re-introduce CaSyn and use it for SpikeBins instead of raw spikes, pl…

…us a lot of cleanup and renaming of learn params: initial commit without actually using CaSyn.

axon/act-layer.goal

@@ -539,31 +539,11 @@ func (ly *LayerParams) SpikeFromG(ctx *Context, lpi, ni, di uint32) {
 			Neurons[ni, di, CaPMax] = spkmax
 		}
 	}
-	spk := Neurons[ni, di, Spike]
 	mx := NetworkIxs[0].NSpikeBins
 	bin := min(ctx.Cycle / ctx.SpikeBinCycles, mx)
-	// if ly.Learn.GateSync.On.IsTrue() && ly.Learn.GateSync.GateLayIndex >= 0 { 
-	// 	rt := int32(LayerStates[ly.Learn.GateSync.GateLayIndex, di, GatedRT])
-	// 	if rt > 0 {
-	// 		gcyc := rt + ly.Learn.GateSync.Offset
-	// 		if ctx.Cycle >= gcyc {
-	// 			minus := ctx.ThetaCycles - ctx.PlusCycles
-	// 			minusBins := minus / spksper
-	// 			plusBins := ctx.PlusCycles / spksper
-	// 			delta := (gcyc - minus) / spksper
-	// 			if gcyc == ctx.Cycle && delta != 0 {
-	// 				ly.Learn.GateSync.ShiftBins(delta, minusBins, plusBins, ni, di)
-	// 			}
-	// 			bin = min((ctx.Cycle / spksper) - delta, 7)
-	// 			// if ly.Index == 13 && ni == ly.Indexes.NeurSt {
-	// 			// 	fmt.Println(ctx.Cycle, rt, gcyc, delta, bin)
-	// 			// }
-	// 		}
-	// 	}
-	// 	Neurons[ni, di, SpikeBin0 + NeuronVars(bin)] += spk
-	// } else {
-	Neurons[ni, di, SpikeBins + NeuronVars(bin)] += spk
-	// }
+	Neurons[ni, di, SpikeBins + NeuronVars(bin)] += Neurons[ni, di, Spike]
+// caSyn := Neurons[ni, di, CaSyn]
+//  	Neurons[ni, di, SpikeBins + NeuronVars(bin)] += caSyn / float32(ctx.SpikeBinCycles)
 }
 
 // SendSpike sends spike to receivers for all neurons that spiked

axon/basic_test.goal

@@ -93,7 +93,7 @@ var pathParams = PathSheets{
 	"SubMean": {
 		{Sel: "Path", Doc: "submean used in some models but not by default",
 			Set: func(pt *PathParams) {
-				pt.Learn.Trace.SubMean =  1
+				pt.Learn.DWt.SubMean =  1
 			}},
 	},
 }

axon/layer.goal

@@ -402,7 +402,6 @@ func (ly *Layer) PostBuild() {
 	ly.Params.LayInhib.Index2 = ly.BuildConfigFindLayer("LayInhib2Name", false) // optional
 	ly.Params.LayInhib.Index3 = ly.BuildConfigFindLayer("LayInhib3Name", false) // optional
 	ly.Params.LayInhib.Index4 = ly.BuildConfigFindLayer("LayInhib4Name", false) // optional
-	ly.Params.Learn.GateSync.GateLayIndex = ly.BuildConfigFindLayer("GateLayName", false) // optional
 
 	switch ly.Type {
 	case PulvinarLayer:

axon/learn-layer.goal

@@ -239,7 +239,7 @@ func (ly *Layer) LRateSched(sched float32) {
 
 // SetSubMean sets the SubMean parameters in all the layers in the network
 // trgAvg is for Learn.TrgAvgAct.SubMean
-// path is for the paths Learn.Trace.SubMean
+// path is for the paths Learn.DWt.SubMean
 // in both cases, it is generally best to have both parameters set to 0
 // at the start of learning
 func (ly *Layer) SetSubMean(trgAvg, path float32) {
@@ -248,7 +248,7 @@ func (ly *Layer) SetSubMean(trgAvg, path float32) {
 		// if pj.Off { // keep all sync'd
 		// 	continue
 		// }
-		pj.Params.Learn.Trace.SubMean = path
+		pj.Params.Learn.DWt.SubMean = path
 	}
 }
 

