diff --git a/ch2/go.sum b/ch2/go.sum
new file mode 100644
index 00000000..59151eb6
--- /dev/null
+++ b/ch2/go.sum
@@ -0,0 +1,375 @@
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diff --git a/ch3/go.sum b/ch3/go.sum
new file mode 100644
index 00000000..59151eb6
--- /dev/null
+++ b/ch3/go.sum
@@ -0,0 +1,375 @@
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diff --git a/ch4/go.sum b/ch4/go.sum
new file mode 100644
index 00000000..59151eb6
--- /dev/null
+++ b/ch4/go.sum
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diff --git a/ch8/bg_action/Makefile b/ch8/bg_action/Makefile
new file mode 100644
index 00000000..6b7a7d58
--- /dev/null
+++ b/ch8/bg_action/Makefile
@@ -0,0 +1,18 @@
+# Makefile for bg_action simulation
+
+all: build
+
+build:
+	go build -v
+
+run:
+	go run bg_action.go
+
+batch:
+	go run bg_action.go -runs 50 -epochs 30
+
+batch-pd:
+	go run bg_action.go -runs 50 -epochs 30 -pd
+
+clean:
+	rm -f bg_action
diff --git a/ch8/bg_action/README.md b/ch8/bg_action/README.md
new file mode 100644
index 00000000..6933bdaf
--- /dev/null
+++ b/ch8/bg_action/README.md
@@ -0,0 +1,121 @@
+# BG Action Selection Model
+
+This simulation implements a biologically realistic basal ganglia (BG) model for the **Probabilistic Selection (PS) task** (Frank et al., 2004). It is translated from the emergent 7.0+ project file `BG_4s_inhib_PS_e7a.proj`.
+
+## Key Features
+
+Unlike the simplified BG model (Chapter 8, `bg` sim) where actions are directly input to the network, this model:
+
+1. **Selects its own actions** via disinhibitory gating of thalamus → premotor cortex
+2. **Explores** via noise in premotor cortex (VM_NOISE with a declining schedule)
+3. **Learns** reward associations through Go/NoGo pathways modulated by dopamine (D1/D2 rules)
+4. **Simulates Parkinson's Disease** by reducing intact SNc dopamine units
+5. **Simulates D2 agonist effects** by modulating D2 projection strength
+
+## Network Architecture
+
+Translated from `BG_4s_inhib_PS_e7a.proj`:
+
+| Layer | Shape | Description |
+|---|---|---|
+| Input | 6×3 (18) | Stimulus representation (S1, S2) |
+| Context | 9×2 (18) | Contextual state information |
+| Go | 6×6 (36) | D1R medium spiny neurons (direct pathway) |
+| NoGo | 6×6 (36) | D2R medium spiny neurons (indirect pathway) |
+| StriatumInhib | 4×4 (16) | Striatal inhibitory interneurons |
+| GPe | 2×2 (4) | External globus pallidus |
+| GPi | 4×2 (8) | Internal globus pallidus / SNr |
+| Thalamus | 2×2 (4) | Thalamus (gating output) |
+| PMC | 4×2 (8) | Premotor cortex (action representation) |
+| Output | 4×1 (4) | Response output |
+| SNc | 2×2 (4) | Dopamine neurons (externally clamped) |
+
+### BG Circuit
+
+```
+Input/Context → Go (D1) → GPi ⊣ Thalamus → PMC → Output
+                 ↑ SNc ↑         ↑
+Input/Context → NoGo (D2) → GPe ⊣ GPi
+                 ↑ SNc (inhib) ↑
+PMC ←→ Thalamus (recurrent)
+```
+
+## Task
+
+The **Probabilistic Selection Task** (Frank et al., 2004):
+
+### Training Phase
+- **S1 trials (8020_R1R2)**: R1 rewarded 80%, R2 rewarded 20%
+- **S2 trials (6040_R3R4)**: R3 rewarded 60%, R4 rewarded 40%
+
+Candidate responses are biased in PMC so that only relevant actions compete (R1/R2 for S1; R3/R4 for S2).
+
+### Test Phase
+Measures **Choose-A** (selecting the most rewarded response) and **Avoid-B** (avoiding the most punished response), demonstrating Go vs. NoGo learning dissociation.
