Index: /cpp/frams/_demos/genoconv_test.cpp
===================================================================
--- /cpp/frams/_demos/genoconv_test.cpp	(revision 974)
+++ /cpp/frams/_demos/genoconv_test.cpp	(revision 975)
@@ -107,4 +107,48 @@
 }
 
+/// Sample Geno converter producing "f0s" genotype from the textual list of shapes, eg. "/*shapes*/ball,cube,cube"
+class GenoConv_Test4 : public GenoConverter
+{
+public:
+	GenoConv_Test4()
+	{
+		name = "Test Converter #4";
+		in_format = "shapes";
+		out_format = "0s";
+		mapsupport = 1;
+	}
+	SString convert(SString &in, MultiMap *map, bool using_checkpoints);
+	~GenoConv_Test4() {}
+};
+
+SString GenoConv_Test4::convert(SString &in, MultiMap *map, bool using_checkpoints)
+{
+	SString result;
+	int numparts=0;
+	int input_pos=0, prev_input_pos=0; SString word;
+	while(in.getNextToken(input_pos,word,','))
+		{
+		Part::Shape sh;
+		if (word=="cube")
+			sh = Part::SHAPE_CUBOID;
+		else if (word=="ball")
+			sh = Part::SHAPE_ELLIPSOID;
+		else if (word=="cylinder")
+			sh = Part::SHAPE_CYLINDER;
+		else
+			return "";
+		SString add;
+		add+=SString::sprintf("p:x=%g,sh=%d\n",numparts*2.0f,sh);
+		if (numparts>0)
+			add+=SString::sprintf("j:p1=%d,p2=%d,sh=1\n",numparts-1,numparts);
+		if (map) // fill in the map only if requested
+			map->add(prev_input_pos, prev_input_pos+word.length()-1, result.length(),result.length()+add.length()-1);
+		result+=add;
+		numparts++;
+		prev_input_pos = input_pos;
+		}
+	return result;
+}
+
 
 ///////////////////////////////////////////////
@@ -118,5 +162,5 @@
 static int goodWidthForFormat(const SString& genotype_format)
 {
-	return genotype_format == "0" ? 45 : 15; // more space for long f0 lines
+	return Geno::formatIsOneOf(genotype_format, Geno::F0_FORMAT_LIST) ? 45 : 15; // more space for long f0 lines
 }
 
@@ -134,4 +178,5 @@
 	gcm.addConverter(new GenoConv_Test2());
 	gcm.addConverter(new GenoConv_Test3());
+	gcm.addConverter(new GenoConv_Test4());
 	Geno::useConverters(&gcm);
 
@@ -154,5 +199,5 @@
 	else
 		src = "X";
-	SString dst = (argc > 2) ? *argv[2] : '0';
+	SString dst = (argc > 2) ? argv[2] : Geno::F0_FORMAT_LIST;
 	bool using_checkpoints = (argc > 3) ? (strcmp(argv[3], "checkpoints") == 0) : false;
 
@@ -162,5 +207,5 @@
 	MultiMap m;
 	Geno g2 = g1.getConverted(dst, &m, using_checkpoints);
-	printf("*** Converted to f%s:\n", dst.c_str());
+	printf("*** Converted:\n");
 	printGen(g2);
 
Index: /cpp/frams/config/f0-SDK.def
===================================================================
--- /cpp/frams/config/f0-SDK.def	(revision 974)
+++ /cpp/frams/config/f0-SDK.def	(revision 975)
@@ -202,4 +202,5 @@
 NEUROCLASS(SolidMuscle,M,Muscle for solids,,1,0,2)
 SHAPETYPE(SolidsShapeType)
+JOINTTYPE(SUPPORTED_JOINT_HINGE_X+SUPPORTED_JOINT_HINGE_XY)
 VISUALHINTS(EffectorClass+AtFirstPart+SolidMuscleFlag)
 SYMBOL(`6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21')
Index: /cpp/frams/config/f0.def
===================================================================
--- /cpp/frams/config/f0.def	(revision 974)
+++ /cpp/frams/config/f0.def	(revision 975)
@@ -202,4 +202,5 @@
 NEUROCLASS(SolidMuscle,M,Muscle for solids,,1,0,2)
 SHAPETYPE(SolidsShapeType)
