)
+                    if (n != null) {
-                {
                         n.position += graphDelta;
                         Repaint();
                         e.Use();
                     }
                 }
-            if (e.type == EventType.MouseUp && e.button == 0)
+                if (e.type == EventType.MouseUp && e.button == 0) {
-            {
                     draggingNode = false;
                     draggingNodeId = -1;
                     e.Use();
@@ -193,8 +175,7 @@ public class DAGEditorWindow : EditorWindow
             }
         }
 
-    DagNode AddNode(string title)
+        DagNode AddNode(string title) {
-    {
             int nextId = nodes.Count > 0 ? nodes.Max(n => n.id) + 1 : 0;
             var n = new DagNode() { id = nextId, title = title, position = Vector2.zero };
             nodes.Add(n);
@@ -203,8 +184,7 @@ public class DAGEditorWindow : EditorWindow
 
         DagNode GetNodeById(int id) => nodes.FirstOrDefault(x => x.id == id);
 
-    void DrawNodeCircle(DagNode n)
+        void DrawNodeCircle(DagNode n) {
-    {
             Vector2 center = n.position;
             float r = n.radius;
             Rect nodeRect = new Rect(center.x - r, center.y - r, r * 2, r * 2);
@@ -222,19 +202,16 @@ public class DAGEditorWindow : EditorWindow
             GUI.Label(new Rect(labelPos.x - r, labelPos.y - 8, r * 2, 18), n.title, labelStyle);
         }
 
-    List<Vector3> GetCircleOutlinePoints(Vector2 center, float radius, int segments)
+        List<Vector3> GetCircleOutlinePoints(Vector2 center, float radius, int segments) {
-    {
             var pts = new List<Vector3>(segments + 1);
-        for (int i = 0; i <= segments; i++)
+            for (int i = 0; i <= segments; i++) {
-        {
                 float a = (float)i / segments * Mathf.PI * 2f;
                 pts.Add(new Vector3(center.x + Mathf.Cos(a) * radius, center.y + Mathf.Sin(a) * radius, 0));
             }
             return pts;
         }
 
-    void DrawEdgeCircleNodes(DagNode from, DagNode to)
+        void DrawEdgeCircleNodes(DagNode from, DagNode to) {
-    {
             Vector2 a = from.position;
             Vector2 b = to.position;
             if (a == b) return;
@@ -257,11 +234,9 @@ public class DAGEditorWindow : EditorWindow
             DrawArrowHead(end - dir * 2f, end, 10f / zoom, 12f / zoom, Color.white);
         }
 
-    List<Vector3> GetBezierPoints(Vector2 p0, Vector2 p1, Vector2 p2, int seg)
+        List<Vector3> GetBezierPoints(Vector2 p0, Vector2 p1, Vector2 p2, int seg) {
-    {
             var pts = new List<Vector3>(seg + 1);
-        for (int i = 0; i <= seg; i++)
+            for (int i = 0; i <= seg; i++) {
-        {
                 float t = (float)i / seg;
                 Vector2 p = (1 - t) * (1 - t) * p0 + 2 * (1 - t) * t * p1 + t * t * p2;
                 pts.Add(new Vector3(p.x, p.y, 0));
@@ -269,8 +244,7 @@ public class DAGEditorWindow : EditorWindow
             return pts;
         }
 
-    void DrawArrowHead(Vector2 from, Vector2 to, float headWidth, float headLength, Color color)
+        void DrawArrowHead(Vector2 from, Vector2 to, float headWidth, float headLength, Color color) {
-    {
             Vector2 dir = (to - from).normalized;
             if (dir == Vector2.zero) return;
             Vector2 right = new Vector2(-dir.y, dir.x);
@@ -284,13 +258,11 @@ public class DAGEditorWindow : EditorWindow
         }
 
         // Left-to-right layered layout (sources on the left, sinks on the right)
-    void ComputeLeftToRightLayout()
+        void ComputeLeftToRightLayout() {
-    {
             // build adjacency and indegree
             var adj = nodes.ToDictionary(n => n.id, n => new List<int>());
             var indeg = nodes.ToDictionary(n => n.id, n => 0);
-        foreach (var e in edges)
+            foreach (var e in edges) {
-        {
                 if (!adj.ContainsKey(e.fromId) || !adj.ContainsKey(e.toId)) continue;
                 adj[e.fromId].Add(e.toId);
                 indeg[e.toId]++;
@@ -301,12 +273,10 @@ public class DAGEditorWindow : EditorWindow
             Queue<int> q = new Queue<int>(indeg.Where(kv => kv.Value == 0).Select(kv => kv.Key));
             foreach (var id in q) layer[id] = 0;
 
-        while (q.Count > 0)
+            while (q.Count > 0) {
-        {
                 int u = q.Dequeue();
                 int l = layer[u];
-            foreach (var v in adj[u])
+                foreach (var v in adj[u]) {
-            {
                     // prefer placing v at least one layer after u
                     if (!layer.ContainsKey(v) || layer[v] < l + 1) layer[v] = l + 1;
                     indeg[v]--;
@@ -316,10 +286,8 @@ public class DAGEditorWindow : EditorWindow
 
             // Any unreachable nodes -> assign next layers
             int maxLayer = layer.Count > 0 ? layer.Values.Max() : 0;
-        foreach (var n in nodes)
+            foreach (var n in nodes) {
-        {
+                if (!layer.ContainsKey(n.id)) {
-            if (!layer.ContainsKey(n.id))
-            {
                     maxLayer++;
                     layer[n.id] = maxLayer;
                 }
@@ -333,12 +301,10 @@ public class DAGEditorWindow : EditorWindow
             float vSpacing = 120f;
 
             // Place nodes: x increases with layer index, y spaced within layer
-        for (int li = 0; li < layers.Count; li++)
+            for (int li = 0; li < layers.Count; li++) {
-        {
                 var lst = layers[li];
                 float totalHeight = (lst.Count - 1) * vSpacing;
-            for (int i = 0; i < lst.Count; i++)
+                for (int i = 0; i < lst.Count; i++) {
-            {
                     int id = lst[i];
                     var n = GetNodeById(id);
                     if (n == null) continue;
@@ -351,8 +317,7 @@ public class DAGEditorWindow : EditorWindow
             Repaint();
         }
 
-    void FitToView()
+        void FitToView() {
-    {
             if (nodes.Count == 0) return;
             Rect bounds = new Rect(nodes[0].position - Vector2.one * nodes[0].radius, Vector2.one * nodes[0].radius * 2f);
             foreach (var n in nodes)
@@ -364,8 +329,7 @@ public class DAGEditorWindow : EditorWindow
             Repaint();
         }
 
-    static Rect RectUnion(Rect a, Rect b)
+        static Rect RectUnion(Rect a, Rect b) {
-    {
             float xMin = Mathf.Min(a.xMin, b.xMin);
             float xMax = Mathf.Max(a.xMax, b.xMax);
             float yMin = Mathf.Min(a.yMin, b.yMin);
@@ -373,21 +337,20 @@ public class DAGEditorWindow : EditorWindow
             return Rect.MinMaxRect(xMin, yMin, xMax, yMax);
         }
 
-    Vector2 ScreenToGraph(Vector2 screenPos)
+        Vector2 ScreenToGraph(Vector2 screenPos) {
-    {
             Vector2 origin = new Vector2(position.width / 2, position.height / 2);
             // invert the GUI.matrix transform (approx for current simple transforms)
             return (screenPos - (origin + pan)) / zoom + origin * (1 - 1 / zoom);
         }
 
-    int HitTestNode(Vector2 graphPos)
+        int HitTestNode(Vector2 graphPos) {
-    {
             // returns node id under point or -1
-        for (int i = nodes.Count - 1; i >= 0; i--)
+            for (int i = nodes.Count - 1; i >= 0; i--) {
-        {
                 var n = nodes[i];
                 if ((graphPos - n.position).sqrMagnitude <= n.radius * n.radius) return n.id;
             }
             return -1;
         }
     }
+
+}

Editor/Icons/NeuraalNetwerkIcoonSchets1.png.meta

@@ -52,7 +52,7 @@ TextureImporter:
   spriteBorder: {x: 0, y: 0, z: 0, w: 0}
   spriteGenerateFallbackPhysicsShape: 1
   alphaUsage: 1
-  alphaIsTransparency: 0
+  alphaIsTransparency: 1
   spriteTessellationDetail: -1
   textureType: 0
   textureShape: 1

Editor/Icons/NeuraalNetwerkIcoonSchets2.png.meta

@@ -52,7 +52,7 @@ TextureImporter:
   spriteBorder: {x: 0, y: 0, z: 0, w: 0}
   spriteGenerateFallbackPhysicsShape: 1
   alphaUsage: 1
-  alphaIsTransparency: 0
+  alphaIsTransparency: 1
   spriteTessellationDetail: -1
   textureType: 0
   textureShape: 1

Editor/NanoBrain_Editor.cs

@@ -4,6 +4,8 @@ using UnityEditor.UIElements;
 using UnityEngine;
 using UnityEngine.UIElements;
 
+namespace NanoBrain {
+
     [CustomEditor(typeof(NanoBrain))]
     public class NanoBrainComponent_Editor : Editor {
         protected static VisualElement mainContainer;
@@ -47,3 +49,5 @@ public class NanoBrainComponent_Editor : Editor {
         }
 
     }
+
+}

IReceptor.cs

@@ -1,73 +0,0 @@
-using UnityEngine;
-
-public interface IReceptor {
-    public string GetName();
-
-    public Nucleus[] nucleiArray { get; set; }
-
-    public void AddReceptorElement(ClusterPrefab prefab);
-    public void RemoveReceptorElement();
-
-    public void AddArrayReceiver(Nucleus receiverToAdd, float weight = 1);
-
-    public void ProcessStimulus(Vector3 inputValue, int thingId = 0, string thingName = null);
-}
-
-public static class IReceptorHelpers {
-
-    public static void AddReceptorElement(IReceptor receptor, ClusterPrefab prefab) {
-        if (receptor.nucleiArray.Length == 0) {
-            Debug.LogError("Empty perceptoid array, cannot add");
-        }
-        int newLength = receptor.nucleiArray.Length + 1;
-        Nucleus[] newArray = new Nucleus[newLength];
-
-        string baseName = receptor.GetName();
-        int colonPos = baseName.IndexOf(":");
-        if (colonPos > 0)
-            baseName = baseName[..colonPos];
-
-        for (int i = 0; i < receptor.nucleiArray.Length; i++)
-            newArray[i] = receptor.nucleiArray[i];
-        if (receptor.nucleiArray[0] is Nucleus nucleus) {
-            newArray[newLength - 1] = nucleus.Clone(prefab);
-            newArray[newLength - 1].name = $"{baseName}: {newLength - 1}";
-        }
-
-        foreach (Nucleus element in receptor.nucleiArray) {
-            if (element is IReceptor receptorElement) {
-                receptorElement.nucleiArray = newArray;
-            }
-        }
-    }
-
-    public static void RemoveReceptorElement(IReceptor receptor) {
-        int newLength = receptor.nucleiArray.Length - 1;
-        if (newLength == 0) {
-            Debug.LogWarning("Perceptoid array cannot be empty");
-        }
-        Nucleus[] newArray = new Nucleus[newLength];
-        for (int i = 0; i < newLength; i++)
-            newArray[i] = receptor.nucleiArray[i];
-        // Delete the last perception
-        if (receptor.nucleiArray[newLength] is Nucleus nucleus)
-            Neuron.Delete(nucleus);
-            
-        foreach (Nucleus element in receptor.nucleiArray) {
-            if (element is IReceptor receptorElement) {
-                receptorElement.nucleiArray = newArray;
-            }
-        }
-
-    }
-
-    public static void AddArrayReceiver(IReceptor receptor, Nucleus receiverToAdd, float weight = 1) {
-        foreach (Nucleus element in receptor.nucleiArray) {
-            if (element is Cluster cluster)
-                cluster.defaultOutput.AddReceiver(receiverToAdd, weight);
-            if (element is Neuron neuron)
-                neuron.AddReceiver(receiverToAdd, weight);
-        }
-
-    }
-}