+
+## Parameters
+
+### DA Manipulation
+
+| Parameter | Default | Description |
+|---|---|---|
+| `NumIntactSNc` | 4 | Intact DA neurons (4=healthy, 2=PD) |
+| `D1Acq` / `D1Perf` | 0.6 | D1 projection strength (acq/perf) |
+| `D2Acq` / `D2Perf` | 0.075 | D2 projection strength (acq/perf) |
+| `DABurstVal` | 1.0 | SNc value for DA burst (reward) |
+| `DADipVal` | 0.0 | SNc value for DA dip (punishment) |
+| `TonicDA` | 0.026 | Tonic DA level |
+
+### Noise Schedule (PMC exploration)
+Follows the original emergent 7 schedule:
+- Epochs 0–25: noise × 1.0 (full exploration)
+- Epochs 25–80: noise ramps 1.0 → 0.5
+- Epochs 80–100: noise ramps 0.5 → 0.2
+
+## Mapping from BG_4s_inhib_PS_e7a.proj
+
+### What was matched
+- All 11 layers with their unit counts and shapes
+- Full projection connectivity (Input/Context → Go/NoGo, Go → GPi, NoGo → GPe → GPi, GPi ⊣ Thalamus, Thalamus ↔ PMC)
+- D1/D2 dopamine modulation of Go/NoGo learning
+- SNc DA burst/dip reward delivery logic
+- PMC noise-based exploration
+- Probabilistic reward schedules (80/20, 60/40)
+- PMC bias for candidate response selection
+- PD simulation via SNc unit reduction
+- D2 agonist simulation via projection strength
+- Program flow: minus phase → determine action → deliver reward → plus phase → DA-modulated learning
+
+### Unavoidable Differences from Emergent 7
+
+1. **Inhibition**: The old model used `UNIT_INHIB` (unit-level inhibition) for most BG layers. The Go leabra implementation uses `FFFB` inhibition. Parameters (Gi, FB) are set to approximate the original behavior.
+
+2. **Noise**: The original used `VM_NOISE` with a `noise_sched` controlling noise level across epochs. This implementation uses `GeNoise` (conductance noise) with programmatic schedule control.
+
+3. **Tessellation projections**: The old model used `TesselPrjnSpec` for topographic SNc→Go/NoGo connectivity (mapping specific SNc units to specific striatal pools). This implementation uses `Full` connectivity with DA modulation applied uniformly.
+
+4. **Bias weight manipulation**: The old model set bias weights on PMC units via `SetCnValName()` to bias candidate responses. This implementation uses external input (`Ext`) on PMC neurons.
+
+5. **Connection specs**: The old model had detailed `wt_scale` parameters for each connection spec. Key values (abs, rel) are preserved but some nuanced parameters (e.g., `savg_cor`, specialized `hebb` ratios for each spec) may differ.
+
+6. **STN**: The old model had an STN (subthalamic nucleus) component with specialized connections. The STN is not explicitly modeled as a separate layer in this translation but its effect (diffuse excitation of GPi) is approximated through GPi excitability settings.
+
+7. **No separate `Striatum_Inhib` feedback/feedforward interneuron spec**: The old model distinguished `FBtoInhib` and `FFtoInhib` connection specs. This implementation groups them under generic inhibitory connectivity.
+
+## Running
+
+```bash
+# GUI mode
+go run bg_action.go
+
+# Batch mode (50 networks, 30 epochs)
+go run bg_action.go -runs 50 -epochs 30
+
+# Batch mode with Parkinson's Disease
+go run bg_action.go -runs 50 -epochs 30 -pd
+```
+
+## References
+
+- Frank, M. J. (2005). Dynamic dopamine modulation in the basal ganglia: A neurocomputational account of cognitive deficits in medicated and non-medicated Parkinsonism. *Journal of Cognitive Neuroscience, 17*(1), 51-72.
+- Frank, M. J., Seeberger, L. C., & O'Reilly, R. C. (2004). By carrot or by stick: Cognitive reinforcement learning in Parkinsonism. *Science, 306*(5703), 1940-1943.
diff --git a/ch8/bg_action/bg_action b/ch8/bg_action/bg_action
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diff --git a/ch8/bg_action/bg_action.go b/ch8/bg_action/bg_action.go
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--- /dev/null
+++ b/ch8/bg_action/bg_action.go
@@ -0,0 +1,1765 @@
+// Copyright (c) 2024, The Emergent Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+/*
+bg_action implements a biologically realistic basal ganglia model for
+the probabilistic selection (PS) task (Frank et al., 2004). Unlike the
+simplified BG model in which actions are provided as input, this model
+selects its own actions via disinhibitory gating of thalamus / premotor
+cortex, with exploration driven by cortical noise.