+JOINTTYPE(SUPPORTED_JOINT_HINGE_X+SUPPORTED_JOINT_HINGE_XY)
 VISUALHINTS(EffectorClass+AtFirstPart+SolidMuscleFlag)
 SYMBOL(`6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21')
Index: /cpp/frams/model/model.cpp
===================================================================
--- /cpp/frams/model/model.cpp	(revision 974)
+++ /cpp/frams/model/model.cpp	(revision 975)
@@ -415,5 +415,5 @@
 	{
 		*f0map = *map;
-		if (geno.getFormat() != '0')
+		if (!Geno::formatIsOneOf(geno.getFormat(), Geno::F0_FORMAT_LIST))
 		{
 			MultiMap tmp;
Index: /cpp/frams/model/modelparts.cpp
===================================================================
--- /cpp/frams/model/modelparts.cpp	(revision 974)
+++ /cpp/frams/model/modelparts.cpp	(revision 975)
@@ -99,5 +99,5 @@
 NeuroClass::NeuroClass(ParamEntry *_props, SString _description,
 	int _prefinputs, int _prefoutput, int _preflocation,
-	int *_vectordata, bool own_vd, int vhints, int sup_shapes)
+	int *_vectordata, bool own_vd, int vhints, int sup_shapes, int sup_joints)
 	:ownedvectordata(own_vd),
 	name(_props->name), longname(_props->id), description(_description),
Index: /cpp/frams/model/modelparts.h
===================================================================
--- /cpp/frams/model/modelparts.h	(revision 974)
+++ /cpp/frams/model/modelparts.h	(revision 975)
@@ -227,4 +227,12 @@
 	static constexpr int SUPPORTED_SHAPE_ALL = SUPPORTED_SHAPE_BALL_AND_STICK | SUPPORTED_SHAPE_SOLIDS;
 	paInt supported_shape_types; //< bitfield of 'Model::shape' values: NeuroClass::SUPPORTED_SHAPE_xxx = 1 << Model::SHAPE_xxx
+
+	static constexpr int SUPPORTED_JOINT_BALL_AND_STICK = 1;
+	static constexpr int SUPPORTED_JOINT_FIXED = 2;
+	static constexpr int SUPPORTED_JOINT_HINGE_X = 4;
+	static constexpr int SUPPORTED_JOINT_HINGE_XY = 8;
+	static constexpr int SUPPORTED_JOINT_ALL = SUPPORTED_JOINT_BALL_AND_STICK + SUPPORTED_JOINT_FIXED + SUPPORTED_JOINT_HINGE_X + SUPPORTED_JOINT_HINGE_XY;
+	paInt supported_joint_types; //< bitfield of 'Joint::shape' values: NeuroClass::SUPPORTED_JOINT_xxx = 1 << JOINT::SHAPE_xxx
+
 	int *vectordata;
 	paInt visualhints;
@@ -240,5 +248,5 @@
 	NeuroClass();
 	NeuroClass(ParamEntry *_props, SString _description,
-		int _prefinputs, int _prefoutput, int _preflocation, int *_vectordata, bool own_vd = 1, int vhints = 0, int sup_shapes = NeuroClass::SUPPORTED_SHAPE_ALL);
+		   int _prefinputs, int _prefoutput, int _preflocation, int *_vectordata, bool own_vd = 1, int vhints = 0, int sup_shapes = NeuroClass::SUPPORTED_SHAPE_ALL, int sup_joints = NeuroClass::SUPPORTED_JOINT_ALL);
 	/** class name for use in Neuro::setClassName(), Neuro::setDetails() (former 'moredata' field),
 		eg. "N","-",G" */
@@ -278,4 +286,6 @@
 	int getSupportedShapeTypes() { return (int)supported_shape_types; }
 	bool isShapeTypeSupported(ModelEnum::ShapeType t) { return (1 << (int)t) & supported_shape_types; }
+	int getSupportedJointTypes() { return (int)supported_joint_types; }
+	bool isJointTypeSupported(Joint::Shape t) { return (1 << (int)t) & supported_joint_types; }
 	int *getSymbolGlyph()
 	{
Index: /cpp/frams/neuro/neurocls-f0-SDK-library.h
===================================================================
--- /cpp/frams/neuro/neurocls-f0-SDK-library.h	(revision 974)