MemoryCell.cs

@@ -1,65 +0,0 @@
-using System;
-#if UNITY_MATHEMATICS
-using Unity.Mathematics;
-#endif
-
-[Serializable]
-public class MemoryCell : Neuron {
-
-    public MemoryCell(ClusterPrefab cluster, string name) : base(cluster, name) { }
-    public MemoryCell(Cluster parent, string name) : base(parent, name) { }
-
-    public bool staticMemory = false;
-    public override bool isSleeping {
-        get {
-            if (staticMemory)
-                return false;
-
-            return base.isSleeping;
-        }
-    }
-
-    public override Nucleus ShallowCloneTo(Cluster newParent) {
-        MemoryCell clone = new(newParent, this.name);
-        CloneFields(clone);
-        clone.staticMemory = this.staticMemory;
-        return clone;
-    }
-
-    #region State
-
-    private bool initialized = false;
-
-#if UNITY_MATHEMATICS
-    private float3 _memorizedValue;
-#else
-    private UnityEngine.Vector3 _memorizedValue;
-#endif
-
-    public override void UpdateStateIsolated() {
-        // A memorycell does not have an activation function
-        var result = Combinator();
-
-        if (initialized)
-            // Output the previous, memorized value
-            this.outputValue = this._memorizedValue;
-        else {
-            // The first time, the result is directly set in output
-            this.outputValue = result;
-            this.initialized = true;
-        }
-
-        // Store the result for the next time
-        this._memorizedValue = result;
-    }
-
-    public override void UpdateNuclei() {
-        if (staticMemory)
-            // Static memory does not get stale or go to sleep
-            return;
-
-        base.UpdateNuclei();
-    }
-
-    #endregion State
-}

NanoBrain.cs

@@ -1,31 +0,0 @@
-using System;
-using UnityEngine;
-
-public class NanoBrain : MonoBehaviour {
-    public ClusterPrefab defaultBrain;
-
-    [NonSerialized]
-    private Cluster brainInstance;
-    public Cluster brain {
-        get {
-            if (brainInstance == null && defaultBrain != null) {
-                brainInstance = new Cluster(defaultBrain) {
-                    name = defaultBrain.name + " (Instance)"
-                };
-            }
-            return brainInstance;
-        }
-    }
-
-    public static void UpdateWeight(Cluster brain, string name, float weight) {
-        Nucleus root = brain.defaultOutput;
-        foreach (Synapse synapse in root.synapses) {
-            if (synapse.neuron.name == name) {
-                if (synapse.weight != weight) {
-                    synapse.weight = weight;
-                    // Debug.Log($"Updated weight for {name}");
-                }
-            }
-        }
-    }
-}

Neuron.cs

@@ -1,445 +0,0 @@
-using System;
-using System.Collections.Generic;
-using UnityEngine;
-using UnityEditor;
-#if UNITY_MATHEMATICS
-using Unity.Mathematics;
-using static Unity.Mathematics.math;
-#endif
-
-[Serializable]
-public class Neuron : Nucleus {
-
-    public Neuron(Cluster parent, string name) {
-        this.parent = parent;
-        this.name = name;
-        this.parent?.clusterNuclei.Add(this);
-    }
-    public Neuron(ClusterPrefab prefab, string name) {
-        this.clusterPrefab = prefab;
-        this.name = name;
-        if (this.clusterPrefab != null)
-            this.clusterPrefab.nuclei.Add(this);
-        else
-            Debug.LogError("No prefab when adding neuron to prefab");
-    }
-
-    #region Serialization
-
-    public enum CombinatorType {
-        Sum,
-        Product,
-        Max
-    }
-    public CombinatorType combinator = CombinatorType.Sum;
-
-    public enum CurvePresets {
-        Linear,
-        Power,
-        Sqrt,
-        Reciprocal,
-        Custom
-    }
-    [SerializeField]
-    public CurvePresets _curvePreset;
-    public CurvePresets curvePreset {
-        get { return _curvePreset; }
-        set {
-            _curvePreset = value;
-            this.curve = GenerateCurve();
-        }
-    }
-    public AnimationCurve curve;
-    public float curveMax = 1.0f;
-
-    public AnimationCurve GenerateCurve() {
-        switch (this.curvePreset) {
-            case CurvePresets.Linear:
-                this.curveMax = 1;
-                return Presets.Linear(1);
-            case CurvePresets.Power:
-                this.curveMax = 1;
-                return Presets.Power(2.0f, 1);
-            case CurvePresets.Sqrt:
-                this.curveMax = 1;
-                return Presets.Power(0.5f, 1);
-            case CurvePresets.Reciprocal:
-                this.curveMax = 1 / 0.01f * 1;
-                return Presets.Reciprocal(1);
-            default:
-                this.curveMax = 1;
-                return this.curve;
-        }
-    }
-
-    public static class Presets {
-        private const int samples = 32;
-        public static AnimationCurve Linear(float weight) {
-            return AnimationCurve.Linear(0f, 0f, 1000f, weight * 1000);
-        }
-        public static AnimationCurve Power(float exponent, float weight) {
-            // build keyframes
-            Keyframe[] keys = new Keyframe[samples];
-            for (int i = 0; i < samples; i++) {
-                float t = i / (float)(samples - 1);
-                float v = Mathf.Pow(t, exponent) * weight;
-                keys[i] = new Keyframe(t, v);
-            }
-
-            AnimationCurve curve = new(keys);
-
-            // set tangent modes for each key to Auto (smooth). Use Linear if you prefer straight segments.
-            for (int i = 0; i < curve.length; i++) {
-                AnimationUtility.SetKeyLeftTangentMode(curve, i, AnimationUtility.TangentMode.Auto);
-                AnimationUtility.SetKeyRightTangentMode(curve, i, AnimationUtility.TangentMode.Auto);
-            }
-
-            return curve;
-        }
-        public static AnimationCurve Reciprocal(float weight) {
-            int samples = 128;
-            float xMin = 0.001f;
-            float xMax = 1;
-            var keys = new Keyframe[samples];
-            for (int i = 0; i < samples; i++) {
-                float t = i / (float)(samples - 1);
-                float x = Mathf.Lerp(xMin, xMax, t);
-                float y = 1f / x * weight;
-                keys[i] = new Keyframe(x, y);
-            }
-            var curve = new AnimationCurve(keys);
-            for (int i = 0; i < curve.length; i++) {
-                AnimationUtility.SetKeyLeftTangentMode(curve, i, AnimationUtility.TangentMode.Linear);
-                AnimationUtility.SetKeyRightTangentMode(curve, i, AnimationUtility.TangentMode.Linear);
-            }
-            return curve;
-        }
-    }
-
-    #endregion Serialization
-
-#if UNITY_MATHEMATICS
-
-    protected float3 _outputValue;
-    public virtual float3 outputValue {
-        get { return _outputValue; }
-        set {
-            _outputValue = value;
-            if (this.isFiring)
-                WhenFiring?.Invoke();
-        }
-    }
-    public float outputMagnitude => length(_outputValue);    
-
-    public bool isFiring => length(_outputValue) > 0.5f;
-    public virtual bool isSleeping => lengthsq(this.outputValue) == 0;
-
-#else
-
-    protected Vector3 _outputValue;
-    public virtual Vector3 outputValue {
-        get { return _outputValue; }
-        set {
-            _outputValue = value;
-            if (this.isFiring)
-                WhenFiring?.Invoke();
-        }
-    }
-    public float outputMagnitude => _outputValue.magnitude;    
-
-    public bool isFiring => _outputValue.magnitude > 0.5f;
-    public virtual bool isSleeping => this.outputValue.sqrMagnitude == 0;
-
-#endif
-    public Action WhenFiring;
-
-    [NonSerialized]
-    public int stale = 1000;
-    public readonly int staleValueForSleep = 20;
-
-    // this clone the nucleus without the synapses and receivers
-    public override Nucleus ShallowCloneTo(Cluster newParent) {
-        Neuron clone = new(newParent, this.name);
-        CloneFields(clone);
-        return clone;
-    }
-
-    public override Nucleus Clone(ClusterPrefab prefab) {
-        Neuron clone = new(prefab, this.name);
-        CloneFields(clone);
-        foreach (Synapse synapse in this.synapses) {
-            Synapse clonedSynapse = clone.AddSynapse(synapse.neuron);
-            clonedSynapse.weight = synapse.weight;
-        }
-        foreach (Nucleus receiver in this.receivers) {
-            clone.AddReceiver(receiver);
-        }
-        return clone;
-    }
-
-    protected virtual void CloneFields(Neuron clone) {
-        clone.clusterPrefab = this.clusterPrefab;
-        clone.bias = this.bias;
-        clone.combinator = this.combinator;
-        clone.curve = this.curve;
-        clone.curvePreset = this.curvePreset;
-        clone.curveMax = this.curveMax;
-    }
-
-    public static void Delete(Nucleus nucleus) {
-        foreach (Synapse synapse in nucleus.synapses) {
-            if (synapse.neuron is Neuron synapse_nucleus) {
-                if (synapse_nucleus.receivers.Count > 1) {
-                    // there is another nucleus feeding into this input nucleus
-                    synapse_nucleus.receivers.RemoveAll(r => r == nucleus);
-                }
-                else {
-                    // No other links, delete it.
-                    Neuron.Delete(synapse_nucleus);
-                }
-            }
-        }
-        if (nucleus is Neuron neuron) {
-            foreach (Nucleus receiver in neuron.receivers) {
-                if (receiver != null && receiver.synapses != null)
-                    receiver.synapses.RemoveAll(s => s.neuron == nucleus);
-            }
-        }
-        else if (nucleus is Cluster cluster) {
-            // remove all receivers for this cluster
-            foreach (Neuron output in cluster.outputs) {
-                foreach (Nucleus receiver in output.receivers) {
-                    receiver.synapses.RemoveAll(s => s.neuron == output);
-                }
-            }
-        }
-
-
-        if (nucleus.clusterPrefab != null) {
-            nucleus.clusterPrefab.nuclei.RemoveAll(n => n == nucleus);
-            nucleus.clusterPrefab.RefreshOutputs();
-            nucleus.clusterPrefab.GarbageCollection();
-        }
-    }
-
-    public override void UpdateStateIsolated() {
-        var result = Combinator();
-        this.outputValue = Activator(result);
-    }
-
-    #region Combinator
-
-#if UNITY_MATHEMATICS
-
-    protected Func<float3> Combinator => combinator switch {
-        CombinatorType.Sum => CombinatorSum,
-        CombinatorType.Product => CombinatorProduct,
-        CombinatorType.Max => CombinatorMax,
-        _ => CombinatorSum
-    };
-
-    public float3 CombinatorSum() {
-        float3 sum = this.bias;
-        foreach (Synapse synapse in this.synapses)
-            sum += synapse.weight * synapse.neuron.outputValue;
-        return sum;
-    }
-
-    public float3 CombinatorProduct() {
-        float3 product = this.bias;
-        foreach (Synapse synapse in this.synapses) {
-            product *= synapse.weight * synapse.neuron.outputValue;
-        }
-        return product;
-    }
-
-    public float3 CombinatorMax() {
-        float3 max = this.bias;
-        float maxLength = length(max);
-
-        //Applying the weight factors
-        foreach (Synapse synapse in this.synapses) {
-            float3 input = synapse.weight * synapse.neuron.outputValue;
-
-            float inputLength = length(input);
-            if (inputLength > maxLength) {
-                max = input;
-                maxLength = inputLength;
-            }
-        }
-        return max;
-    }
-
-#else 
-
-    protected Func<Vector3> Combinator => combinator switch {
-        CombinatorType.Sum => CombinatorSum,
-        CombinatorType.Product => CombinatorProduct,
-        CombinatorType.Max => CombinatorMax,
-        _ => CombinatorSum
-    };
-
-    public Vector3 CombinatorSum() {
-        Vector3 sum = this.bias;
-        foreach (Synapse synapse in this.synapses)
-            sum += synapse.weight * synapse.neuron.outputValue;
-        return sum;
-    }
-
-    public Vector3 CombinatorProduct() {
-        Vector3 product = this.bias;
-        foreach (Synapse synapse in this.synapses) {
-            //product *= synapse.weight * synapse.neuron.outputValue;
-            product = Vector3.Scale(product, synapse.weight * synapse.neuron.outputValue);
-        }
-        return product;
-    }
-
-    public Vector3 CombinatorMax() {
-        Vector3 max = this.bias;
-        float maxLength = max.magnitude;
-
-        //Applying the weight factors
-        foreach (Synapse synapse in this.synapses) {
-            Vector3 input = synapse.weight * synapse.neuron.outputValue;
-
-            float inputLength = input.magnitude;
-            if (inputLength > maxLength) {
-                max = input;
-                maxLength = inputLength;
-            }
-        }
-        return max;
-    }
-#endif
-    #endregion Combinator
-
-    #region Activator
-
-#if UNITY_MATHEMATICS
-
-    public Func<float3, float3> Activator => this.curvePreset switch {
-        CurvePresets.Linear => ActivatorLinear,
-        CurvePresets.Sqrt => ActivatorSqrt,
-        CurvePresets.Power => ActivatorPower,
-        CurvePresets.Reciprocal => ActivatorReciprocal,
-        _ => ActivatorCustom
-    };
-
-    protected float3 ActivatorLinear(float3 input) {
-        return input;
-    }
-
-    protected float3 ActivatorSqrt(float3 input) {
-        float3 result = normalize(input) * System.MathF.Sqrt(length(input));
-        return result;
-    }
-
-    protected float3 ActivatorPower(float3 input) {
-        float3 result = normalize(input) * System.MathF.Pow(length(input), 2);
-        return result;
-    }
-
-    protected float3 ActivatorReciprocal(float3 input) {
-        float magnitude = length(input);
-        if (magnitude == 0)
-            return new float3(0, 0, 0);
-
-        float3 result = normalize(input) * (1 / magnitude);
-        return result;
-    }
-
-    protected float3 ActivatorCustom(float3 input) {
-        float activatedValue = this.curve.Evaluate(length(input));
-        float3 result = normalize(input) * activatedValue;
-        return result;
-    }
-
-#else
-
-    public Func<Vector3, Vector3> Activator => this.curvePreset switch {
-        CurvePresets.Linear => ActivatorLinear,
-        CurvePresets.Sqrt => ActivatorSqrt,
-        CurvePresets.Power => ActivatorPower,
-        CurvePresets.Reciprocal => ActivatorReciprocal,
-        _ => ActivatorCustom
-    };
-
-    protected Vector3 ActivatorLinear(Vector3 input) {
-        return input;
-    }
-
-    protected Vector3 ActivatorSqrt(Vector3 input) {
-        Vector3 result = input.normalized * System.MathF.Sqrt(input.magnitude);
-        return result;
-    }
-
-    protected Vector3 ActivatorPower(Vector3 input) {
-        Vector3 result = input.normalized * System.MathF.Pow(input.magnitude, 2);
-        return result;
-    }
-
-    protected Vector3 ActivatorReciprocal(Vector3 input) {
-        float magnitude = input.magnitude;
-        if (magnitude == 0)
-            return new Vector3(0, 0, 0);
-
-        Vector3 result = input.normalized * (1 / magnitude);
-        return result;
-    }
-
-    protected Vector3 ActivatorCustom(Vector3 input) {
-        float activatedValue = this.curve.Evaluate(input.magnitude);
-        Vector3 result = input.normalized * activatedValue;
-        return result;
-    }
-
-#endif
-
-    #endregion Activator
-
-    #region Receivers
-
-    [SerializeReference]
-    private List<Nucleus> _receivers = new();
-    public virtual List<Nucleus> receivers {
-        get { return _receivers; }
-        set { _receivers = value; }
-    }
-
-    public virtual void AddReceiver(Nucleus receiverToAdd, float weight = 1) {
-        this._receivers.Add(receiverToAdd);
-        receiverToAdd.AddSynapse(this, weight);
-    }
-
-    public virtual void RemoveReceiver(Nucleus receiverToRemove) {
-        if (this is IReceptor receptor) {
-            foreach (Nucleus element in receptor.nucleiArray) {
-                if (element is Neuron neuron) {
-                    neuron._receivers.RemoveAll(receiver => receiver == receiverToRemove);
-                    receiverToRemove.synapses.RemoveAll(synapse => synapse.neuron == neuron);
-                }
-            }
-        }
-        else {
-            this._receivers.RemoveAll(receiver => receiver == receiverToRemove);
-            receiverToRemove.synapses.RemoveAll(synapse => synapse.neuron == this);
-        }
-    }
-
-
-    #endregion Receivers
-
-    public override void ProcessStimulus(Vector3 inputValue, int thingId = 0, string thingName = null) {
-        if (this.parent is ClusterReceptor clusterReceptor)
-            clusterReceptor.ProcessStimulus(this, inputValue, thingId, thingName);
-        else
-            ProcessStimulusDirect(inputValue, thingId, thingName);
-    }
-
-    public void ProcessStimulusDirect(Vector3 inputValue, int thingId = 0, string thingName = null) {
-        this.stale = 0;
-        this.bias = inputValue;
-        this.parent.UpdateFromNucleus(this);
-    }
-}