+
+Translated from the emergent 7 project: BG_4s_inhib_PS_e7a.proj
+*/
+package main
+
+import (
+	"bytes"
+	"fmt"
+	"log"
+	"math"
+	"math/rand"
+	"os"
+	"strconv"
+	"strings"
+	"time"
+
+	"github.com/emer/emergent/emer"
+	"github.com/emer/emergent/netview"
+	"github.com/emer/emergent/params"
+	"github.com/emer/emergent/prjn"
+	"github.com/emer/etable/agg"
+	"github.com/emer/etable/eplot"
+	"github.com/emer/etable/etable"
+	"github.com/emer/etable/etensor"
+	_ "github.com/emer/etable/etview" // include to get gui views
+	"github.com/emer/etable/split"
+	"github.com/emer/leabra/leabra"
+	"github.com/goki/gi/gi"
+	"github.com/goki/gi/gimain"
+	"github.com/goki/gi/giv"
+	"github.com/goki/ki/ki"
+	"github.com/goki/ki/kit"
+)
+
+func main() {
+	TheSim.New()
+	TheSim.Config()
+	if len(os.Args) > 1 {
+		TheSim.CmdArgs() // simple abs-mode
+		return
+	}
+	gimain.Main(func() { // this starts gui -- requires assignment into var
+		guirun()
+	})
+}
+
+func guirun() {
+	TheSim.Init()
+	win := TheSim.ConfigGui()
+	win.StartEventLoop()
+}
+
+// ParamSets defines the parameters for the simulation.
+// The Base set provides the core parameters from the original emergent 7 project.
+var ParamSets = params.Sets{
+	{Name: "Base", Desc: "base parameters from BG_4s_inhib_PS_e7a.proj", Sheets: params.Sheets{
+		"Network": &params.Sheet{
+			// -- Input layer (clamped) --
+			{Sel: "#Input", Desc: "input layer",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.25",
+					"Layer.Act.Dt.VmTau":   "10",
+					"Layer.Act.Dt.GTau":    "1.4",
+				}},
+			{Sel: "#Context", Desc: "context layer",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.25",
+				}},
+			// -- Matrisome (Go / NoGo) layers --
+			{Sel: "#Go", Desc: "Go (D1R) MSN layer",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Inhib.Layer.FB": "0",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.26",
+					"Layer.Act.Dt.VmTau":   "43.5",
+					"Layer.Act.Dt.GTau":    "1.4",
+					"Layer.Act.Gbar.E":     "1",
+					"Layer.Act.Gbar.L":     "0.35",
+					"Layer.Act.Gbar.I":     "7.5",
+					"Layer.Act.Erev.E":     "1",
+					"Layer.Act.Erev.L":     "0.15",
+					"Layer.Act.Erev.I":     "0.15",
+				}},
+			{Sel: "#NoGo", Desc: "NoGo (D2R) MSN layer",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Inhib.Layer.FB": "0",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.26",
+					"Layer.Act.Dt.VmTau":   "43.5",
+					"Layer.Act.Dt.GTau":    "1.4",
+					"Layer.Act.Gbar.E":     "1",
+					"Layer.Act.Gbar.L":     "0.35",
+					"Layer.Act.Gbar.I":     "7.5",
+					"Layer.Act.Erev.E":     "1",
+					"Layer.Act.Erev.L":     "0.15",
+					"Layer.Act.Erev.I":     "0.15",
+				}},
+			// -- Striatum_Inhib layer --
+			{Sel: "#StriatumInhib", Desc: "striatal inhibitory interneurons",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.25",
+				}},
+			// -- GPe layer --
+			{Sel: "#GPe", Desc: "external segment of globus pallidus",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Inhib.Layer.FB": "0",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.25",
+					"Layer.Act.Dt.VmTau":   "10",
+					"Layer.Act.Dt.GTau":    "1.4",
+					"Layer.Act.Gbar.E":     "1",
+					"Layer.Act.Gbar.L":     "1",
+					"Layer.Act.Gbar.I":     "1.5",
+					"Layer.Act.Erev.E":     "1",
+					"Layer.Act.Erev.L":     "0.26",
+					"Layer.Act.Erev.I":     "0.15",
+				}},
+			// -- GPi layer --
+			{Sel: "#GPi", Desc: "internal segment of globus pallidus",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Inhib.Layer.FB": "0",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.25",
