kinaseq is just for testing learned regress eqs; cleanup a few things.

examples/deep_fsa/params.go

@@ -88,7 +88,7 @@ var ParamSets = netparams.Sets{
 				"Path.SWts.Adapt.LRate":    "0.01", // 0.01 or 0.0001 music
 				"Path.SWts.Init.SPct":      "1.0",  // 1 works fine here -- .5 also ok
 				"Path.Com.PFail":           "0.0",
-				"Path.Learn.Trace.Tau":     "2", // 2 > 1 still 1.7.19
+				"Path.Learn.Trace.Tau":     "1", // 1 >> 2 v0.0.9
 			}},
 		{Sel: ".BackPath", Desc: "top-down back-pathways MUST have lower relative weight scale, otherwise network hallucinates",
 			Params: params.Params{

examples/kinaseq/config.go

@@ -6,22 +6,6 @@ package main
 
 // ParamConfig has config parameters related to sim params
 type ParamConfig struct {
-
-	// gain multiplier on spike for computing CaSpk: increasing this directly affects the magnitude of the trace values, learning rate in Target layers, and other factors that depend on CaSpk values: RLRate, UpdateThr.  Path.KinaseCa.SpikeG provides an additional gain factor specific to the synapse-level trace factors, without affecting neuron-level CaSpk values.  Larger networks require higher gain factors at the neuron level -- 12, vs 8 for smaller.
-	SpikeG float32 `default:"8"`
-
-	// time constant for integrating spike-driven calcium trace at sender and recv neurons, CaSyn, which then drives synapse-level integration of the joint pre * post synapse-level activity, in cycles (msec).  Note: if this param is changed, then there will be a change in effective learning rate that can be compensated for by multiplying PathParams.Learn.KinaseCa.SpikeG by sqrt(30 / sqrt(SynTau)
-	SynTau float32 `default:"30" min:"1"`
-
-	// rate = 1 / tau
-	SynDt float32 `view:"-" json:"-" xml:"-" edit:"-"`
-
-	// learning rate for decoder
-	LRate float32 `default:"0.001"`
-
-	// network parameters
-	Network map[string]any
-
 	// Extra Param Sheet name(s) to use (space separated if multiple) -- must be valid name as listed in compiled-in params or loaded params
 	Sheet string
 
@@ -41,15 +25,8 @@ type ParamConfig struct {
 	Good bool `nest:"+"`
 }
 
-func (pc *ParamConfig) Update() {
-	pc.SynDt = 1.0 / pc.SynTau
-}
-
 // RunConfig has config parameters related to running the sim
 type RunConfig struct {
-	// Neuron runs the standard Neuron update equations, vs. Kinase experimental equations
-	Neuron bool `default:"false"`
-
 	// use the GPU for computation -- only for testing in this model -- not faster
 	GPU bool `default:"false"`
 

examples/kinaseq/kinaseq.go

@@ -54,7 +54,7 @@ func (kn *KinaseNeuron) StartTrial() {
 
 // Cycle does one cycle of neuron updating, with given exponential spike interval
 // based on target spiking firing rate.
-func (kn *KinaseNeuron) Cycle(expInt float32, params *ParamConfig, cyc int) {
+func (ss *Sim) Cycle(kn *KinaseNeuron, expInt float32, cyc int) {
 	kn.Spike = 0
 	bin := cyc / 50
 	if expInt > 0 {
@@ -66,7 +66,7 @@ func (kn *KinaseNeuron) Cycle(expInt float32, params *ParamConfig, cyc int) {
 			kn.SpikeBins[bin] += 1
 		}
 	}
-	kn.CaSyn += params.SynDt * (params.SpikeG*kn.Spike - kn.CaSyn)
+	kn.CaSyn += ss.NeurCa.SynDt * (ss.NeurCa.SpikeG*kn.Spike - kn.CaSyn)
 }
 
 func (kn *KinaseNeuron) SetInput(inputs []float32, off int) {
@@ -102,9 +102,6 @@ func (ks *KinaseSynapse) Init() {
 // KinaseState is basic Kinase equation state
 type KinaseState struct {
 
-	// if true, training decoder
-	Train bool
-
 	// SSE for decoder
 	SSE float32
 
@@ -159,7 +156,6 @@ func (ss *Sim) ConfigKinase() {
 
 // Sweep runs a sweep through minus-plus ranges
 func (ss *Sim) Sweep() {
-	ss.Kinase.Train = false
 	// hz := []float32{25, 50, 100}
 	// nhz := len(hz)
 