Nucleus.cs

@@ -1,72 +0,0 @@
-using System;
-using System.Collections.Generic;
-using UnityEngine;
-
-[Serializable]
-public abstract class Nucleus {
-    public string name;
-
-    [SerializeReference]
-    public ClusterPrefab clusterPrefab;
-    [SerializeReference]
-    public Cluster parent;
-
-    public bool trace = false;
-
-    public abstract Nucleus ShallowCloneTo(Cluster parent);
-    public abstract Nucleus Clone(ClusterPrefab prefab);
-
-    public enum Type {
-        None,
-        Neuron,
-        MemoryCell,
-        Cluster,
-        Receptor,
-        ClusterReceptor,
-    }
-
-    #region Synapses
-
-    public Vector3 bias = Vector3.zero;
-
-    [SerializeField]
-    private List<Synapse> _synapses = new();
-    public List<Synapse> synapses => _synapses;
-
-    public Synapse AddSynapse(Neuron sendingNucleus, float weight = 1.0f) {
-        Synapse synapse = new(sendingNucleus, weight);
-        this.synapses.Add(synapse);
-        return synapse;
-    }
-
-    public Synapse GetSynapse(Nucleus sender) {
-        foreach (Synapse synapse in this.synapses)
-            if (synapse.neuron == sender)
-                return synapse;
-        return null;
-    }
-
-    public void RemoveSynapse(Nucleus sendingNucleus) {
-        this.synapses.RemoveAll(synapse => synapse.neuron == sendingNucleus);
-    }
-
-    #endregion Synapses
-
-    #region Update
-
-    public abstract void UpdateStateIsolated();
-
-    public virtual void UpdateNuclei() {
-    }
-
-    public virtual void SetBias(Vector3 inputValue) {
-        this.bias = inputValue;
-        this.parent.UpdateFromNucleus(this);
-    }
-
-    public virtual void ProcessStimulus(Vector3 inputValue, int thingId = 0, string thingName = "") {
-    }
-
-    #endregion Update
-
-}