+++ /cpp/frams/neuro/neurocls-f0-SDK-library.h	(revision 975)
@@ -13,5 +13,5 @@
 
 
-      
+       
 static ParamEntry NI_StdNeuron_tab[]={
 {"Neuron",1, 4 ,"N",},
@@ -23,7 +23,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_StdNeuron_tab,"Standard neuron",-1,1,0, NULL,false, 2, 3));
-
-      
+addClass(new NeuroClass(NI_StdNeuron_tab,"Standard neuron",-1,1,0, NULL,false, 2, 3, 15));
+
+       
 static ParamEntry NI_StdUNeuron_tab[]={
 {"Unipolar neuron [EXPERIMENTAL!]",1, 4 ,"Nu",},
@@ -34,7 +34,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_StdUNeuron_tab,"Works like standard neuron (N) but the output value is scaled to 0...+1 instead of -1...+1.\nHaving 0 as one of the saturation states should help in \"gate circuits\", where input signal is passed through or blocked depending on the other singal.",-1,1,0, NULL,false, 0, 3));
-
-      static int Gyro_xy[]={83,8,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,12,43,24,48,24,48,19,38,19,38,24,43,24,43,54,48,54,48,64,43,69,38,64,38,54,43,54,5,63,69,58,74,48,79,38,79,28,74,23,69,1,43,79,43,74,1,23,69,26,66,1,63,69,60,66,1,55,76,53,73,1,31,75,33,72};   
+addClass(new NeuroClass(NI_StdUNeuron_tab,"Works like standard neuron (N) but the output value is scaled to 0...+1 instead of -1...+1.\nHaving 0 as one of the saturation states should help in \"gate circuits\", where input signal is passed through or blocked depending on the other singal.",-1,1,0, NULL,false, 0, 3, 15));
+
+       static int Gyro_xy[]={83,8,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,12,43,24,48,24,48,19,38,19,38,24,43,24,43,54,48,54,48,64,43,69,38,64,38,54,43,54,5,63,69,58,74,48,79,38,79,28,74,23,69,1,43,79,43,74,1,23,69,26,66,1,63,69,60,66,1,55,76,53,73,1,31,75,33,72};   
 static ParamEntry NI_Gyro_tab[]={
 {"Gyroscope",1, 0 ,"G",},
@@ -44,7 +44,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Gyro_tab,"Tilt sensor.\nSignal is proportional to sin(angle) = most sensitive in horizontal orientation.\n0=the stick is horizontal\n+1/-1=the stick is vertical",0,1,2, Gyro_xy,false, 32, 1));
-
-      static int GyroP_xy[]={83,8,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,12,43,24,48,24,48,19,38,19,38,24,43,24,43,54,48,54,48,64,43,69,38,64,38,54,43,54,5,63,69,58,74,48,79,38,79,28,74,23,69,1,43,79,43,74,1,23,69,26,66,1,63,69,60,66,1,55,76,53,73,1,31,75,33,72};   
+addClass(new NeuroClass(NI_Gyro_tab,"Tilt sensor.\nSignal is proportional to sin(angle) = most sensitive in horizontal orientation.\n0=the stick is horizontal\n+1/-1=the stick is vertical",0,1,2, Gyro_xy,false, 32, 1, 15));
+
+       static int GyroP_xy[]={83,8,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,12,43,24,48,24,48,19,38,19,38,24,43,24,43,54,48,54,48,64,43,69,38,64,38,54,43,54,5,63,69,58,74,48,79,38,79,28,74,23,69,1,43,79,43,74,1,23,69,26,66,1,63,69,60,66,1,55,76,53,73,1,31,75,33,72};   
 static ParamEntry NI_GyroP_tab[]={
 {"Part Gyroscope",1, 2 ,"Gp",},
@@ -55,7 +55,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_GyroP_tab,"Tilt sensor. Signal is directly proportional to the tilt angle.\n0=the part X axis is horizontal\n+1/-1=the axis is vertical",0,1,1, GyroP_xy,false, 32, 3));
-
