mostly building on gpu now for basic act funcs -- still need atomics

axon/act.goal

@@ -132,14 +132,15 @@ func (sk *SpikeParams) ActFromISI(isi, timeInc, integ float32) float32 {
 
 // AvgFromISI returns updated spiking ISI from current isi interval value
 func (sk *SpikeParams) AvgFromISI(avg float32, isi float32) float32 {
-	if avg <= 0 {
-		avg = isi
-	} else if isi < 0.8*avg {
-		avg = isi // if significantly less than we take that
+	av := avg
+	if av <= 0 {
+		av = isi
+	} else if isi < 0.8*av {
+		av = isi // if significantly less than we take that
 	} else { // integrate on slower
-		avg += sk.ISIDt * (isi - avg) // running avg updt
+		av += sk.ISIDt * (isi - av) // running avg updt
 	}
-	return avg
+	return av
 }
 
 ////////  DendParams
@@ -580,8 +581,7 @@ func (sm *SMaintParams) ExpInt(isi float32) float32 {
 	if isi <= 0 {
 		return 0
 	}
-	isi = max(isi, sm.ISI.Min)
-	return math32.FastExp(-isi / sm.NNeurons)
+	return math32.FastExp(-max(isi, sm.ISI.Min) / sm.NNeurons)
 }
 
 ////////  PopCodeParams
@@ -1218,7 +1218,7 @@ func (ac *ActParams) AddGiNoise(ctx *Context, ni, di uint32) {
 func (ac *ActParams) GiFromSyn(ctx *Context, ni, di uint32, giSyn float32) float32 {
 	ac.AddGiNoise(ctx, ni, di)
 	if giSyn < 0 { // negative inhib G doesn't make any sense
-		giSyn = 0
+		return 0
 	}
 	return giSyn
 }
@@ -1242,11 +1242,11 @@ func (ac *ActParams) VmFromInet(vm, dt, inet float32) float32 {
 // VmInteg integrates Vm over VmSteps to obtain a more stable value
 // Returns the new Vm and inet values.
 func (ac *ActParams) VmInteg(vm, dt, ge, gl, gi, gk float32, nvm, inet *float32) {
-	dt *= ac.Dt.DtStep
+	dtEff := dt * ac.Dt.DtStep
 	*nvm = vm
 	for i := int32(0); i < ac.Dt.VmSteps; i++ {
 		*inet = ac.InetFromG(*nvm, ge, gl, gi, gk)
-		*nvm = ac.VmFromInet(*nvm, dt, *inet)
+		*nvm = ac.VmFromInet(*nvm, dtEff, *inet)
 	}
 }
 

axon/layerparams.goal

@@ -78,11 +78,11 @@ func SetNeuronExtPosNeg(ctx *Context, ni, di uint32, val float32) {
 		if val >= 0 {
 			Neurons[Ext, ni, di] = val
 		} else {
-			Neurons[Ext, ni, di] = 0
+			Neurons[Ext, ni, di] = float32(0)
 		}
 	} else {
 		if val >= 0 {
-			Neurons[Ext, ni, di] = 0
+			Neurons[Ext, ni, di] = float32(0)
 		} else {
 			Neurons[Ext, ni, di] = -val
 		}
@@ -593,7 +593,7 @@ func (ly *LayerParams) SpecialPreGs(ctx *Context, ni, di uint32, pl *Pool, drvGe
 
 	case BLALayer:
 		if ly.Learn.NeuroMod.IsBLAExt() {
-			md := max(-GlobalScalars[GvDA, di], 0) // ext is modulated by negative da
+			md := max(-GlobalScalars[GvDA, di], float32(0)) // ext is modulated by negative da
 			geCtxt := md * ly.CT.GeGain * Neurons[CtxtGeOrig, ni, di]
 			Neurons[GeRaw, ni, di] += geCtxt
 			ctxExt := ly.Acts.Dt.GeSynFromRawSteady(geCtxt)
@@ -671,6 +671,7 @@ func (ly *LayerParams) SpecialPreGs(ctx *Context, ni, di uint32, pl *Pool, drvGe
 	case TDIntegLayer:
 		NrnSetFlag(ni, di, NeuronHasExt)
 		SetNeuronExtPosNeg(ctx, ni, di, GlobalScalars[GvRewPred, di])
+	default:
 	}
 	return saveVal
 }
@@ -688,6 +689,7 @@ func (ly *LayerParams) SpecialPostGs(ctx *Context, ni, di uint32, saveVal float3
 		if orig < 0.05 {
 			Neurons[Ge, ni, di] = 0
 		}
+	default:
 	}
 }
 
@@ -959,6 +961,7 @@ func (ly *LayerParams) PostSpikeSpecial(ctx *Context, ni, di uint32, pl *Pool, l
 	case TDDaLayer:
 		// I set this in CyclePost
 		Neurons[Act, ni, di] = GlobalScalars[GvDA, di]
+	default:
 	}
 }
 
@@ -1013,6 +1016,7 @@ func (ly *LayerParams) CyclePost(ctx *Context, di uint32) {
 		ly.CyclePostTDIntegLayer(ctx, di)
 	case TDDaLayer:
 		ly.CyclePostTDDaLayer(ctx, di)
+	default:
 	}
 }
 

axon/learn.goal

@@ -326,13 +326,11 @@ func (ln *LearnNeurParams) InitNeurCa(ctx *Context, ni, di uint32) {
 // that drive learning -- can be the same as activation but also can be different
 // for testing learning Ca effects independent of activation effects.
 func (ln *LearnNeurParams) LrnNMDAFromRaw(ctx *Context, ni, di uint32, geTot float32) {
-	if geTot < 0 {
-		geTot = 0
-	}
+	geEff := max(geTot, 0)
 	vmd := Neurons[VmDend, ni, di]
-	Neurons[GnmdaLrn, ni, di] = ln.LrnNMDA.NMDASyn(Neurons[GnmdaLrn, ni, di], geTot)
+	Neurons[GnmdaLrn, ni, di] = ln.LrnNMDA.NMDASyn(Neurons[GnmdaLrn, ni, di], geEff)
 	gnmda := ln.LrnNMDA.Gnmda(Neurons[GnmdaLrn, ni, di], vmd)
-	Neurons[NmdaCa, ni, di] = gnmda * ln.LrnNMDA.CaFromV(vmd)
+	Neurons[NmdaCa, ni, di] = float32(gnmda * ln.LrnNMDA.CaFromV(vmd))
 }
 
 // CaFromSpike updates all spike-driven calcium variables, including CaLrn and CaSpk.

axon/rubicon.goal

@@ -1195,7 +1195,9 @@ func GlobalSetRew(ctx *Context, di uint32, rew float32, hasRew bool) {
 	}
 }
 
-// RubiconUSStimVal returns stimulus value for US at given index
+//gosl:start
+
+// RubiconUSStimValue returns stimulus value for US at given index
 // and valence (includes Cost).  If US > 0.01, a full 1 US activation is returned.
 func RubiconUSStimValue(ctx *Context, di uint32, usIndex uint32, valence ValenceTypes) float32 {
 	nix := GetNetworkIxs(0)
@@ -1218,3 +1220,5 @@ func RubiconUSStimValue(ctx *Context, di uint32, usIndex uint32, valence Valence
 	return us
 }
 
+//gosl:end
+