NucleusArray.cs

@@ -1,208 +0,0 @@
-using System.Linq;
-using System.Collections.Generic;
-using UnityEngine;
-#if UNITY_MATHEMATICS
-using Unity.Mathematics;
-using static Unity.Mathematics.math;
-#endif
-
-[System.Serializable]
-public class NucleusArray {
-    [SerializeReference]
-    private Nucleus[] _nuclei;
-    public Nucleus[] nuclei {
-        get {
-            return _nuclei;
-        }
-        set {
-            _nuclei = value;
-        }
-    }
-
-    public NucleusArray(Nucleus nucleus) {
-        this._nuclei = new Nucleus[1];
-        this._nuclei[0] = nucleus;
-    }
-    public NucleusArray(ClusterPrefab cluster) {
-        this._nuclei = new Nucleus[0];
-    }
-    public NucleusArray(int size, string name) {
-        this._nuclei = new Nucleus[size];
-    }
-
-
-    public void AddNucleus(ClusterPrefab prefab) {
-        if (this._nuclei.Length == 0) {
-            Debug.LogError("Empty perceptoid array, cannot add");
-            return;
-        }
-        int newLength = this._nuclei.Length + 1;
-        Nucleus[] newArray = new Nucleus[newLength];
-
-        for (int i = 0; i < this._nuclei.Length; i++)
-            newArray[i] = this._nuclei[i];
-        if (this._nuclei[0] is Nucleus nucleus) {
-            newArray[newLength - 1] = nucleus.Clone(prefab);
-            newArray[newLength - 1].name += $": {newLength - 1}"; 
-        }
-
-        this._nuclei = newArray;
-    }
-
-    public void RemoveNucleus() {
-        int newLength = this._nuclei.Length - 1;
-        if (newLength == 0) {
-            Debug.LogWarning("Perceptoid array cannot be empty");
-            return;
-        }
-        Nucleus[] newPerceptei = new Nucleus[newLength];
-        for (int i = 0; i < newLength; i++)
-            newPerceptei[i] = this._nuclei[i];
-        // Delete the last perception
-        if (this._nuclei[newLength] is Nucleus nucleus)
-            Neuron.Delete(nucleus); //this._nuclei[newLength]);
-
-        this._nuclei = newPerceptei;
-    }
-
-    public Dictionary<int, Nucleus> thingReceivers = new();
-
-#if UNITY_MATHEMATICS
-
-    private Nucleus FindReceiver(int thingId, float3 inputValue) {
-        // No existing nucleus for this thing
-        float inputMagnitude = length(inputValue);
-        Neuron selectedReceiver = null;
-        float selectedMagnitude = 0;
-        foreach (Nucleus nucleusReceiver in this._nuclei) {
-            if (nucleusReceiver is not Neuron receiver)
-                continue;
-            if (thingReceivers.ContainsValue(receiver) == false) {
-                // We found an unusued receiver
-                thingReceivers.Add(thingId, receiver);
-                return receiver;
-            }
-            else if (receiver.isSleeping) {
-                // A sleeping receiver is not active and can therefore always be used
-                thingReceivers.Add(thingId, receiver);
-                return receiver;
-            }
-            else if (selectedReceiver == null) {
-                // If we haven't found a receiver yet, just start by taking the first
-                selectedReceiver = receiver;
-                selectedMagnitude = length(selectedReceiver.outputValue);
-            }
-            // Look for the receiver with the lowest magnitude
-            else {
-                float magnitude = length(receiver.outputValue);
-                
-                if (magnitude < inputMagnitude && length(receiver.outputValue) < selectedMagnitude) {
-                    selectedReceiver = receiver;
-                    selectedMagnitude = length(selectedReceiver.outputValue);
-                }
-            }
-        }
-        if (selectedReceiver != null) {
-            // Replace the receiver
-            // Find the thingId current associated with the receiver
-            int keyToRemove = thingReceivers.FirstOrDefault(r => r.Value.Equals(selectedReceiver)).Key;
-            if (keyToRemove != 0 || thingReceivers.ContainsKey(keyToRemove))
-                thingReceivers.Remove(keyToRemove);
-            // And add the new association
-            thingReceivers.Add(thingId, selectedReceiver);
-        }
-        return selectedReceiver;
-    }
-
-#else
-
-    private Nucleus FindReceiver(int thingId, Vector3 inputValue) {
-        // No existing nucleus for this thing
-        float inputMagnitude = inputValue.magnitude;
-        Neuron selectedReceiver = null;
-        float selectedMagnitude = 0;
-        foreach (Nucleus nucleusReceiver in this._nuclei) {
-            if (nucleusReceiver is not Neuron receiver)
-                continue;
-            if (thingReceivers.ContainsValue(receiver) == false) {
-                // We found an unusued receiver
-                thingReceivers.Add(thingId, receiver);
-                return receiver;
-            }
-            else if (receiver.isSleeping) {
-                // A sleeping receiver is not active and can therefore always be used
-                thingReceivers.Add(thingId, receiver);
-                return receiver;
-            }
-            else if (selectedReceiver == null) {
-                // If we haven't found a receiver yet, just start by taking the first
-                selectedReceiver = receiver;
-                selectedMagnitude = selectedReceiver.outputMagnitude;
-            }
-            // Look for the receiver with the lowest magnitude
-            else {
-                float magnitude = receiver.outputMagnitude;
-                
-                if (magnitude < inputMagnitude && receiver.outputMagnitude < selectedMagnitude) {
-                    selectedReceiver = receiver;
-                    selectedMagnitude = selectedReceiver.outputMagnitude;
-                }
-            }
-        }
-        if (selectedReceiver != null) {
-            // Replace the receiver
-            // Find the thingId current associated with the receiver
-            int keyToRemove = thingReceivers.FirstOrDefault(r => r.Value.Equals(selectedReceiver)).Key;
-            if (keyToRemove != 0 || thingReceivers.ContainsKey(keyToRemove))
-                thingReceivers.Remove(keyToRemove);
-            // And add the new association
-            thingReceivers.Add(thingId, selectedReceiver);
-        }
-        return selectedReceiver;
-    }
-#endif
-
-    public virtual void ProcessStimulus(int thingId, Vector3 inputValue, string thingName = null) {
-        CleanupReceivers();
-
-        if (this._nuclei[0] is Neuron neuron)
-            inputValue = neuron.Activator(inputValue);
-
-        if (!thingReceivers.TryGetValue(thingId, out Nucleus selectedReceiver)) {
-            // No existing nucleus for this thing
-            selectedReceiver = FindReceiver(thingId, inputValue);
-        }
-        if (selectedReceiver == null)
-            return;
-
-        if (thingName != null) {
-            string baseName = selectedReceiver.name;
-            int colonPos = selectedReceiver.name.IndexOf(":");
-            if (colonPos > 0)
-                baseName = selectedReceiver.name[..colonPos];
-            selectedReceiver.name = baseName + ": " + thingName;
-        }
-
-        if (selectedReceiver is Neuron selectedNucleus)
-            selectedNucleus.ProcessStimulusDirect(inputValue);
-    }
-
-    private void CleanupReceivers() {
-        // Remove a thing-receiver connection when the nucleus is inactive
-        List<int> receiversToRemove = new();
-        foreach (KeyValuePair<int, Nucleus> item in thingReceivers) {
-            if (item.Value != null && item.Value is Neuron neuron && neuron.isSleeping)
-                receiversToRemove.Add(item.Key);            
-        }
-        foreach (int thingId in receiversToRemove) {
-            Nucleus selectedReceiver = thingReceivers[thingId];
-
-            thingReceivers.Remove(thingId);
-
-            int colonPos = selectedReceiver.name.IndexOf(":");
-            if (colonPos > 0)
-                selectedReceiver.name = selectedReceiver.name[..colonPos];
-
-        }
-    }
-}

Receptor.cs

@@ -1,94 +0,0 @@
-using UnityEngine;
-#if UNITY_MATHEMATICS
-using Unity.Mathematics;
-using static Unity.Mathematics.math;
-#endif
-
-[System.Serializable]
-public class Receptor : Neuron, IReceptor {
-    public Receptor(Cluster parent, string name) : base(parent, name) {
-        this.array = new NucleusArray(this);
-        if (this.name.IndexOf(":") < 0)
-            this.name += ": 0";
-    }
-    public Receptor(ClusterPrefab prefab, string name) : base(prefab, name) {
-        this.array = new NucleusArray(this);
-    }
-
-    public string GetName() {
-        return this.name;
-    }
-
-    public override Nucleus ShallowCloneTo(Cluster parent) {
-        Receptor clone = new(parent, name) {
-
-        };
-        CloneFields(clone);
-        return clone;
-    }
-    public override Nucleus Clone(ClusterPrefab prefab) {
-        Receptor clone = new(prefab, name) {
-            array = this._array
-        };
-        CloneFields(clone);
-        // Adding receivers will also add synapses to the receivers
-        foreach (Nucleus receiver in this.receivers.ToArray())
-            clone.AddReceiver(receiver);
-
-        return clone;
-    }
-
-    [SerializeReference]
-    private NucleusArray _array;
-    public NucleusArray array {
-        set { _array = value; }
-    }
-
-    public Nucleus[] nucleiArray {
-        get { return _array.nuclei; }
-        set { _array.nuclei = value; }
-    }
-
-    public void AddReceptorElement(ClusterPrefab prefab) {
-        IReceptorHelpers.AddReceptorElement(this, prefab);
-    }
-
-    public void RemoveReceptorElement() {
-        IReceptorHelpers.RemoveReceptorElement(this);
-    }
-
-    public virtual void AddArrayReceiver(Nucleus receiverToAdd, float weight = 1) {
-        IReceptorHelpers.AddArrayReceiver(this, receiverToAdd, weight);
-    }
-
-    public override void UpdateStateIsolated() {
-        this.outputValue = this.bias;
-    }
-
-#if UNITY_MATHEMATICS
-
-    public override void UpdateNuclei() {
-        this.stale++;
-        if (this.stale > staleValueForSleep && lengthsq(this.bias) > 0) {
-            this.bias = new float3(0, 0, 0);
-            this.parent.UpdateFromNucleus(this);
-        }
-    }
-
-#else
-
-    public override void UpdateNuclei() {
-        this.stale++;
-        if (this.stale > staleValueForSleep && this.bias.sqrMagnitude > 0) {
-            this.bias = new Vector3(0, 0, 0);
-            this.parent.UpdateFromNucleus(this);
-        }
-    }
-
-
-#endif
-    public override void ProcessStimulus(Vector3 inputValue, int thingId = 0, string thingName = null) {
-        this._array ??= new NucleusArray(this.parent);
-        this._array.ProcessStimulus(thingId, inputValue, thingName);
-    }
-}

Runtime/Scripts.meta

@@ -1,5 +1,5 @@
 fileFormatVersion: 2
-guid: 363b69b84de0e4b729794c10e7c40ab5
+guid: cfd403fd558edec539ab9d0a1bed0c72
 folderAsset: yes
 DefaultImporter:
   externalObjects: {}

Runtime/Scripts/Cluster.cs

@@ -6,9 +6,19 @@ using Unity.Mathematics;
 using static Unity.Mathematics.math;
 #endif
 
+namespace NanoBrain {
+
+/// <summary>
+/// A Cluster combines a collection of Nuclei to implement reusable behaviour
+/// </summary>
+/// A Cluster is an instantiation of a ClusterPrefab.
+/// Clusters can be nested inside other clusters.
 [Serializable]
 public class Cluster : Nucleus {
 
+    /// <summary>
+    /// The base name of the cluster. I don't think this is actively used at this moment
+    /// </summary>
     public string baseName {
         get {
             int colonPositon = this.name.IndexOf(':');
@@ -20,6 +30,11 @@ public class Cluster : Nucleus {
 
     #region Init
 
+    /// <summary>
+    /// Instantiate a new copy of a ClusterPrefab in the given parent
+    /// </summary>
+    /// <param name="prefab">The prefab to use</param>
+    /// <param name="parent">The cluster in which this new cluster will be placed</param>
     public Cluster(ClusterPrefab prefab, Cluster parent) {
         this.prefab = prefab;
         this.name = prefab.name;
@@ -32,6 +47,11 @@ public class Cluster : Nucleus {
         this.sortedNuclei = TopologicalSort(this.clusterNuclei);
     }
 
+    /// <summary>
+    /// Add a new cluster to a ClusterPrefab
+    /// </summary>
+    /// <param name="prefab">The prefab to copy</param>
+    /// <param name="parent">The prefab in which the new copy is placed</param>
     public Cluster(ClusterPrefab prefab, ClusterPrefab parent = null) {
         this.prefab = prefab;
         this.name = prefab.name;
@@ -45,6 +65,11 @@ public class Cluster : Nucleus {
         this.sortedNuclei = TopologicalSort(this.clusterNuclei);
     }
 
+    /// <summary>
+    /// Clone a prefab.
+    /// </summary>
+    /// Strange that this does not take any parameters or return values.
+    /// Where which the clone be found???
     private void ClonePrefab() {
         Nucleus[] prefabNuclei = this.prefab.nuclei.ToArray();
         // first clone the nuclei without their connections
@@ -99,7 +124,7 @@ public class Cluster : Nucleus {
             IReceptor clonedNucleus = clonedNuclei[nucleusIx] as IReceptor;
             if (prefabReceptor == prefabReceptor.nucleiArray[0]) {
                 // We clone the array only for the first entry
-                NucleusArray clonedArray = new(prefabReceptor.nucleiArray.Length, "array");
+                NucleusArray clonedArray = new(prefabReceptor.nucleiArray.Length);
                 int arrayIx = 0;
                 foreach (Nucleus prefabArrayNucleus in prefabReceptor.nucleiArray) {
                     int arrayNucleusIx = GetNucleusIndex(prefabNuclei, prefabArrayNucleus);
@@ -129,7 +154,12 @@ public class Cluster : Nucleus {
         }
     }
 
-    // Sort the nuclei in a correct evaluation order
+    /// <summary>
+    /// Sort the nuclei in a correct evaluation order
+    /// </summary>
+    /// <param name="nodes"></param>
+    /// <returns></returns>
+    /// <exception cref="InvalidOperationException"></exception>
     private List<Nucleus> TopologicalSort(List<Nucleus> nodes) {
         Dictionary<Nucleus, int> inDegree = new();
         foreach (Nucleus node in nodes)
@@ -508,3 +538,5 @@ public class Cluster : Nucleus {
     #endregion Update
 