-      static int Touch_xy[]={43,2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50};   
+addClass(new NeuroClass(NI_GyroP_tab,"Tilt sensor. Signal is directly proportional to the tilt angle.\n0=the part X axis is horizontal\n+1/-1=the axis is vertical",0,1,1, GyroP_xy,false, 32, 3, 15));
+
+       static int Touch_xy[]={43,2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50};   
 static ParamEntry NI_Touch_tab[]={
 {"Touch",1, 3 ,"T",},
@@ -67,7 +67,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Touch_tab,"Touch and proximity sensor (Tc+Tp combined)\n-1=no contact\n0=just touching\n>0=pressing, value depends on the force applied (not implemented in ODE mode)",0,1,1, Touch_xy,false, 32, 3));
-
-      static int TouchC_xy[]={43,2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50};   
+addClass(new NeuroClass(NI_Touch_tab,"Touch and proximity sensor (Tc+Tp combined)\n-1=no contact\n0=just touching\n>0=pressing, value depends on the force applied (not implemented in ODE mode)",0,1,1, Touch_xy,false, 32, 3, 15));
+
+       static int TouchC_xy[]={43,2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50};   
 static ParamEntry NI_TouchC_tab[]={
 {"Touch contact",1, 0 ,"Tc",},
@@ -76,7 +76,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_TouchC_tab,"Touch sensor.\n-1=no contact\n0=the Part is touching the obstacle\n>0=pressing, value depends on the force applied (not implemented in ODE mode)",0,1,1, TouchC_xy,false, 32, 3));
-
-      static int TouchP_xy[]={43,2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50};   
+addClass(new NeuroClass(NI_TouchC_tab,"Touch sensor.\n-1=no contact\n0=the Part is touching the obstacle\n>0=pressing, value depends on the force applied (not implemented in ODE mode)",0,1,1, TouchC_xy,false, 32, 3, 15));
+
+       static int TouchP_xy[]={43,2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50};   
 static ParamEntry NI_TouchP_tab[]={
 {"Touch proximity",1, 3 ,"Tp",},
@@ -88,7 +88,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_TouchP_tab,"Proximity sensor detecting obstacles along the X axis.\n-1=distance is \r\' or more\n0=zero distance'",0,1,1, TouchP_xy,false, 32, 3));
-
-      static int Smell_xy[]={64,5,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,3,10,40,15,45,15,55,10,60,5,20,30,25,35,30,45,30,55,25,65,20,70,4,15,35,20,40,22,50,20,60,15,65,5,75,50,50,50,45,45,40,50,45,55,50,50};   
+addClass(new NeuroClass(NI_TouchP_tab,"Proximity sensor detecting obstacles along the X axis.\n-1=distance is \r\' or more\n0=zero distance'",0,1,1, TouchP_xy,false, 32, 3, 15));
+
+       static int Smell_xy[]={64,5,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,3,10,40,15,45,15,55,10,60,5,20,30,25,35,30,45,30,55,25,65,20,70,4,15,35,20,40,22,50,20,60,15,65,5,75,50,50,50,45,45,40,50,45,55,50,50};   
 static ParamEntry NI_Smell_tab[]={
 {"Smell",1, 0 ,"S",},
@@ -97,7 +97,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Smell_tab,"Smell sensor. Aggregated \"smell of energy\" experienced from all energy objects (creatures and food pieces).\nClose objects have bigger influence than the distant ones: for each energy source, its partial feeling is proportional to its energy/(distance^2)",0,1,1, Smell_xy,false, 32, 3));