axon/learn-net.goal

@@ -85,7 +85,7 @@ func (nt *Network) LRateSched(sched float32) {
 
 // SetSubMean sets the SubMean parameters in all the layers in the network
 // trgAvg is for Learn.TrgAvgAct.SubMean
-// path is for the paths Learn.Trace.SubMean
+// path is for the paths Learn.DWt.SubMean
 // in both cases, it is generally best to have both parameters set to 0
 // at the start of learning
 func (nt *Network) SetSubMean(trgAvg, path float32) {

axon/learn-path.goal

@@ -46,7 +46,8 @@ func (pt *PathParams) DWtSyn(ctx *Context, rlay *LayerParams, syni, si, ri, di u
 }
 
 // SynCa gets the synaptic calcium P (potentiation) and D (depression)
-// values, using optimized computation.
+// values, using an optimized integration of neuron-level SpikeBins values,
+// and weight factors to capture the different CaP vs. CaD time constants.
 func (pt *PathParams) SynCa(ctx *Context, si, ri, di uint32, syCaP, syCaD *float32) {
 	nbins := NetworkIxs[0].NSpikeBins
 	cadSt := GvSpikeBinWts+GlobalScalarVars(nbins)
@@ -56,13 +57,15 @@ func (pt *PathParams) SynCa(ctx *Context, si, ri, di uint32, syCaP, syCaD *float
 		cp += sp * GlobalScalars[GvSpikeBinWts + GlobalScalarVars(i), 0]
 		cd += sp * GlobalScalars[cadSt + GlobalScalarVars(i), 0]
 	}
-	*syCaP = pt.Learn.Trace.CaGain * cp
-	*syCaD = pt.Learn.Trace.CaGain * cd
+	*syCaP = pt.Learn.DWt.CaGain * cp
+	*syCaD = pt.Learn.DWt.CaGain * cd
 }
 
-// DWtSynCortex computes the weight change (learning) at given synapse for cortex.
-// Uses synaptically integrated spiking, computed at the Theta cycle interval.
-// This is the trace version for hidden units, and uses syn CaP - CaD for targets.
+// DWtSynCortex computes the weight change (learning) at given synapse, using the
+// kinase error-driven learning rule for cortical neurons. The error delta is
+// based on the receiving neuron's [LearnCaP] - [LearnCaD], multiplied by a separate
+// credit assignment trace factor computed from synaptic co-product CaD values
+// that can be integrated across multiple theta cycle learning trials.
 func (pt *PathParams) DWtSynCortex(ctx *Context, syni, si, ri, lpi, pi, di uint32, isTarget bool) {
 	var syCaP, syCaD float32
 	pt.SynCa(ctx, si, ri, di, &syCaP, &syCaD)
@@ -74,7 +77,7 @@ func (pt *PathParams) DWtSynCortex(ctx *Context, syni, si, ri, lpi, pi, di uint3
 	// save delta trace for GUI
 	SynapseTraces[syni, di, DTr] = dtr
 	// TrFromCa(prev-multiTrial Integrated Trace, deltaTrace), as a mixing func
-	tr := pt.Learn.Trace.TrFromCa(SynapseTraces[syni, di, Tr], dtr)
+	tr := pt.Learn.DWt.TrFromCa(SynapseTraces[syni, di, Tr], dtr)
 	// save new trace, updated w/ credit assignment (dependent on Tau in the TrFromCa function
 	SynapseTraces[syni, di, Tr] = tr
 	// failed con, no learn
@@ -84,7 +87,7 @@ func (pt *PathParams) DWtSynCortex(ctx *Context, syni, si, ri, lpi, pi, di uint3
 