 }
+
+}

Runtime/Scripts/ClusterPrefab.cs

@@ -0,0 +1,140 @@
+using System.Collections.Generic;
+using UnityEngine;
+
+namespace NanoBrain {
+
+    /// <summary>
+    /// The Unity ScriptableObject to implement re-usable Cluster Prefabs
+    /// </summary>
+    [CreateAssetMenu(menuName = "Passer/Cluster")]
+    public class ClusterPrefab : ScriptableObject {
+        /// The nuclei in this cluster
+        [SerializeReference]
+        public List<Nucleus> nuclei = new();
+
+        /// <summary>
+        /// The output of this cluster
+        /// </summary>
+        /// <deprecated>This only returens the first(default) nucleus. Use outputs[0] instead</deprecated>
+        public virtual Nucleus output => this.nuclei[0] as Nucleus;
+
+        /// <summary>
+        /// The nuclei in this cluster which are meant for receiving signals from outside the cluster
+        /// </summary>
+        /// <remark>This is currently the nuclei which do not have any incoming synapse</remark>
+        public List<Nucleus> _inputs = null;
+        public virtual List<Nucleus> inputs {
+            get {
+                if (this._inputs == null) {
+                    this._inputs = new();
+                    foreach (Nucleus receptor in this.nuclei) {
+                        if (receptor is Nucleus nucleus) {
+                            // inputs have no incoming synapses yet.
+                            if (nucleus.synapses.Count == 0)
+                                this._inputs.Add(nucleus);
+                        }
+                    }
+                }
+                return this._inputs;
+            }
+        }
+        /// <summary>
+        /// The nuclei in this cluster which are meant for sending signals onward
+        /// </summary>
+        private List<Nucleus> _outputs = null;
+        public List<Nucleus> outputs {
+            get {
+                if (this._outputs == null)
+                    RefreshOutputs();
+                return this._outputs;
+            }
+        }
+        /// <summary>
+        /// Redetermine the outpus in the cluster
+        /// </summary>
+        public void RefreshOutputs() {
+            this._outputs = new();
+            foreach (Nucleus nucleus in this.nuclei) {
+                if (nucleus is Neuron neuron && neuron.receivers.Count == 0)
+                    this._outputs.Add(nucleus);
+            }
+        }
+
+        /// <summary>
+        /// Retrieve a nucleus in this cluster
+        /// </summary>
+        /// <param name="nucleusName">The name of the nucleus</param>
+        /// <returns>The Nucleus with the given name or null if no such Nucleus could be found</returns>
+        public Nucleus GetNucleus(string nucleusName) {
+            foreach (Nucleus nucleus in this.nuclei) {
+                if (nucleus.name == nucleusName)
+                    return nucleus;
+            }
+            return null;
+        }
+
+        // Call this function to ensure that there is at least one nucleus
+        // This is an invariant and should be ensured before the nucleus is used
+        // because output requires it.
+        public void EnsureInitialization() {
+            nuclei ??= new List<Nucleus>();
+            if (nuclei.Count == 0)
+                new Neuron(this, "Output"); // Every cluster should have at least 1 neuron
+        }
+
+        public void GarbageCollection() {
+            HashSet<Nucleus> visitedNuclei = new();
+            foreach (Nucleus output in this.outputs)
+                MarkNuclei(visitedNuclei, output);
+            //Debug.Log($"Garbage collection found {visitedNuclei.Count} Nuclei");
+            this.nuclei.RemoveAll(nucleus => visitedNuclei.Contains(nucleus) == false);
+        }
+
+        public void MarkNuclei(HashSet<Nucleus> visitedNuclei, Nucleus nucleus) {
+            if (nucleus is null)
+                return;
+
+            if (nucleus.parent != null && nucleus.parent.prefab != this)
+                visitedNuclei.Add(nucleus.parent);
+            else
+                visitedNuclei.Add(nucleus);
+            if (nucleus.synapses != null) {
+                HashSet<Synapse> visitedSynapses = new();
+                foreach (Synapse synapse in nucleus.synapses) {
+                    if (synapse != null && synapse.neuron != null) {
+                        visitedSynapses.Add(synapse);
+                        if (synapse.neuron is Nucleus synapse_nucleus)
+                            MarkNuclei(visitedNuclei, synapse_nucleus);
+                    }
+                }
+                nucleus.synapses.RemoveAll(synapse => visitedSynapses.Contains(synapse) == false);
+            }
+            if (nucleus is Neuron neuron && neuron.receivers != null) {
+                HashSet<Nucleus> visitedReceivers = new();
+                foreach (Nucleus receiver in neuron.receivers) {
+                    if (receiver != null && receiver != null) {
+                        visitedReceivers.Add(receiver);
+                        visitedNuclei.Add(receiver);
+                    }
+                }
+                neuron.receivers.RemoveAll(receiver => visitedReceivers.Contains(receiver) == false);
+            }
+        }
+
+        public virtual void UpdateNuclei() {
+            foreach (Nucleus nucleus in this.nuclei)
+                nucleus.UpdateNuclei();
+        }
+
+        public int GetNucleusIndex(Nucleus receiver) {
+            int ix = 0;
+            foreach (Nucleus nucleus in this.nuclei) {
+                if (receiver == nucleus)
+                    return ix;
+                ix++;
+            }
+            return -1;
+        }
+    }
+
+}

Runtime/Scripts/ClusterPrefab.cs.meta

@@ -0,0 +1,11 @@
+fileFormatVersion: 2
+guid: 60a957541c24c57e78018c202ebb1d9b
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {fileID: 2800000, guid: 288088fdc016525a59f83f1c608e514d, type: 3}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Runtime/Scripts/ClusterReceptor.cs

@@ -7,6 +7,8 @@ using static Unity.Mathematics.math;
 #endif
 using System.Linq;
 
+namespace NanoBrain {
+
 [Serializable]
 public class ClusterReceptor : Cluster, IReceptor {
     public ClusterReceptor(ClusterPrefab prefab, Cluster parent, string name) : base(prefab, parent) {
@@ -271,3 +273,5 @@ public class ClusterReceptor : Cluster, IReceptor {
         }
     }
 }
+
+}

Runtime/Scripts/IReceptor.cs

@@ -0,0 +1,123 @@
+using UnityEngine;
+
+namespace NanoBrain {
+
+    /// <summary>
+    /// A Receptor is a Nucleus which can receive input (called Stimulus) from outside the the cluster/brain
+    /// </summary>
+    /// It has the ability to distinguish stimuli from different things using an array of Nuclei
+    public interface IReceptor {
+        /// <summary>
+        /// Get the name of the receptor
+        /// </summary>
+        /// <returns>The name of the receptor</returns>
+        public string GetName();
+
+        /// <summary>
+        /// The array of nuclei used to track multiple things sending stimuli
+        /// </summary>
+        /// The size of the array determines the maximum number of things which can be distinguished
+        public Nucleus[] nucleiArray { get; set; }
+
+        /// <summary>
+        /// Extends the nucleiArray with an additional element
+        /// </summary>
+        /// <param name="prefab">A prefab of the nucleus to add?</param>
+        public void AddReceptorElement(ClusterPrefab prefab);
+        /// <summary>
+        /// Removes the last element from the nucleiArray
+        /// </summary>
+        public void RemoveReceptorElement();
+
+        /// <summary>
+        /// Add a receiver for this receptor array
+        /// </summary>
+        /// <param name="receiverToAdd">The receiving Nucleus</param>
+        /// <param name="weight">The initial weight to use for the synapses</param>
+        /// This function will add a synapse to the receiver for each element in the nucleiArray.
+        public void AddArrayReceiver(Nucleus receiverToAdd, float weight = 1);
+
+        /// <summary>
+        /// Process an external stimulus
+        /// </summary>
+        /// <param name="inputValue">The value of the stimulus</param>
+        /// <param name="thingId">The id of the thing causing the stimulus</param>
+        /// <param name="thingName">The name of the thing causing the stimulus</param>
+        public void ProcessStimulus(Vector3 inputValue, int thingId = 0, string thingName = null);
+    }
+
+    public static class IReceptorHelpers {
+
+        /// <summary>
+        /// Implementation for the NanoBrain::IReceptor::AddReceptorElement which can be used for all implementations of IReceptor
+        /// </summary>
+        /// <param name="receptor">The IReceptor which needs to extend its nucleiArray</param>
+        /// <param name="prefab">A prefab of the nucleus to add?</param>
+        public static void AddReceptorElement(IReceptor receptor, ClusterPrefab prefab) {
+            if (receptor.nucleiArray.Length == 0) {
+                Debug.LogError("Empty perceptoid array, cannot add");
+            }
+            int newLength = receptor.nucleiArray.Length + 1;
+            Nucleus[] newArray = new Nucleus[newLength];
+
+            string baseName = receptor.GetName();
+            int colonPos = baseName.IndexOf(":");
+            if (colonPos > 0)
+                baseName = baseName[..colonPos];
+
+            for (int i = 0; i < receptor.nucleiArray.Length; i++)
+                newArray[i] = receptor.nucleiArray[i];
+            if (receptor.nucleiArray[0] is Nucleus nucleus) {
+                newArray[newLength - 1] = nucleus.Clone(prefab);
+                newArray[newLength - 1].name = $"{baseName}: {newLength - 1}";
+            }
+
+            foreach (Nucleus element in receptor.nucleiArray) {
+                if (element is IReceptor receptorElement) {
+                    receptorElement.nucleiArray = newArray;
+                }
+            }
+        }
+
+        /// <summary>
+        /// Implementation for the NanoBrain::IReceptor::RemoteReceptorElement which can be used for all implementations of IReceptor
+        /// </summary>
+        /// <param name="receptor">The IReceptor which needs to shorten its nucleiArray</param>
+        public static void RemoveReceptorElement(IReceptor receptor) {
+            int newLength = receptor.nucleiArray.Length - 1;
+            if (newLength == 0) {
+                Debug.LogWarning("Perceptoid array cannot be empty");
+            }
+            Nucleus[] newArray = new Nucleus[newLength];
+            for (int i = 0; i < newLength; i++)
+                newArray[i] = receptor.nucleiArray[i];
+            // Delete the last perception
+            if (receptor.nucleiArray[newLength] is Nucleus nucleus)
+                Neuron.Delete(nucleus);
+
+            foreach (Nucleus element in receptor.nucleiArray) {
+                if (element is IReceptor receptorElement) {
+                    receptorElement.nucleiArray = newArray;
+                }
+            }
+
+        }
+
+        /// <summary>
+        /// Implementation for the NanoBreain::IRceptor::AddArrayReceiver which can be used for all implementations of IReceptor
+        /// </summary>
+        /// <param name="receptor">The IReceptor for which a receiving nuclues needs to be added</param>
+        /// <param name="receiverToAdd">The nucleus to receive input from the receptor</param>
+        /// <param name="weight">The initial weight for the synapses</param>
+        public static void AddArrayReceiver(IReceptor receptor, Nucleus receiverToAdd, float weight = 1) {
+            foreach (Nucleus element in receptor.nucleiArray) {
+                if (element is Cluster cluster)
+                    cluster.defaultOutput.AddReceiver(receiverToAdd, weight);
+                if (element is Neuron neuron)
+                    neuron.AddReceiver(receiverToAdd, weight);
+            }
+
+        }
+    }
+
+}

Runtime/Scripts/MemoryCell.cs

@@ -0,0 +1,73 @@
+using System;
+#if UNITY_MATHEMATICS
+using Unity.Mathematics;
+#endif
+
+namespace NanoBrain {
+
+    /// <summary>
+    /// A MemoryCell stored its value for one update
+    /// </summary>
+    /// When the input for a Memory Cell changes, it will output the previous value 
+    [Serializable]
+    public class MemoryCell : Neuron {
+
+        public MemoryCell(ClusterPrefab cluster, string name) : base(cluster, name) { }
+        public MemoryCell(Cluster parent, string name) : base(parent, name) { }
+
+        public bool staticMemory = false;
+        public override bool isSleeping {
+            get {
+                if (staticMemory)
+                    return false;
+
+                return base.isSleeping;
+            }
+        }
+
+        public override Nucleus ShallowCloneTo(Cluster newParent) {
+            MemoryCell clone = new(newParent, this.name);
+            CloneFields(clone);
+            clone.staticMemory = this.staticMemory;
+            return clone;
+        }
+
+        #region State
+
+        private bool initialized = false;
+
+#if UNITY_MATHEMATICS
+        private float3 _memorizedValue;
+#else
+    private UnityEngine.Vector3 _memorizedValue;
+#endif
+
+        public override void UpdateStateIsolated() {
+            // A memorycell does not have an activation function
+            var result = Combinator();
+
+            if (initialized)
+                // Output the previous, memorized value
+                this.outputValue = this._memorizedValue;
+            else {
+                // The first time, the result is directly set in output
+                this.outputValue = result;
+                this.initialized = true;
+            }
+
+            // Store the result for the next time
+            this._memorizedValue = result;
+        }
+
+        public override void UpdateNuclei() {
+            if (staticMemory)
+                // Static memory does not get stale or go to sleep
+                return;
+
+            base.UpdateNuclei();
+        }
+
+        #endregion State
+    }
+
+}