-
-      static int Const_xy[]={29,4,4,26,27,26,73,73,73,73,27,26,27,1,73,50,100,50,1,56,68,46,68,2,41,47,51,32,51,68};   
+addClass(new NeuroClass(NI_Smell_tab,"Smell sensor. Aggregated \"smell of energy\" experienced from all energy objects (creatures and food pieces).\nClose objects have bigger influence than the distant ones: for each energy source, its partial feeling is proportional to its energy/(distance^2)",0,1,1, Smell_xy,false, 32, 3, 15));
+
+       static int Const_xy[]={29,4,4,26,27,26,73,73,73,73,27,26,27,1,73,50,100,50,1,56,68,46,68,2,41,47,51,32,51,68};   
 static ParamEntry NI_Const_tab[]={
 {"Constant",1, 0 ,"*",},
@@ -106,7 +106,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Const_tab,"Constant value",0,1,0, Const_xy,false, 1, 3));
-
-      static int BendMuscle_xy[]={63,6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21};   
+addClass(new NeuroClass(NI_Const_tab,"Constant value",0,1,0, Const_xy,false, 1, 3, 15));
+
+       static int BendMuscle_xy[]={63,6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21};   
 static ParamEntry NI_BendMuscle_tab[]={
 {"Bend muscle",1, 2 ,"|",},
@@ -118,7 +118,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_BendMuscle_tab,"",1,0,2, BendMuscle_xy,false, 2+16+64+4, 1));
-
-      static int RotMuscle_xy[]={62,5,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,1,69,10,77,17,10,59,15,57,17,57,22,60,26,69,27,78,26,82,21,82,16,79,12,69,10,80,6,3,65,50,65,20,75,20,75,50};   
+addClass(new NeuroClass(NI_BendMuscle_tab,"",1,0,2, BendMuscle_xy,false, 2+16+64+4, 1, 15));
+
+       static int RotMuscle_xy[]={62,5,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,1,69,10,77,17,10,59,15,57,17,57,22,60,26,69,27,78,26,82,21,82,16,79,12,69,10,80,6,3,65,50,65,20,75,20,75,50};   
 static ParamEntry NI_RotMuscle_tab[]={
 {"Rotation muscle",1, 1 ,"@",},
@@ -129,7 +129,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_RotMuscle_tab,"",1,0,2, RotMuscle_xy,false, 2+16+128+4, 1));
-
-      static int SolidMuscle_xy[]={63,6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21};   
+addClass(new NeuroClass(NI_RotMuscle_tab,"",1,0,2, RotMuscle_xy,false, 2+16+128+4, 1, 15));
+
+       static int SolidMuscle_xy[]={63,6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21};   
 static ParamEntry NI_SolidMuscle_tab[]={
 {"Muscle for solids",1, 2 ,"M",},
@@ -137,11 +137,12 @@
 
 
+
 {"p",0,0,"power","f 0.01 1.0 1.0",},
 {"a",0,0,"axis","d 0 1 0",},
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_SolidMuscle_tab,"",1,0,2, SolidMuscle_xy,false, 16+4+512, 2));
-
-      static int Diff_xy[]={24,3,3,25,0,25,100,75,50,25,0,1,75,50,100,50,3,44,42,51,57,36,57,44,42};   
+addClass(new NeuroClass(NI_SolidMuscle_tab,"",1,0,2, SolidMuscle_xy,false, 16+4+512, 2, 4+8));
+
+       static int Diff_xy[]={24,3,3,25,0,25,100,75,50,25,0,1,75,50,100,50,3,44,42,51,57,36,57,44,42};   
 static ParamEntry NI_Diff_tab[]={
 {"Differentiate",1, 0 ,"D",},
@@ -149,7 +150,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Diff_tab,"Calculate the difference between the current and previous input value. Multiple inputs are aggregated with respect to their weights",-1,1,0, Diff_xy,false, 0, 3));
-