+					"Layer.Act.Dt.VmTau":   "10",
+					"Layer.Act.Dt.GTau":    "1.4",
+					"Layer.Act.Gbar.E":     "1",
+					"Layer.Act.Gbar.L":     "1",
+					"Layer.Act.Gbar.I":     "1.5",
+					"Layer.Act.Erev.E":     "1",
+					"Layer.Act.Erev.L":     "0.26",
+					"Layer.Act.Erev.I":     "0.15",
+				}},
+			// -- Thalamus layer --
+			{Sel: "#Thalamus", Desc: "thalamus",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Inhib.Layer.FB": "0",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.25",
+					"Layer.Act.Dt.VmTau":   "20",
+					"Layer.Act.Dt.GTau":    "2.5",
+					"Layer.Act.Gbar.E":     "0.5",
+					"Layer.Act.Gbar.L":     "0.07",
+					"Layer.Act.Gbar.I":     "1.7",
+					"Layer.Act.Erev.E":     "1",
+					"Layer.Act.Erev.L":     "0.15",
+					"Layer.Act.Erev.I":     "0.15",
+				}},
+			// -- PMC (Premotor Cortex) --
+			{Sel: "#PMC", Desc: "premotor cortex",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.25",
+					"Layer.Act.Dt.VmTau":   "20",
+					"Layer.Act.Dt.GTau":    "1.4",
+					"Layer.Act.Gbar.E":     "1",
+					"Layer.Act.Gbar.L":     "0.1",
+					"Layer.Act.Gbar.I":     "1",
+					"Layer.Act.Erev.E":     "1",
+					"Layer.Act.Erev.L":     "0.15",
+					"Layer.Act.Erev.I":     "0.15",
+					"Layer.Act.Noise.Dist":  "Gaussian",
+					"Layer.Act.Noise.Var":   "0.001",
+					"Layer.Act.Noise.Type":  "GeNoise",
+					"Layer.Act.Noise.Fixed": "false",
+				}},
+			// -- Output layer --
+			{Sel: "#Output", Desc: "output layer",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Act.XX1.Gain":   "20",
+					"Layer.Act.XX1.Thr":    "0.2",
+					"Layer.Act.Dt.VmTau":   "33",
+					"Layer.Act.Dt.GTau":    "10",
+				}},
+			// -- SNc layer --
+			{Sel: "#SNc", Desc: "dopamine neurons (clamped externally)",
+				Params: params.Params{
+					"Layer.Inhib.Layer.Gi": "2",
+					"Layer.Act.XX1.Gain":   "600",
+					"Layer.Act.XX1.Thr":    "0.25",
+				}},
+
+			// ---- Projection parameters ----
+
+			// Input -> Go (D1 learning)
+			{Sel: "#InputToGo", Desc: "input to Go D1 pathway",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.6",
+					"Prjn.Learn.Lrate": "0.05",
+					"Prjn.Learn.Hebb":  "0.1",
+				}},
+			// Input -> NoGo (D2 learning)
+			{Sel: "#InputToNoGo", Desc: "input to NoGo D2 pathway",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.65",
+					"Prjn.Learn.Lrate": "0.05",
+					"Prjn.Learn.Hebb":  "0.1",
+				}},
+			// Context -> Go
+			{Sel: "#ContextToGo", Desc: "context to Go",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.6",
+					"Prjn.WtScale.Rel": "1.25",
+					"Prjn.Learn.Lrate": "0",
+					"Prjn.Learn.Hebb":  "0.1",
+				}},
+			// Context -> NoGo
+			{Sel: "#ContextToNoGo", Desc: "context to NoGo",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.6",
+					"Prjn.WtScale.Rel": "1.25",
+					"Prjn.Learn.Lrate": "0",
+					"Prjn.Learn.Hebb":  "0.1",
+				}},
+			// SNc -> Go (D1 dopamine)
+			{Sel: "#SNcToGo", Desc: "D1 dopamine to Go pathway",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.5",
+					"Prjn.WtScale.Rel": "2",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// SNc -> NoGo (D2 dopamine inhibitory)
+			{Sel: "#SNcToNoGo", Desc: "D2 dopamine to NoGo pathway (inhibitory)",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.5",
+					"Prjn.WtScale.Rel": "1.5",
+					"Prjn.Learn.Lrate": "0",
+					"Prjn.WtInit.Mean": "0.5",
+				}},
+			// PMC -> Go (motor cortex to striatum)
+			{Sel: "#PMCToGo", Desc: "motor cortex to Go",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "1",