Runtime/Scripts/NanoBrain.cs

@@ -0,0 +1,51 @@
+using System;
+using UnityEngine;
+
+namespace NanoBrain {
+
+    /// <summary>
+    /// The NanoBrain Unity Componnent
+    /// </summary>
+    /// This implements the top-level NanoBrain Cluster 
+    public class NanoBrain : MonoBehaviour {
+        /// <summary>
+        /// The Cluster prefab from which the cluster is created
+        /// </summary>
+        public ClusterPrefab defaultBrain;
+
+        [NonSerialized]
+        private Cluster brainInstance;
+        /// <summary>
+        /// The cluster isntance
+        /// </summary>
+        public Cluster brain {
+            get {
+                if (brainInstance == null && defaultBrain != null) {
+                    brainInstance = new Cluster(defaultBrain) {
+                        name = defaultBrain.name + " (Instance)"
+                    };
+                }
+                return brainInstance;
+            }
+        }
+
+        /// <summary>
+        /// Update the weight for all Synapses coming from the Neuron with the given name
+        /// </summary>
+        /// <param name="brain">The cluster in which the synapses are updated</param>
+        /// <param name="name">The name of the Neuron for which the weights are updated</param>
+        /// <param name="weight">The new Synapse weight</param>
+        public static void UpdateWeight(Cluster brain, string name, float weight) {
+            Nucleus root = brain.defaultOutput;
+            foreach (Synapse synapse in root.synapses) {
+                if (synapse.neuron.name == name) {
+                    if (synapse.weight != weight) {
+                        synapse.weight = weight;
+                        // Debug.Log($"Updated weight for {name}");
+                    }
+                }
+            }
+        }
+    }
+
+}

Runtime/Scripts/Neuron.cs

@@ -0,0 +1,476 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+using UnityEditor;
+#if UNITY_MATHEMATICS
+using Unity.Mathematics;
+using static Unity.Mathematics.math;
+#endif
+
+namespace NanoBrain {
+
+    /// <summary>
+    /// A neuron is a basic Nucleus
+    /// </summary>
+    [Serializable]
+    public class Neuron : Nucleus {
+
+        /// <summary>
+        /// Create a new Neuron in a Cluster instance
+        /// </summary>
+        /// <param name="parent">The parent cluster in which the new Neuron should be created</param>
+        /// <param name="name">The name of the new Neuron</param>
+        public Neuron(Cluster parent, string name) {
+            this.parent = parent;
+            this.name = name;
+            this.parent?.clusterNuclei.Add(this);
+        }
+        /// <summary>
+        /// Create a new Neuron in a Cluster Prefab
+        /// </summary>
+        /// <param name="prefab">The Cluster Preafb in which the new Neuron should be created</param>
+        /// <param name="name">The name of the new Neuron</param>
+        public Neuron(ClusterPrefab prefab, string name) {
+            this.clusterPrefab = prefab;
+            this.name = name;
+            if (this.clusterPrefab != null)
+                this.clusterPrefab.nuclei.Add(this);
+            else
+                Debug.LogError("No prefab when adding neuron to prefab");
+        }
+
+        #region Serialization
+
+        /// <summary>
+        /// The type of combinators
+        /// </summary>
+        /// A combinator combines the weighted values of the synapses to a single value
+        public enum CombinatorType {
+            /// Add the weighted values together
+            Sum,
+            /// Multiply the weighted values
+            Product,
+            /// Take the maximum of all the weighted values
+            Max,
+        }
+        /// <summary>
+        /// The type of combinator used for this Neuron
+        /// </summary>
+        public CombinatorType combinator = CombinatorType.Sum;
+
+        /// <summary>
+        /// The type of 
+        /// </summary>
+        public enum CurvePresets {
+            Linear,
+            Power,
+            Sqrt,
+            Reciprocal,
+            Custom
+        }
+        [SerializeField]
+        public CurvePresets _curvePreset;
+        public CurvePresets curvePreset {
+            get { return _curvePreset; }
+            set {
+                _curvePreset = value;
+                this.curve = GenerateCurve();
+            }
+        }
+        public AnimationCurve curve;
+        public float curveMax = 1.0f;
+
+        public AnimationCurve GenerateCurve() {
+            switch (this.curvePreset) {
+                case CurvePresets.Linear:
+                    this.curveMax = 1;
+                    return Presets.Linear(1);
+                case CurvePresets.Power:
+                    this.curveMax = 1;
+                    return Presets.Power(2.0f, 1);
+                case CurvePresets.Sqrt:
+                    this.curveMax = 1;
+                    return Presets.Power(0.5f, 1);
+                case CurvePresets.Reciprocal:
+                    this.curveMax = 1 / 0.01f * 1;
+                    return Presets.Reciprocal(1);
+                default:
+                    this.curveMax = 1;
+                    return this.curve;
+            }
+        }
+
+        public static class Presets {
+            private const int samples = 32;
+            public static AnimationCurve Linear(float weight) {
+                return AnimationCurve.Linear(0f, 0f, 1000f, weight * 1000);
+            }
+            public static AnimationCurve Power(float exponent, float weight) {
+                // build keyframes
+                Keyframe[] keys = new Keyframe[samples];
+                for (int i = 0; i < samples; i++) {
+                    float t = i / (float)(samples - 1);
+                    float v = Mathf.Pow(t, exponent) * weight;
+                    keys[i] = new Keyframe(t, v);
+                }
+
+                AnimationCurve curve = new(keys);
+
+                // set tangent modes for each key to Auto (smooth). Use Linear if you prefer straight segments.
+                for (int i = 0; i < curve.length; i++) {
+                    AnimationUtility.SetKeyLeftTangentMode(curve, i, AnimationUtility.TangentMode.Auto);
+                    AnimationUtility.SetKeyRightTangentMode(curve, i, AnimationUtility.TangentMode.Auto);
+                }
+
+                return curve;
+            }
+            public static AnimationCurve Reciprocal(float weight) {
+                int samples = 128;
+                float xMin = 0.001f;
+                float xMax = 1;
+                var keys = new Keyframe[samples];
+                for (int i = 0; i < samples; i++) {
+                    float t = i / (float)(samples - 1);
+                    float x = Mathf.Lerp(xMin, xMax, t);
+                    float y = 1f / x * weight;
+                    keys[i] = new Keyframe(x, y);
+                }
+                var curve = new AnimationCurve(keys);
+                for (int i = 0; i < curve.length; i++) {
+                    AnimationUtility.SetKeyLeftTangentMode(curve, i, AnimationUtility.TangentMode.Linear);
+                    AnimationUtility.SetKeyRightTangentMode(curve, i, AnimationUtility.TangentMode.Linear);
+                }
+                return curve;
+            }
+        }
+
+        #endregion Serialization
+
+#if UNITY_MATHEMATICS
+
+        protected float3 _outputValue;
+        public virtual float3 outputValue {
+            get { return _outputValue; }
+            set {
+                _outputValue = value;
+                if (this.isFiring)
+                    WhenFiring?.Invoke();
+            }
+        }
+        public float outputMagnitude => length(_outputValue);
+        public float outputSqrMagnitude => lengthsq(_outputValue);
+
+#else
+
+    protected Vector3 _outputValue;
+    public virtual Vector3 outputValue {
+        get { return _outputValue; }
+        set {
+            _outputValue = value;
+            if (this.isFiring)
+                WhenFiring?.Invoke();
+        }
+    }
+    public float outputMagnitude => _outputValue.magnitude;    
+    public float outputSqrMagnitude => _outputValue.sqrMagnitude;
+
+#endif
+        public bool isFiring => this.outputMagnitude > 0.5f;
+        public Action WhenFiring;
+
+
+        public virtual bool isSleeping => this.outputMagnitude == 0;
+
+        [NonSerialized]
+        public int stale = 1000;
+        public readonly int staleValueForSleep = 20;
+
+        /// \copydoc NanoBrain::Nucleus::ShallowCloneTo
+        public override Nucleus ShallowCloneTo(Cluster newParent) {
+            Neuron clone = new(newParent, this.name);
+            CloneFields(clone);
+            return clone;
+        }
+
+        /// \copydoc NanoBrain::Nucleus::Clone
+        public override Nucleus Clone(ClusterPrefab prefab) {
+            Neuron clone = new(prefab, this.name);
+            CloneFields(clone);
+            foreach (Synapse synapse in this.synapses) {
+                Synapse clonedSynapse = clone.AddSynapse(synapse.neuron);
+                clonedSynapse.weight = synapse.weight;
+            }
+            foreach (Nucleus receiver in this.receivers) {
+                clone.AddReceiver(receiver);
+            }
+            return clone;
+        }
+
+        protected virtual void CloneFields(Neuron clone) {
+            clone.clusterPrefab = this.clusterPrefab;
+            clone.bias = this.bias;
+            clone.combinator = this.combinator;
+            clone.curve = this.curve;
+            clone.curvePreset = this.curvePreset;
+            clone.curveMax = this.curveMax;
+        }
+
+        public static void Delete(Nucleus nucleus) {
+            foreach (Synapse synapse in nucleus.synapses) {
+                if (synapse.neuron is Neuron synapse_nucleus) {
+                    if (synapse_nucleus.receivers.Count > 1) {
+                        // there is another nucleus feeding into this input nucleus
+                        synapse_nucleus.receivers.RemoveAll(r => r == nucleus);
+                    }
+                    else {
+                        // No other links, delete it.
+                        Neuron.Delete(synapse_nucleus);
+                    }
+                }
+            }
+            if (nucleus is Neuron neuron) {
+                foreach (Nucleus receiver in neuron.receivers) {
+                    if (receiver != null && receiver.synapses != null)
+                        receiver.synapses.RemoveAll(s => s.neuron == nucleus);
+                }
+            }
+            else if (nucleus is Cluster cluster) {
+                // remove all receivers for this cluster
+                foreach (Neuron output in cluster.outputs) {
+                    foreach (Nucleus receiver in output.receivers) {
+                        receiver.synapses.RemoveAll(s => s.neuron == output);
+                    }
+                }
+            }
+
+
+            if (nucleus.clusterPrefab != null) {
+                nucleus.clusterPrefab.nuclei.RemoveAll(n => n == nucleus);
+                nucleus.clusterPrefab.RefreshOutputs();
+                nucleus.clusterPrefab.GarbageCollection();
+            }
+        }
+
+        public override void UpdateStateIsolated() {
+            var result = Combinator();
+            this.outputValue = Activator(result);
+        }
+
+        #region Combinator
+
+#if UNITY_MATHEMATICS
+
+        protected Func<float3> Combinator => combinator switch {
+            CombinatorType.Sum => CombinatorSum,
+            CombinatorType.Product => CombinatorProduct,
+            CombinatorType.Max => CombinatorMax,
+            _ => CombinatorSum
+        };
+
+        public float3 CombinatorSum() {
+            float3 sum = this.bias;
+            foreach (Synapse synapse in this.synapses)
+                sum += synapse.weight * synapse.neuron.outputValue;
+            return sum;
+        }
+
+        public float3 CombinatorProduct() {
+            float3 product = this.bias;
+            foreach (Synapse synapse in this.synapses) {
+                product *= synapse.weight * synapse.neuron.outputValue;
+            }
+            return product;
+        }
+
+        public float3 CombinatorMax() {
+            float3 max = this.bias;
+            float maxLength = length(max);
+
+            //Applying the weight factors
+            foreach (Synapse synapse in this.synapses) {
+                float3 input = synapse.weight * synapse.neuron.outputValue;
+
+                float inputLength = length(input);
+                if (inputLength > maxLength) {
+                    max = input;
+                    maxLength = inputLength;
+                }
+            }
+            return max;
+        }
+
+#else
+
+    protected Func<Vector3> Combinator => combinator switch {
+        CombinatorType.Sum => CombinatorSum,
+        CombinatorType.Product => CombinatorProduct,
+        CombinatorType.Max => CombinatorMax,
+        _ => CombinatorSum
+    };
+
+    public Vector3 CombinatorSum() {
+        Vector3 sum = this.bias;
+        foreach (Synapse synapse in this.synapses)
+            sum += synapse.weight * synapse.neuron.outputValue;
+        return sum;
+    }
+
+    public Vector3 CombinatorProduct() {
+        Vector3 product = this.bias;
+        foreach (Synapse synapse in this.synapses) {
+            //product *= synapse.weight * synapse.neuron.outputValue;
+            product = Vector3.Scale(product, synapse.weight * synapse.neuron.outputValue);
+        }
+        return product;
+    }
+
+    public Vector3 CombinatorMax() {
+        Vector3 max = this.bias;
+        float maxLength = max.magnitude;
+
+        //Applying the weight factors
+        foreach (Synapse synapse in this.synapses) {
+            Vector3 input = synapse.weight * synapse.neuron.outputValue;
+
+            float inputLength = input.magnitude;
+            if (inputLength > maxLength) {
+                max = input;
+                maxLength = inputLength;
+            }
+        }
+        return max;
+    }
+#endif
+        #endregion Combinator
+
+        #region Activator
+
+#if UNITY_MATHEMATICS
+
+        public Func<float3, float3> Activator => this.curvePreset switch {
+            CurvePresets.Linear => ActivatorLinear,
+            CurvePresets.Sqrt => ActivatorSqrt,
+            CurvePresets.Power => ActivatorPower,
+            CurvePresets.Reciprocal => ActivatorReciprocal,
+            _ => ActivatorCustom
+        };
+
+        protected float3 ActivatorLinear(float3 input) {
+            return input;
+        }
+
+        protected float3 ActivatorSqrt(float3 input) {
+            float3 result = normalize(input) * System.MathF.Sqrt(length(input));
+            return result;
+        }
+
+        protected float3 ActivatorPower(float3 input) {
+            float3 result = normalize(input) * System.MathF.Pow(length(input), 2);
+            return result;
+        }
+
+        protected float3 ActivatorReciprocal(float3 input) {
+            float magnitude = length(input);
+            if (magnitude == 0)
+                return new float3(0, 0, 0);
+
+            float3 result = normalize(input) * (1 / magnitude);
+            return result;
+        }
+
+        protected float3 ActivatorCustom(float3 input) {
+            float activatedValue = this.curve.Evaluate(length(input));
+            float3 result = normalize(input) * activatedValue;
+            return result;
+        }
+
+#else
+
+    public Func<Vector3, Vector3> Activator => this.curvePreset switch {
+        CurvePresets.Linear => ActivatorLinear,
+        CurvePresets.Sqrt => ActivatorSqrt,
+        CurvePresets.Power => ActivatorPower,
+        CurvePresets.Reciprocal => ActivatorReciprocal,
+        _ => ActivatorCustom
+    };
+
+    protected Vector3 ActivatorLinear(Vector3 input) {
+        return input;
+    }
+
+    protected Vector3 ActivatorSqrt(Vector3 input) {
+        Vector3 result = input.normalized * System.MathF.Sqrt(input.magnitude);
+        return result;
+    }
+
+    protected Vector3 ActivatorPower(Vector3 input) {
+        Vector3 result = input.normalized * System.MathF.Pow(input.magnitude, 2);
+        return result;
+    }
+
+    protected Vector3 ActivatorReciprocal(Vector3 input) {
+        float magnitude = input.magnitude;
+        if (magnitude == 0)
+            return new Vector3(0, 0, 0);
+
+        Vector3 result = input.normalized * (1 / magnitude);
+        return result;
+    }
+
+    protected Vector3 ActivatorCustom(Vector3 input) {
+        float activatedValue = this.curve.Evaluate(input.magnitude);
+        Vector3 result = input.normalized * activatedValue;
+        return result;
+    }
+
+#endif
+
+        #endregion Activator
+
+        #region Receivers
+
+        [SerializeReference]
+        private List<Nucleus> _receivers = new();
+        public virtual List<Nucleus> receivers {
+            get { return _receivers; }
+            set { _receivers = value; }
+        }
+
+        public virtual void AddReceiver(Nucleus receiverToAdd, float weight = 1) {
+            this._receivers.Add(receiverToAdd);
+            receiverToAdd.AddSynapse(this, weight);
+        }
+
+        public virtual void RemoveReceiver(Nucleus receiverToRemove) {
+            if (this is IReceptor receptor) {
+                foreach (Nucleus element in receptor.nucleiArray) {
+                    if (element is Neuron neuron) {
+                        neuron._receivers.RemoveAll(receiver => receiver == receiverToRemove);
+                        receiverToRemove.synapses.RemoveAll(synapse => synapse.neuron == neuron);
+                    }
+                }
+            }
+            else {
+                this._receivers.RemoveAll(receiver => receiver == receiverToRemove);
+                receiverToRemove.synapses.RemoveAll(synapse => synapse.neuron == this);
+            }
+        }
+
+
+        #endregion Receivers
+
+        public override void ProcessStimulus(Vector3 inputValue, int thingId = 0, string thingName = null) {
+            if (this.parent is ClusterReceptor clusterReceptor)
+                clusterReceptor.ProcessStimulus(this, inputValue, thingId, thingName);
+            else
+                ProcessStimulusDirect(inputValue, thingId, thingName);
+        }
+
+        public void ProcessStimulusDirect(Vector3 inputValue, int thingId = 0, string thingName = null) {
+            this.stale = 0;
+            this.bias = inputValue;
+            this.parent.UpdateFromNucleus(this);
+        }
+    }
+
+}