 	// error-driven learning
 	var err float32
-	if isTarget {
+	if isTarget || pt.Learn.DWt.Trace.IsFalse() {
 		err = syCaP - syCaD // for target layers, syn Ca drives error signal directly
 	} else {
 		err = tr * (Neurons[ri, di, LearnCaP] - Neurons[ri, di, LearnCaD]) // hiddens: recv NMDA Ca drives error signal w/ trace credit
@@ -128,7 +131,7 @@ func (pt *PathParams) DWtSynHip(ctx *Context, syni, si, ri, lpi, pi, di uint32,
 	// save delta trace for GUI
 	SynapseTraces[syni, di, DTr] = dtr
 	// TrFromCa(prev-multiTrial Integrated Trace, deltaTrace), as a mixing func
-	tr := pt.Learn.Trace.TrFromCa(SynapseTraces[syni, di, Tr], dtr)
+	tr := pt.Learn.DWt.TrFromCa(SynapseTraces[syni, di, Tr], dtr)
 	// save new trace, updated w/ credit assignment (dependent on Tau in the TrFromCa function
 	SynapseTraces[syni, di, Tr] = tr
 	// failed con, no learn
@@ -175,7 +178,7 @@ func (pt *PathParams) DWtSynBLA(ctx *Context, syni, si, ri, lpi, pi, di uint32)
 	ach := GlobalScalars[GvACh, di]
 	if GlobalScalars[GvHasRew, di] > 0 { // learn and reset
 		ract := Neurons[ri, di, CaD]
-		if ract < pt.Learn.Trace.LearnThr {
+		if ract < pt.Learn.DWt.LearnThr {
 			ract = 0
 		}
 		tr := SynapseTraces[syni, di, Tr]
@@ -191,7 +194,7 @@ func (pt *PathParams) DWtSynBLA(ctx *Context, syni, si, ri, lpi, pi, di uint32)
 		// note: the former NonUSLRate parameter is not used -- Trace update Tau replaces it..  elegant
 		dtr := ach * Neurons[si, di, Burst]
 		SynapseTraces[syni, di, DTr] = dtr
-		tr := pt.Learn.Trace.TrFromCa(SynapseTraces[syni, di, Tr], dtr)
+		tr := pt.Learn.DWt.TrFromCa(SynapseTraces[syni, di, Tr], dtr)
 		SynapseTraces[syni, di, Tr] = tr
 	} else {
 		SynapseTraces[syni, di, DTr] = 0.0
@@ -281,7 +284,7 @@ func (pt *PathParams) DWtSynVSMatrix(ctx *Context, syni, si, ri, lpi, pi, di uin
 	rminus := Neurons[ri, di, CaD]
 	sact := Neurons[si, di, CaD]
 	dtr := ach * (pt.Matrix.Delta * sact * (rplus - rminus))
-	if rminus > pt.Learn.Trace.LearnThr { // key: prevents learning if < threshold
+	if rminus > pt.Learn.DWt.LearnThr { // key: prevents learning if < threshold
 		dtr += ach * (pt.Matrix.Credit * sact * rminus)
 	}
 	if hasRew {
@@ -318,7 +321,7 @@ func (pt *PathParams) DWtSynDSMatrix(ctx *Context, syni, si, ri, lpi, pi, di uin
 		rminus := Neurons[ri, di, CaD]
 		sact := Neurons[si, di, CaD]
 		dtr := rlr * (pt.Matrix.Delta * sact * (rplus - rminus))
-		if rminus > pt.Learn.Trace.LearnThr { // key: prevents learning if < threshold
+		if rminus > pt.Learn.DWt.LearnThr { // key: prevents learning if < threshold
 			dtr += rlr * (pt.Matrix.Credit * pfmod * sact * rminus)
 		}
 		SynapseTraces[syni, di, DTr] = dtr
@@ -330,7 +333,7 @@ func (pt *PathParams) DWtSynDSMatrix(ctx *Context, syni, si, ri, lpi, pi, di uin
 // for the VSPatchPath type.
 func (pt *PathParams) DWtSynVSPatch(ctx *Context, syni, si, ri, lpi, pi, di uint32) {
 	ract := Neurons[ri, di, CaDPrev] // t-1
-	if ract < pt.Learn.Trace.LearnThr {
+	if ract < pt.Learn.DWt.LearnThr {
 		ract = 0
 	}
 	// note: rn.RLRate already has ACh * DA * (D1 vs. D2 sign reversal) factored in.
@@ -358,7 +361,7 @@ func (pt *PathParams) DWtSubMean(ctx *Context, pti, ri, lni uint32) {
 	if pt.Learn.Learn.IsFalse() {
 		return
 	}
-	sm := pt.Learn.Trace.SubMean
+	sm := pt.Learn.DWt.SubMean
 	if sm == 0 { // note default is now 0, so don't exclude Target layers, which should be 0
 		return
 	}