CLASS(Part,f0_part,p)
GROUP(Geometry)
GROUP(Other properties)
GROUP(Visual)
PROP(x,0,1024,position.x,f,,,,p.x)
PROP(y,0,1024,position.y,f,,,,p.y)
PROP(z,0,1024,position.z,f,,,,p.z)
XPROP(m,1,0,mass,f,0.1,999.0,1.0,mass)
PROP(s,1,0,size,f,0.1,10.0,1.0,size)
XPROP(dn,1,0,density,f,0.2,5.0,1.0,density)
XPROP(fr,1,0,friction,f,0.0,4.0,0.4,friction)
XPROP(ing,1,0,ingestion,f,0.0,1.0,0.25,ingest)
XPROP(as,1,0,assimilation,f,0.0,1.0,0.25,assim)
PROP(rx,0,0,rot.x,f,,,,rot.x)
PROP(ry,0,1024,rot.y,f,,,,rot.y)
PROP(rz,0,1024,rot.z,f,,,,rot.z)
PROP(i,1,0,`info',s,,,,info)
PROP(Vstyle,2,0,vis_style,s,0,-1,part,vis_style)
ENDCLASS

CLASS(Joint,f0_joint,j)
GROUP(Connections)
GROUP(Geometry)
GROUP(Other properties)
GROUP(Visual)
PROP(p1,0,1024,`part1 ref#',d,-1,999999,-1,p1_refno)
PROP(p2,0,1024,`part2 ref#',d,-1,999999,-1,p2_refno)
PROP(rx,1,0,rotation.x,f,,,,rot.x)
PROP(ry,1,1024,rotation.y,f,,,,rot.y)
PROP(rz,1,1024,rotation.z,f,,,,rot.z)
PROP(dx,1,0,delta.x,f,-2,2,0,d.x)
PROP(dy,1,1024,delta.y,f,-2,2,0,d.y)
PROP(dz,1,1024,delta.z,f,-2,2,0,d.z)
XPROP(stif,2,0,stiffness,f,0.0,1.0,1.0,stif)
XPROP(rotstif,2,0,rotation stiffness,f,0.0,1.0,1.0,rotstif)
PROP(stam,2,0,stamina,f,0.0,1.0,0.25,stamina)
PROP(i,2,0,`info',s,,,,info)
PROP(Vstyle,3,0,vis_style,s,0,-1,joint,vis_style)
ENDCLASS

CLASS(Joint,f0_nodeltajoint,j,NOXML)
GROUP(Connections)
GROUP(Geometry)
GROUP(Other properties)
GROUP(Visual)
PROP(p1,0,1024,`part1 ref#',d,-1,999999,-1,p1_refno)
PROP(p2,0,1024,`part2 ref#',d,-1,999999,-1,p2_refno)
XPROP(stif,2,0,stiffness,f,0.0,1.0,1.0,stif)
XPROP(rotstif,2,0,rotation stiffness,f,0.0,1.0,1.0,rotstif)
PROP(stam,2,0,stamina,f,0.0,1.0,0.25,stamina)
PROP(i,2,0,`info',s,,,,info)
PROP(Vstyle,3,0,vis_style,s,0,-1,joint,vis_style)
ENDCLASS

CLASS(Neuro,f0_neuro,n)
GROUP(Connections)
GROUP(Other)
GROUP(Visual)
PROP(p,0,0,`part ref#',d,-1,999999,-1,part_refno)
PROP(j,0,0,`joint ref#',d,-1,999999,-1,joint_refno)
PROP(d,1,0,item details,s,,,,details,GETSET)
PROP(i,1,0,`info',s,,,,info)
PROP(Vstyle,2,0,vis_style,s,0,-1,neuro,vis_style)
PROP(getInputCount,0,1+2,`input count',d,,,,inputCount,GETONLY)
PROP(getInputNeuroDef,0,1+2,`get input neuron',p oNeuroDef(d),,,,p_getInputNeuroDef,PROCEDURE)
PROP(getInputNeuroIndex,0,1+2,`get input neuron index',p d(d),,,,p_getInputNeuroIndex,PROCEDURE)
PROP(getInputWeight,0,1+2,`get input weight',p f(d),,,,p_getInputWeight,PROCEDURE)
PROP(classObject,0,1+2,`neuron class',o NeuroClass,,,,classObject,GETONLY)
ENDCLASS

CLASS(NeuroConn,f0_neuroconn,c)
GROUP(Connection)
GROUP(Other)
PROP(n1,0,1024,`this neuro ref#',d,-1,999999,-1,n1_refno)
PROP(n2,0,1024,`connected neuro ref#',d,-1,999999,-1,n2_refno)
PROP(w,0,1024,weight,f,-999999,999999,1.0,weight)
PROP(i,1,0,`info',s,,,,info)
ENDCLASS

NEUROCLASS(StdNeuron,N,Neuron,`Standard neuron',-1,1,0)
VISUALHINTS(DontShowClass)
NEUROPROP(in,1,0,Inertia,f,0.0,1.0,0.8,inertia)
NEUROPROP(fo,1,0,Force,f,0.0,999.0,0.04,force)
NEUROPROP(si,1,0,Sigmoid,f,-99999.0,99999.0,2.0,sigmo)
NEUROPROP(s,2,0,State,f,-1.0,1.0,0.0,newstate)
ENDNEUROCLASS