Runtime/Scripts/Nucleus.cs

@@ -0,0 +1,147 @@
+using System;
+using System.Collections.Generic;
+using UnityEngine;
+
+/// <summary>
+/// The Nanobrain namespace
+/// </summary>
+namespace NanoBrain {
+
+/// <summary>
+/// A Nucleus is a basic element in a brain cluster
+/// </summary>
+[Serializable]
+public abstract class Nucleus {
+    /// <summary>
+    /// The name of the Nucleus
+    /// </summary>
+    public string name;
+
+    /// <summary>
+    /// The cluster prefab in which the nucleus is located
+    /// </summary>
+    [SerializeReference]
+    public ClusterPrefab clusterPrefab;
+    /// <summary>
+    /// The cluster instance in which the nucleus is located
+    /// </summary>
+    [SerializeReference]
+    public Cluster parent;
+
+    /// <summary>
+    /// Toggle for printing debugging trace data
+    /// </summary>
+    public bool trace = false;
+
+    /// <summary>
+    /// Function to make a partial clone of this nucleus
+    /// </summary>
+    /// <param name="parent">The cluster in which the cloned nucleus should be placed</param>
+    /// <returns></returns>
+    public abstract Nucleus ShallowCloneTo(Cluster parent);
+    /// <summary>
+    /// Function to clone a nucleus to a Cluster prefab
+    /// </summary>
+    /// <param name="prefab"></param>
+    /// <returns></returns>
+    public abstract Nucleus Clone(ClusterPrefab prefab);
+
+    /// <summary>
+    /// The types of Nucleus
+    /// </summary>
+    public enum Type {
+        None,
+        Neuron,
+        MemoryCell,
+        Cluster,
+        Receptor,
+        ClusterReceptor,
+    }
+
+    #region Synapses
+
+    /// <summary>
+    /// The bias of the nucleus
+    /// </summary>
+    /// The bias which a value which is always added to the combined value of the nucleus
+    /// It does not have a synapse and therefore no weight of source nucleus
+    public Vector3 bias = Vector3.zero;
+
+    [SerializeField]
+    private List<Synapse> _synapses = new();
+    /// <summary>
+    /// The synapses of the nucleus
+    /// </summary>
+    public List<Synapse> synapses => _synapses;
+
+    /// <summary>
+    /// Add a new synapse to this nuclues
+    /// </summary>
+    /// <param name="sendingNucleus">The nucleus from which the signals may originate</param>
+    /// <param name="weight">The weight applied to the input. Default value = 1</param>
+    /// <returns>The created Synapse</returns>
+    /// This will add a new input to this nucleus with the given weight.
+    public Synapse AddSynapse(Neuron sendingNucleus, float weight = 1) {
+        Synapse synapse = new(sendingNucleus, weight);
+        this.synapses.Add(synapse);
+        return synapse;
+    }
+
+    /// <summary>
+    /// Find a synapse
+    /// </summary>
+    /// <param name="sender">The sender of the input to the Synapse</param>
+    /// <returns>The found Synapse or null when the sender has no synapse to this nucleus.</returns>
+    public Synapse GetSynapse(Nucleus sender) {
+        foreach (Synapse synapse in this.synapses)
+            if (synapse.neuron == sender)
+                return synapse;
+        return null;
+    }
+
+    /// <summary>
+    /// Remove a synapse from a Nucleus
+    /// </summary>
+    /// <param name="sendingNucleus">Remote the synapse connecting to this Nucleus</param>
+    public void RemoveSynapse(Nucleus sendingNucleus) {
+        this.synapses.RemoveAll(synapse => synapse.neuron == sendingNucleus);
+    }
+
+    #endregion Synapses
+
+    #region Update
+
+    /// <summary>
+    /// Update the state without updating other Nuclei
+    /// </summary>
+    public abstract void UpdateStateIsolated();
+
+    /// <summary>
+    /// Update the state and recursively all Nuclei receiving data from this Nucleus
+    /// </summary>
+    public virtual void UpdateNuclei() {
+    }
+
+    /// <summary>
+    /// Set the bias, recalculate the output and update all Nuclei receiving from this Nucleus
+    /// </summary>
+    /// <param name="inputValue"></param>
+    public virtual void SetBias(Vector3 inputValue) {
+        this.bias = inputValue;
+        this.parent.UpdateFromNucleus(this);
+    }
+
+    /// <summary>
+    /// Process an external stimulus
+    /// </summary>
+    /// <param name="inputValue">The value of the stimulus</param>
+    /// <param name="thingId">The id of the thing causing the stimulus</param>
+    /// <param name="thingName">The name of the thing causing the stimulus</param>
+    public virtual void ProcessStimulus(Vector3 inputValue, int thingId = 0, string thingName = "") {
+    }
+
+    #endregion Update
+
+}
+
+}

