found the bug: learn now for SynCa was not the right value.

Author: rcoreilly

axon/learn-path.go

@@ -138,12 +138,12 @@ func (pt *PathParams) DWtSynSoftBound(ctx *Context, syni, di uint32, dwt float32
 // based on the receiving neuron's [LearnCaP] - [LearnCaD], multiplied by a separate
 // synaptic activation credit assignment factor computed from synaptic co-product CaD values.
 func (pt *PathParams) DWtSynCortex(ctx *Context, rlay *LayerParams, syni, si, ri, lpi, pi, di uint32) {
-	learnNow := int32(Neurons[ri, di, LearnNow]) - (ctx.CyclesTotal - ctx.ThetaCycles)
-	if learnNow < 0 { // not in this time window
+	learnNow := int32(Neurons[ri, di, LearnNow])
+	if learnNow - (ctx.CyclesTotal - ctx.ThetaCycles) < 0 { // not in this time window
 		SynapseTraces[syni, di, DiDWt] = 0.0
 		return
 	}
-	syCa := pt.SynCaTotal(ctx, si, ri, di, int32(learnNow), rlay.Learn.Timing.SynCaCycles)
+	syCa := pt.SynCaTotal(ctx, si, ri, di, learnNow, rlay.Learn.Timing.SynCaCycles)
 
 	// integrate synaptic trace over time: this is actually beneficial in certain cases,
 	// in addition to the ETraceLearn factor.

axon/learn-path.goal

@@ -1006,11 +1006,11 @@ fn PathParams_DWtSynSoftBound(pt: PathParams, ctx: Context, syni: u32,di: u32, d
 	}
 }
 fn PathParams_DWtSynCortex(pt: PathParams, ctx: Context, rlay: LayerParams, syni: u32,si: u32,ri: u32,lpi: u32,pi: u32,di: u32) {
-	var learnNow = i32(Neurons[Index3D(TensorStrides[70], TensorStrides[71], TensorStrides[72], u32(ri), u32(di), u32(LearnNow))]) - (ctx.CyclesTotal - ctx.ThetaCycles);
-	if (learnNow < 0) { // not in this time window
+	var learnNow = i32(Neurons[Index3D(TensorStrides[70], TensorStrides[71], TensorStrides[72], u32(ri), u32(di), u32(LearnNow))]);
+	if (learnNow-(ctx.CyclesTotal-ctx.ThetaCycles) < 0) { // not in this time window
 		SynapseTracesSet(0.0, Index3D(TensorStrides[180], TensorStrides[181], TensorStrides[182], u32(syni), u32(di), u32(DiDWt)));return;
 	}
-	var syCa = PathParams_SynCaTotal(pt, ctx, si, ri, di, i32(learnNow), rlay.Learn.Timing.SynCaCycles);
+	var syCa = PathParams_SynCaTotal(pt, ctx, si, ri, di, learnNow, rlay.Learn.Timing.SynCaCycles);
 	SynapseTracesSet(syCa, Index3D(TensorStrides[180], TensorStrides[181], TensorStrides[182], u32(syni), u32(di), u32(DTr)));
 	var tr = DWtParams_SynTrace(pt.Learn.DWt, SynapseTracesGet(Index3D(TensorStrides[180], TensorStrides[181], TensorStrides[182], u32(syni), u32(di), u32(Tr))), syCa);
 	SynapseTracesSet(tr, Index3D(TensorStrides[180], TensorStrides[181], TensorStrides[182], u32(syni), u32(di), u32(Tr)));

axon/shaders/DWtSyn.wgsl

@@ -33,8 +33,8 @@ var LayerParams = axon.LayerSheets{
 				ly.Learn.CaSpike.Dt.MTau = 5                // 5 > 10 even with more ncycles
 
 				ly.Learn.Timing.On.SetBool(false)
-				ly.Learn.Timing.Refractory.SetBool(true)
-				ly.Learn.Timing.LearnThr = 0.05
+				ly.Learn.Timing.Refractory.SetBool(false)
+				ly.Learn.Timing.LearnThr = 0
 				ly.Learn.Timing.SynCaCycles = 160
 				ly.Learn.Timing.MinusThr = 0
 				ly.Learn.Timing.MinusCycles = 110
@@ -95,7 +95,7 @@ var PathParams = axon.PathSheets{
 			Set: func(pt *axon.PathParams) {
 				// pt.Com.MaxDelay = 10 // not much effect
 				// pt.Com.Delay = 10
-				pt.Learn.LRate.Base = 0.1     // 0.1 > 0.05
+				pt.Learn.LRate.Base = 0.2     // 0.1 > 0.05
 				pt.Learn.DWt.SubMean = 1      // 1 -- faster if 0 until 20 epc -- prevents sig amount of late deterioration
 				pt.SWts.Adapt.LRate = 0.0001  // 0.005 == .1 == .01
 				pt.SWts.Adapt.HiMeanDecay = 0 // 0 > 0.0008 (best in lvis)
@@ -105,6 +105,10 @@ var PathParams = axon.PathSheets{
 				pt.Learn.DWt.LearnThr = 0.1
 				pt.Learn.DWt.SynCa20.SetBool(false)
 			}},
+		{Sel: "#ITToOutput", Doc: "",
+			Set: func(pt *axon.PathParams) {
+				pt.Learn.LRate.Base = 0.1 // 0.1 > 0.05
+			}},
 		{Sel: ".BackPath", Doc: "top-down back-pathways MUST have lower relative weight scale, otherwise network hallucinates -- smaller as network gets bigger",
 			Set: func(pt *axon.PathParams) {
 				pt.PathScale.Rel = 0.2 // .2 >= .3 > .15 > .1 > .05 @176
@@ -115,7 +119,7 @@ var PathParams = axon.PathSheets{
 			}},
 		{Sel: ".V1SC", Doc: "v1 shortcut",
 			Set: func(pt *axon.PathParams) {
-				pt.Learn.LRate.Base = 0.02      // 0.02 >= 0.01 > 0.001, 0.05
+				pt.Learn.LRate.Base = 0.04      // 0.02 >= 0.01 > 0.001, 0.05
 				pt.PathScale.Rel = 0.05         // 0.2 >> 0.3, 0.5 (blows up)
 				pt.SWts.Adapt.On.SetBool(false) // seems better
 			}},