+					"Prjn.WtScale.Rel": "1.5",
+					"Prjn.Learn.Lrate": "0",
+					"Prjn.Learn.Hebb":  "0.1",
+				}},
+			// PMC -> NoGo (motor cortex to NoGo)
+			{Sel: "#PMCToNoGo", Desc: "motor cortex to NoGo",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.65",
+					"Prjn.WtScale.Rel": "2",
+					"Prjn.Learn.Lrate": "0",
+					"Prjn.Learn.Hebb":  "0.1",
+				}},
+			// NoGo -> Go (lateral inhibition)
+			{Sel: "#NoGoToGo", Desc: "NoGo inhibits Go",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.6",
+					"Prjn.WtScale.Rel": "1.5",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// NoGo -> GPe
+			{Sel: "#NoGoToGPe", Desc: "NoGo inhibits GPe",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.4",
+					"Prjn.WtScale.Rel": "1.2",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// Go -> GPi (direct pathway inhibition)
+			{Sel: "#GoToGPi", Desc: "Go inhibits GPi",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "1",
+					"Prjn.WtScale.Rel": "3",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// GPe -> GPi (indirect pathway inhibition)
+			{Sel: "#GPeToGPi", Desc: "GPe inhibits GPi",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.4",
+					"Prjn.WtScale.Rel": "1.2",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// GPi -> Thalamus (inhibition)
+			{Sel: "#GPiToThalamus", Desc: "GPi inhibits thalamus",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "1",
+					"Prjn.WtScale.Rel": "3",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// Thalamus -> PMC
+			{Sel: "#ThalamusToPMC", Desc: "thalamus excites PMC",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "1",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// PMC -> Thalamus (recurrent)
+			{Sel: "#PMCToThalamus", Desc: "PMC recurrent to thalamus",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.7",
+					"Prjn.WtScale.Rel": "5",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// Input -> PMC (prepotent)
+			{Sel: "#InputToPMC", Desc: "input to premotor cortex prepotent bias",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "5",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// Context -> PMC
+			{Sel: "#ContextToPMC", Desc: "context to premotor cortex",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "3",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// PMC -> Output
+			{Sel: "#PMCToOutput", Desc: "premotor cortex to output",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "1",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			// StriatumInhib projections
+			{Sel: "#StriatumInhibToGo", Desc: "inhib interneurons to Go",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.3",
+					"Prjn.WtScale.Rel": "2",
+					"Prjn.Learn.Lrate": "0",
+				}},
+			{Sel: "#StriatumInhibToNoGo", Desc: "inhib interneurons to NoGo",
+				Params: params.Params{
+					"Prjn.WtScale.Abs": "0.3",
+					"Prjn.WtScale.Rel": "2",
+					"Prjn.Learn.Lrate": "0",
+				}},
+		},
+	}},
+	{Name: "PD", Desc: "Parkinson's Disease: reduced DA (2/4 intact SNc units)", Sheets: params.Sheets{
+		"Sim": &params.Sheet{
+			{Sel: "Sim", Desc: "reduce intact SNc units",
+				Params: params.Params{
+					"Sim.NumIntactSNc": "2",
+				}},
+		},
+	}},
+	{Name: "D2Agonist", Desc: "D2 agonist: increase D2 projection strength", Sheets: params.Sheets{
+		"Sim": &params.Sheet{
+			{Sel: "Sim", Desc: "double D2 strength",
+				Params: params.Params{
+					"Sim.D2Acq":  "0.15",
+					"Sim.D2Perf": "0.15",
+				}},
+		},
+	}},
+}
+
+// Sim encapsulates the entire simulation.