-      static int FuzzyNeuro_xy[]={44,5,2,30,65,37,37,44,65,3,37,65,44,37,51,37,58,65,2,51,65,58,37,65,65,6,100,50,70,50,70,25,25,10,25,90,70,75,70,50,1,70,65,25,65};   
+addClass(new NeuroClass(NI_Diff_tab,"Calculate the difference between the current and previous input value. Multiple inputs are aggregated with respect to their weights",-1,1,0, Diff_xy,false, 0, 3, 15));
+
+       static int FuzzyNeuro_xy[]={44,5,2,30,65,37,37,44,65,3,37,65,44,37,51,37,58,65,2,51,65,58,37,65,65,6,100,50,70,50,70,25,25,10,25,90,70,75,70,50,1,70,65,25,65};   
 static ParamEntry NI_FuzzyNeuro_tab[]={
 {"Fuzzy system [EXPERIMENTAL!]",1, 4 ,"Fuzzy",},
@@ -161,7 +162,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_FuzzyNeuro_tab,"Refer to publications to learn more about this neuron.",-1,1,0, FuzzyNeuro_xy,false, 0, 3));
-
-      
+addClass(new NeuroClass(NI_FuzzyNeuro_tab,"Refer to publications to learn more about this neuron.",-1,1,0, FuzzyNeuro_xy,false, 0, 3, 15));
+
+       
 static ParamEntry NI_Sticky_tab[]={
 {"Sticky [EXPERIMENTAL!]",1, 0 ,"Sti",},
@@ -170,7 +171,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Sticky_tab,"",1,0,1, NULL,false, 16, 1));
-
-      
+addClass(new NeuroClass(NI_Sticky_tab,"",1,0,1, NULL,false, 16, 1, 15));
+
+       
 static ParamEntry NI_LinearMuscle_tab[]={
 {"Linear muscle [EXPERIMENTAL!]",1, 1 ,"LMu",},
@@ -180,7 +181,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_LinearMuscle_tab,"",1,0,2, NULL,false, 16+256, 1));
-
-      
+addClass(new NeuroClass(NI_LinearMuscle_tab,"",1,0,2, NULL,false, 16+256, 1, 15));
+
+       
 static ParamEntry NI_WaterDetect_tab[]={
 {"Water detector",1, 0 ,"Water",},
@@ -188,7 +189,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_WaterDetect_tab,"Output signal:\n0=on or above water surface\n1=under water (deeper than 1)\n0..1=in the transient area just below water surface",0,1,1, NULL,false, 32, 3));
-
-      
+addClass(new NeuroClass(NI_WaterDetect_tab,"Output signal:\n0=on or above water surface\n1=under water (deeper than 1)\n0..1=in the transient area just below water surface",0,1,1, NULL,false, 32, 3, 15));
+
+       
 static ParamEntry NI_Energy_tab[]={
 {"Energy level",1, 0 ,"Energy",},
@@ -196,7 +197,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Energy_tab,"The current energy level divided by the initial energy level.\nUsually falls from initial 1.0 down to 0.0 and then the creature dies. It can rise above 1.0 if enough food is ingested",0,1,0, NULL,false, 32, 3));
-
-      static int Channelize_xy[]={57,10,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,1,30,80,55,50,1,30,20,55,50,1,30,35,55,50,1,30,45,55,50,1,30,55,55,50,1,61,53,65,47,1,30,65,55,50};   
+addClass(new NeuroClass(NI_Energy_tab,"The current energy level divided by the initial energy level.\nUsually falls from initial 1.0 down to 0.0 and then the creature dies. It can rise above 1.0 if enough food is ingested",0,1,0, NULL,false, 32, 3, 15));
+
+       static int Channelize_xy[]={57,10,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,1,30,80,55,50,1,30,20,55,50,1,30,35,55,50,1,30,45,55,50,1,30,55,55,50,1,61,53,65,47,1,30,65,55,50};   
 static ParamEntry NI_Channelize_tab[]={
 {"Channelize",1, 0 ,"Ch",},
