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 {