Runtime/Scripts/NucleusArray.cs

@@ -0,0 +1,197 @@
+using System.Linq;
+using System.Collections.Generic;
+using UnityEngine;
+#if UNITY_MATHEMATICS
+using Unity.Mathematics;
+using static Unity.Mathematics.math;
+#endif
+
+namespace NanoBrain {
+
+    /// <summary>
+    /// Class to manage an array of nuclei for an IReceptor
+    /// </summary>
+    /// Would love to get rid of this class.
+    [System.Serializable]
+    public class NucleusArray {
+        /// <summary>
+        /// The nuclei in this array
+        /// </summary>
+        [SerializeReference]
+        private Nucleus[] _nuclei;
+        public Nucleus[] nuclei {
+            get {
+                return _nuclei;
+            }
+            set {
+                _nuclei = value;
+            }
+        }
+
+        /// <summary>
+        /// Create a new NucleusArray with the given nucleus
+        /// </summary>
+        /// <param name="nucleus">The Nucleus to put in the NucleusArray</param>
+        /// This results in an nucleus array of size 1
+        public NucleusArray(Nucleus nucleus) {
+            this._nuclei = new Nucleus[1];
+            this._nuclei[0] = nucleus;
+        }
+        /// <summary>
+        /// Create a new NucleusArray of the given size
+        /// </summary>
+        /// <param name="size">The size of the nucluesArray</param>
+        public NucleusArray(int size) {
+            this._nuclei = new Nucleus[size];
+        }
+
+
+        // public void AddNucleus(ClusterPrefab prefab) {
+        //     if (this._nuclei.Length == 0) {
+        //         Debug.LogError("Empty perceptoid array, cannot add");
+        //         return;
+        //     }
+        //     int newLength = this._nuclei.Length + 1;
+        //     Nucleus[] newArray = new Nucleus[newLength];
+
+        //     for (int i = 0; i < this._nuclei.Length; i++)
+        //         newArray[i] = this._nuclei[i];
+        //     if (this._nuclei[0] is Nucleus nucleus) {
+        //         newArray[newLength - 1] = nucleus.Clone(prefab);
+        //         newArray[newLength - 1].name += $": {newLength - 1}";
+        //     }
+
+        //     this._nuclei = newArray;
+        // }
+
+        // public void RemoveNucleus() {
+        //     int newLength = this._nuclei.Length - 1;
+        //     if (newLength == 0) {
+        //         Debug.LogWarning("Perceptoid array cannot be empty");
+        //         return;
+        //     }
+        //     Nucleus[] newPerceptei = new Nucleus[newLength];
+        //     for (int i = 0; i < newLength; i++)
+        //         newPerceptei[i] = this._nuclei[i];
+        //     // Delete the last perception
+        //     if (this._nuclei[newLength] is Nucleus nucleus)
+        //         Neuron.Delete(nucleus); //this._nuclei[newLength]);
+
+        //     this._nuclei = newPerceptei;
+        // }
+
+        public Dictionary<int, Nucleus> thingReceivers = new();
+
+#if UNITY_MATHEMATICS
+
+        private Nucleus FindReceiver(int thingId, float3 inputValue) {
+            float inputMagnitude = length(inputValue);
+            return FindReceiverMagnitude(thingId, inputMagnitude);
+        }
+
+#else
+
+        private Nucleus FindReceiver(int thingId, Vector3 inputValue) {
+            float inputMagnitude = inputValue.magnitude;
+            return FindReceiverMagnitude(thingId, inputMagnitude);
+        }
+
+#endif
+
+        private Nucleus FindReceiverMagnitude(int thingId, float inputMagnitude) {
+            Neuron selectedReceiver = null;
+            float selectedMagnitude = 0;
+            foreach (Nucleus nucleusReceiver in this._nuclei) {
+                if (nucleusReceiver is not Neuron receiver)
+                    continue;
+                if (thingReceivers.ContainsValue(receiver) == false) {
+                    // We found an unusued receiver
+                    thingReceivers.Add(thingId, receiver);
+                    return receiver;
+                }
+                else if (receiver.isSleeping) {
+                    // A sleeping receiver is not active and can therefore always be used
+                    thingReceivers.Add(thingId, receiver);
+                    return receiver;
+                }
+                else if (selectedReceiver == null) {
+                    // If we haven't found a receiver yet, just start by taking the first
+                    selectedReceiver = receiver;
+                    selectedMagnitude = selectedReceiver.outputMagnitude;
+                }
+                // Look for the receiver with the lowest magnitude
+                else {
+                    float magnitude = receiver.outputMagnitude;
+
+                    if (magnitude < inputMagnitude && receiver.outputMagnitude < selectedMagnitude) {
+                        selectedReceiver = receiver;
+                        selectedMagnitude = selectedReceiver.outputMagnitude;
+                    }
+                }
+            }
+            if (selectedReceiver != null) {
+                // Replace the receiver
+                // Find the thingId current associated with the receiver
+                int keyToRemove = thingReceivers.FirstOrDefault(r => r.Value.Equals(selectedReceiver)).Key;
+                if (keyToRemove != 0 || thingReceivers.ContainsKey(keyToRemove))
+                    thingReceivers.Remove(keyToRemove);
+                // And add the new association
+                thingReceivers.Add(thingId, selectedReceiver);
+            }
+            return selectedReceiver;
+        }
+
+        /// <summary>
+        /// Process an external stimulus
+        /// </summary>
+        /// <param name="inputValue">The value of the stimulus</param>
+        /// <param name="thingId">The id of the thing causing the stimulus</param>
+        /// <param name="thingName">The name of the thing causing the stimulus</param>
+        public virtual void ProcessStimulus(int thingId, Vector3 inputValue, string thingName = null) {
+            CleanupReceivers();
+
+            if (this._nuclei[0] is Neuron neuron)
+                inputValue = neuron.Activator(inputValue);
+
+            if (!thingReceivers.TryGetValue(thingId, out Nucleus selectedReceiver)) {
+                // No existing nucleus for this thing
+                selectedReceiver = FindReceiver(thingId, inputValue);
+            }
+            if (selectedReceiver == null)
+                return;
+
+            if (thingName != null) {
+                string baseName = selectedReceiver.name;
+                int colonPos = selectedReceiver.name.IndexOf(":");
+                if (colonPos > 0)
+                    baseName = selectedReceiver.name[..colonPos];
+                selectedReceiver.name = baseName + ": " + thingName;
+            }
+
+            if (selectedReceiver is Neuron selectedNucleus)
+                selectedNucleus.ProcessStimulusDirect(inputValue);
+        }
+
+        /// <summary>
+        /// Remove a thing-receiver connection when the nucleus is inactive
+        /// </summary>
+        private void CleanupReceivers() {
+            List<int> receiversToRemove = new();
+            foreach (KeyValuePair<int, Nucleus> item in thingReceivers) {
+                if (item.Value != null && item.Value is Neuron neuron && neuron.isSleeping)
+                    receiversToRemove.Add(item.Key);
+            }
+            foreach (int thingId in receiversToRemove) {
+                Nucleus selectedReceiver = thingReceivers[thingId];
+
+                thingReceivers.Remove(thingId);
+
+                int colonPos = selectedReceiver.name.IndexOf(":");
+                if (colonPos > 0)
+                    selectedReceiver.name = selectedReceiver.name[..colonPos];
+
+            }
+        }
+    }
+
+}

Runtime/Scripts/Receptor.cs

@@ -0,0 +1,113 @@
+using UnityEngine;
+#if UNITY_MATHEMATICS
+using Unity.Mathematics;
+using static Unity.Mathematics.math;
+#endif
+
+namespace NanoBrain {
+
+    /// <summary>
+    /// Basic IReceptor to receive external input
+    /// </summary>
+    [System.Serializable]
+    public class Receptor : Neuron, IReceptor {
+        /// <summary>
+        /// Create a new Receptor in a Cluster instance
+        /// </summary>
+        /// <param name="parent">The Cluster in which the Receptor is created</param>
+        /// <param name="name">The name of the new Receptor</param>
+        public Receptor(Cluster parent, string name) : base(parent, name) {
+            this.array = new NucleusArray(this);
+            if (this.name.IndexOf(":") < 0)
+                this.name += ": 0";
+        }
+        /// <summary>
+        /// Create a new Receptor in a Cluster Prefab
+        /// </summary>
+        /// <param name="prefab">The Cluster Prefab in which the Receptor is created</param>
+        /// <param name="name">The name of the new Receptor</param>
+        public Receptor(ClusterPrefab prefab, string name) : base(prefab, name) {
+            this.array = new NucleusArray(this);
+        }
+        
+        public string GetName() {
+            return this.name;
+        }
+
+        /// \copydoc NanoBrain::Neuron::ShallowCloneTo
+        public override Nucleus ShallowCloneTo(Cluster parent) {
+            Receptor clone = new(parent, name) {
+
+            };
+            CloneFields(clone);
+            return clone;
+        }
+        /// \copydoc NanoBrain::Neuron::Clone
+        public override Nucleus Clone(ClusterPrefab prefab) {
+            Receptor clone = new(prefab, name) {
+                array = this._array
+            };
+            CloneFields(clone);
+            // Adding receivers will also add synapses to the receivers
+            foreach (Nucleus receiver in this.receivers.ToArray())
+                clone.AddReceiver(receiver);
+
+            return clone;
+        }
+
+        [SerializeReference]
+        private NucleusArray _array;
+        public NucleusArray array {
+            set { _array = value; }
+        }
+
+        public Nucleus[] nucleiArray {
+            get { return _array.nuclei; }
+            set { _array.nuclei = value; }
+        }
+
+        public void AddReceptorElement(ClusterPrefab prefab) {
+            IReceptorHelpers.AddReceptorElement(this, prefab);
+        }
+
+        public void RemoveReceptorElement() {
+            IReceptorHelpers.RemoveReceptorElement(this);
+        }
+
+        public virtual void AddArrayReceiver(Nucleus receiverToAdd, float weight = 1) {
+            IReceptorHelpers.AddArrayReceiver(this, receiverToAdd, weight);
+        }
+
+        public override void UpdateStateIsolated() {
+            this.outputValue = this.bias;
+        }
+
+#if UNITY_MATHEMATICS
+
+        public override void UpdateNuclei() {
+            this.stale++;
+            if (this.stale > staleValueForSleep && lengthsq(this.bias) > 0) {
+                this.bias = new float3(0, 0, 0);
+                this.parent.UpdateFromNucleus(this);
+            }
+        }
+
+#else
+
+    public override void UpdateNuclei() {
+        this.stale++;
+        if (this.stale > staleValueForSleep && this.bias.sqrMagnitude > 0) {
+            this.bias = new Vector3(0, 0, 0);
+            this.parent.UpdateFromNucleus(this);
+        }
+    }
+
+
+#endif
+        public override void ProcessStimulus(Vector3 inputValue, int thingId = 0, string thingName = null) {
+            this._array ??= new NucleusArray(this.parent);
+            this._array.ProcessStimulus(thingId, inputValue, thingName);
+        }
+    }
+
+}

Runtime/Scripts/Synapse.cs

@@ -0,0 +1,33 @@
+using System;
+using UnityEngine;
+
+namespace NanoBrain {
+
+    /// <summary>
+    /// A Synapse connects the ouput of a Neuron to another Neuron
+    /// </summary>
+    [Serializable]
+    public class Synapse {
+        /// <summary>
+        /// The neuron from which input is received
+        /// </summary>
+        [SerializeReference]
+        public Neuron neuron;
+
+        /// <summary>
+        /// The weight value to apply to the Neuron input
+        /// </summary>
+        public float weight;
+
+        /// <summary>
+        /// Create a new Synapse
+        /// </summary>
+        /// <param name="nucleus">The neuron from which input is received</param>
+        /// <param name="weight">The weight value to apply to the Neuron input</param>
+        public Synapse(Neuron nucleus, float weight = 1.0f) {
+            this.neuron = nucleus;
+            this.weight = weight;
+        }
+    }
+
+}

Runtime/Vector.cs

@@ -1,8 +0,0 @@
-using UnityEngine;
-
-#if UNITY_MATHEMATICS
-using Unity.Mathematics;
-using static Unity.Mathematics.math;
-//#endif
-
-#endif

Runtime/Vector.cs.meta

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Samples.meta

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Samples/Clusters.meta

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-[Serializable]
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