@@ -204,7 +205,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Channelize_tab,"Combines all input signals into a single multichannel output; Note: ChSel and ChMux are the only neurons which support multiple channels. Other neurons discard everything except the first channel.",-1,1,0, Channelize_xy,false, 0, 3));
-
-      static int ChMux_xy[]={52,7,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,3,30,67,45,67,45,50,50,50,1,35,70,39,64,2,30,33,53,33,53,48};   
+addClass(new NeuroClass(NI_Channelize_tab,"Combines all input signals into a single multichannel output; Note: ChSel and ChMux are the only neurons which support multiple channels. Other neurons discard everything except the first channel.",-1,1,0, Channelize_xy,false, 0, 3, 15));
+
+       static int ChMux_xy[]={52,7,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,3,30,67,45,67,45,50,50,50,1,35,70,39,64,2,30,33,53,33,53,48};   
 static ParamEntry NI_ChMux_tab[]={
 {"Channel multiplexer",1, 0 ,"ChMux",},
@@ -212,7 +213,7 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_ChMux_tab,"Outputs the selected channel from the second (multichannel) input. The first input is used as the selector value (-1=select first channel, .., 1=last channel)",2,1,0, ChMux_xy,false, 0, 3));
-
-      static int ChSel_xy[]={41,6,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,1,30,50,50,50,1,35,53,39,47};   
+addClass(new NeuroClass(NI_ChMux_tab,"Outputs the selected channel from the second (multichannel) input. The first input is used as the selector value (-1=select first channel, .., 1=last channel)",2,1,0, ChMux_xy,false, 0, 3, 15));
+
+       static int ChSel_xy[]={41,6,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,1,30,50,50,50,1,35,53,39,47};   
 static ParamEntry NI_ChSel_tab[]={
 {"Channel selector",1, 1 ,"ChSel",},
@@ -221,14 +222,14 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_ChSel_tab,"Outputs a single channel (selected by the \"ch\" parameter) from multichannel input",1,1,0, ChSel_xy,false, 0, 3));
-
-      
+addClass(new NeuroClass(NI_ChSel_tab,"Outputs a single channel (selected by the \"ch\" parameter) from multichannel input",1,1,0, ChSel_xy,false, 0, 3, 15));
+
+       
 static ParamEntry NI_Random_tab[]={
 {"Random noise",1, 0 ,"Rnd",},
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Random_tab,"Generates random noise (subsequent random values in the range of -1..+1)",0,1,0, NULL,false, 0, 3));
-
-      static int Sinus_xy[]={46,3,12,75,50,71,37,62,28,50,25,37,28,28,37,25,50,28,62,37,71,50,75,62,71,71,62,75,50,1,75,50,100,50,5,35,50,40,35,45,35,55,65,60,65,65,50};   
+addClass(new NeuroClass(NI_Random_tab,"Generates random noise (subsequent random values in the range of -1..+1)",0,1,0, NULL,false, 0, 3, 15));
+
+       static int Sinus_xy[]={46,3,12,75,50,71,37,62,28,50,25,37,28,28,37,25,50,28,62,37,71,50,75,62,71,71,62,75,50,1,75,50,100,50,5,35,50,40,35,45,35,55,65,60,65,65,50};   
 static ParamEntry NI_Sinus_tab[]={
 {"Sinus generator",1, 2 ,"Sin",},
@@ -238,3 +239,3 @@
  
 {0,0,0,},};
-addClass(new NeuroClass(NI_Sinus_tab,"Output frequency = f0+input",1,1,0, Sinus_xy,false, 0, 3));
+addClass(new NeuroClass(NI_Sinus_tab,"Output frequency = f0+input",1,1,0, Sinus_xy,false, 0, 3, 15));