NEUROCLASS(StdUNeuron,Nu,`Unipolar neuron [EXPERIMENTAL!]',`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)
NEUROPROP(in,1,0,Inertia,f,0.0,1.0,0.8,inertia)
NEUROPROP(fo,1,0,Force,f,0.0,999.0,0.04,force)
NEUROPROP(si,1,0,Sigmoid,f,-99999.0,99999.0,2.0,sigmo)
NEUROPROP(s,2,0,State,f,-1.0,1.0,0.0,newstate)
ENDNEUROCLASS

NEUROCLASS(Gyro,G,Gyroscope,`Equilibrium sensor.\n0=the stick is horizontal\n+1/-1=the stick is vertical',0,1,2)
VISUALHINTS(ReceptorClass)
SYMBOL(`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')
ENDNEUROCLASS

NEUROCLASS(Touch,T,Touch,`Touch sensor.\n-1=no contact\n0=just touching\n>0=pressing, value depends on the force applied',0,1,1)
VISUALHINTS(ReceptorClass)
SYMBOL(`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')
NEUROPROP(r,1,0,Range,f,0.0,1.0,1.0,range)
ENDNEUROCLASS

NEUROCLASS(Smell,S,Smell,`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)
VISUALHINTS(ReceptorClass)
SYMBOL(`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')
ENDNEUROCLASS

NEUROCLASS(Const,*,Constant,Constant value,0,1,0)
VISUALHINTS(Invisible)
SYMBOL(`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')
ENDNEUROCLASS

NEUROCLASS(BendMuscle,|,Bend muscle,,1,0,2)
VISUALHINTS(DontShowClass+EffectorClass+V1BendMuscle+AtFirstPart)
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')
NEUROPROP(p,0,0,power,f,0.01,1.0,0.25,power)
NEUROPROP(r,0,0,bending range,f,0.0,1.0,1.0,bendrange)
ENDNEUROCLASS

NEUROCLASS(RotMuscle,@,Rotation muscle,,1,0,2)
VISUALHINTS(DontShowClass+EffectorClass+V1RotMuscle+AtFirstPart)
SYMBOL(`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')
NEUROPROP(p,0,0,power,f,0.01,1.0,1.0,power)
ENDNEUROCLASS

NEUROCLASS(Diff,D,Differentiate,Calculate the difference between the current and previous input value. Multiple inputs are aggregated with respect to their weights,-1,1,0)
SYMBOL(`3,3,25,0,25,100,75,50,25,0,1,75,50,100,50,3,44,42,51,57,36,57,44,42')
ENDNEUROCLASS

NEUROCLASS(FuzzyNeuro,Fuzzy,Fuzzy system [EXPERIMENTAL!],Refer to publications to learn more about this neuron.,-1,1,0)
SYMBOL(`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')
NEUROPROP(ns,0,0,number of fuzzy sets,d,1,,,fuzzySetsNr)
NEUROPROP(nr,0,0,number of rules,d,1,,,rulesNr)
NEUROPROP(fs,0,0,fuzzy sets,s,0,-1,0,fuzzySetString)
NEUROPROP(fr,0,0,fuzzy rules,s,0,-1,0,fuzzyRulesString)
ENDNEUROCLASS

NEUROCLASS(Sticky,Sti,Sticky [EXPERIMENTAL!],,1,0,1)
VISUALHINTS(EffectorClass)
ENDNEUROCLASS

NEUROCLASS(LinearMuscle,LMu,Linear muscle [EXPERIMENTAL!],,1,0,2)
VISUALHINTS(EffectorClass)
NEUROPROP(p,0,0,power,f,0.01,1.0,1.0,power)
ENDNEUROCLASS

NEUROCLASS(WaterDetect,Water,Water detector,`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)
VISUALHINTS(ReceptorClass)
ENDNEUROCLASS

NEUROCLASS(Energy,Energy,Energy level,`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)
VISUALHINTS(ReceptorClass)
ENDNEUROCLASS

NEUROCLASS(Channelize,Ch,Channelize,`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)
SYMBOL(`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')
ENDNEUROCLASS

NEUROCLASS(ChMux,ChMux,Channel multiplexer,`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)
SYMBOL(`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')
ENDNEUROCLASS

NEUROCLASS(ChSel,ChSel,Channel selector,`Outputs a single channel (selected by the \"ch\" parameter) from multichannel input',1,1,0)
SYMBOL(`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')
NEUROPROP(ch,0,0,channel,d,,,,ch)
ENDNEUROCLASS

NEUROCLASS(Random,Rnd,Random noise,`Generates random noise (subsequent random values in the range of -1..+1)',0,1,0)
ENDNEUROCLASS

NEUROCLASS(Sinus,Sin,Sinus generator,`Output frequency = f0+input',1,1,0)
SYMBOL(`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')
NEUROPROP(f0,0,0,base frequency,f,-1.0,1.0,0.06283185307,f0)
NEUROPROP(t,0,0,time,f,0,6.283185307,0,t)
ENDNEUROCLASS