@@ -196,7 +192,6 @@ func (ss *Sim) Run() {
 
 // RunImpl runs NTrials, recording to RunLog and TrialLog
 func (ss *Sim) RunImpl(minusHz, plusHz float32, ntrials int) {
-	ss.Kinase.Train = false
 	ss.Kinase.Init()
 	for trl := 0; trl < ntrials; trl++ {
 		ss.Kinase.Trial = trl
@@ -248,14 +243,12 @@ func (ss *Sim) TrialImpl(minusHz, plusHz float32) {
 			Sint = math32.Exp(-1000.0 / float32(shz))
 		}
 		for t := 0; t < maxcyc; t++ {
-			ks.Send.Cycle(Sint, &cfg.Params, ks.Cycle)
-			ks.Recv.Cycle(Rint, &cfg.Params, ks.Cycle)
+			ss.Cycle(&ks.Send, Sint, ks.Cycle)
+			ss.Cycle(&ks.Recv, Rint, ks.Cycle)
 
 			ca := ks.Send.CaSyn * ks.Recv.CaSyn
-			ss.CaParams.FromCa(ca, &ks.StdSyn.CaM, &ks.StdSyn.CaP, &ks.StdSyn.CaD)
-			if !ks.Train {
-				ss.Logs.LogRow(etime.Test, etime.Cycle, ks.Cycle)
-			}
+			ss.SynCa.FromCa(ca, &ks.StdSyn.CaM, &ks.StdSyn.CaP, &ks.StdSyn.CaD)
+			ss.Logs.LogRow(etime.Test, etime.Cycle, ks.Cycle)
 			ks.Cycle++
 		}
 	}
@@ -265,36 +258,12 @@ func (ss *Sim) TrialImpl(minusHz, plusHz float32) {
 		ks.SpikeBins[i] = 0.1 * (ks.Recv.SpikeBins[i] * ks.Send.SpikeBins[i])
 	}
 
-	ss.CaParams.FinalCa(ks.SpikeBins[0], ks.SpikeBins[1], ks.SpikeBins[2], ks.SpikeBins[3], &ks.LinearSyn.CaM, &ks.LinearSyn.CaP, &ks.LinearSyn.CaD)
+	ss.LinearSynCa.FinalCa(ks.SpikeBins[0], ks.SpikeBins[1], ks.SpikeBins[2], ks.SpikeBins[3], &ks.LinearSyn.CaP, &ks.LinearSyn.CaD)
 	ks.LinearSyn.DWt = ks.LinearSyn.CaP - ks.LinearSyn.CaD
 
-	if ks.Train {
-		ss.Logs.LogRow(etime.Train, etime.Cycle, 0)
-		ss.GUI.UpdatePlot(etime.Train, etime.Cycle)
-		ss.Logs.LogRow(etime.Train, etime.Trial, ks.Trial)
-		ss.GUI.UpdatePlot(etime.Train, etime.Trial)
-	} else {
-		ss.GUI.UpdatePlot(etime.Test, etime.Cycle)
-		ss.Logs.LogRow(etime.Test, etime.Trial, ks.Trial)
-		ss.GUI.UpdatePlot(etime.Test, etime.Trial)
-	}
-}
-
-// Train trains the linear decoder
-func (ss *Sim) Train() {
-	ss.Kinase.Train = true
-	ss.Kinase.Init()
-	for epc := 0; epc < ss.Config.Run.NEpochs; epc++ {
-		ss.Kinase.Condition = epc
-		for trl := 0; trl < ss.Config.Run.NTrials; trl++ {
-			ss.Kinase.Trial = trl
-			minusHz := 100 * rand.Float32()
-			plusHz := 100 * rand.Float32()
-			ss.TrialImpl(minusHz, plusHz)
-		}
-		ss.Logs.LogRow(etime.Train, etime.Condition, ss.Kinase.Condition)
-		ss.GUI.UpdatePlot(etime.Train, etime.Condition)
-	}
+	ss.GUI.UpdatePlot(etime.Test, etime.Cycle)
+	ss.Logs.LogRow(etime.Test, etime.Trial, ks.Trial)
+	ss.GUI.UpdatePlot(etime.Test, etime.Trial)
 }
 
 func (ss *Sim) ConfigKinaseLogItems() {
@@ -346,15 +315,6 @@ func (ss *Sim) ConfigKinaseLogItems() {
 						ctx.SetAgg(ctx.Mode, times[ti+1], stats.Mean)
 					}
 				} else {
-					itm.Write[etime.Scope(etime.Train, times[ti])] = func(ctx *elog.Context) {
-						fany := value.Interface()
-						switch fkind {
-						case reflect.Int:
-							ctx.SetFloat32(float32(fany.(int)))
-						case reflect.String:
-							ctx.SetString(fany.(string))
-						}
-					}
 					itm.Write[etime.Scope(etime.Test, times[ti])] = func(ctx *elog.Context) {
 						fany := value.Interface()
 						switch fkind {