+type Sim struct {
+
+	// network
+	Net *leabra.Network `view:"no-inline" desc:"the network"`
+
+	// ---- task parameters ----
+
+	// NumIntactSNc is the number of intact SNc (dopamine) units (out of 4).
+	// Default 4 = healthy; 2 = simulated Parkinson's disease.
+	NumIntactSNc int `desc:"number of intact SNc units (out of 4) -- 4=healthy, 2=PD"`
+
+	// DA parameters
+	D1Acq  float64 `desc:"D1 projection strength during acquisition"`
+	D1Perf float64 `desc:"D1 projection strength during performance/test"`
+	D2Acq  float64 `desc:"D2 projection strength during acquisition"`
+	D2Perf float64 `desc:"D2 projection strength during performance/test"`
+
+	// DA signal values
+	DABurstVal float64 `desc:"SNc external value for DA burst (reward)"`
+	DADipVal   float64 `desc:"SNc external value for DA dip (punishment)"`
+	TonicDA    float64 `desc:"tonic DA level for neutral outcomes"`
+
+	// PMC bias for candidate responses
+	PMCBias float64 `desc:"bias weight applied to candidate PMC units"`
+
+	// ---- training parameters ----
+	MaxEpochs int `desc:"maximum number of training epochs"`
+	MaxCycles int `desc:"maximum cycles per trial (settling)"`
+	TestEpochs int `desc:"number of test epochs"`
+	NTrialsPerEpoch int `desc:"number of trials per epoch"`
+
+	// ---- noise schedule ----
+	NoiseStart float64 `desc:"starting noise multiplier"`
+
+	// ---- state ----
+	TrainEnv   PSEnv       `desc:"training environment"`
+	TestEnv    PSEnv       `desc:"testing environment"`
+	Time       leabra.Time `desc:"time context"`
+	IsRunning  bool        `view:"-" desc:"true when running"`
+	StopNow    bool        `view:"-" desc:"flag to stop"`
+
+	// ---- logging ----
+	TrnEpcLog  *etable.Table `view:"no-inline" desc:"training epoch log"`
+	TstEpcLog  *etable.Table `view:"no-inline" desc:"testing epoch log"`
+	TrnTrlLog  *etable.Table `view:"no-inline" desc:"training trial log"`
+	TstTrlLog  *etable.Table `view:"no-inline" desc:"testing trial log"`
+	RunLog     *etable.Table `view:"no-inline" desc:"run log"`
+	RunStats   *etable.Table `view:"no-inline" desc:"run stats summary"`
+
+	// ---- plots ----
+	TrnEpcPlot  *eplot.Plot2D `view:"-" desc:"training epoch plot"`
+	TstEpcPlot  *eplot.Plot2D `view:"-" desc:"test epoch plot"`
+	TrnTrlPlot  *eplot.Plot2D `view:"-" desc:"training trial plot"`
+	TstTrlPlot  *eplot.Plot2D `view:"-" desc:"testing trial plot"`
+	RunPlot     *eplot.Plot2D `view:"-" desc:"run plot"`
+
+	// ---- gui ----
+	ViewOn    bool              `desc:"whether to update the network view while running"`
+	TrainUpdt leabra.TimeScales `desc:"at what time scale to update the display during training? Change to AlphaCyc to make display updating go faster"`
+	TestUpdt  leabra.TimeScales `desc:"at what time scale to update the display during testing? Change to AlphaCyc to make display updating go faster"`
+	NetView   *netview.NetView `view:"-" desc:"net view"`
+	Win       *gi.Window       `view:"-" desc:"main gui window"`
+	ToolBar   *gi.ToolBar      `view:"-" desc:"toolbar"`
+	ValsTsr   *etensor.Float32 `view:"-" desc:"temp tensor for vals"`
+
+	// ---- current trial state ----
+	TrialName string  `view:"-" desc:"current trial name"`
+	Action    int     `view:"-" desc:"selected action (1-4, 0=no action)"`
+	Gated     bool    `view:"-" desc:"whether an action was gated by BG"`
+	Rewarded  bool    `view:"-" desc:"whether reward was delivered"`
+	DASignal  float64 `view:"-" desc:"DA signal applied on this trial"`
+
+	// ---- counters ----
+	Epoch   int `view:"-" desc:"current epoch"`
+	Trial   int `view:"-" desc:"current trial"`
+	Run     int `view:"-" desc:"current run"`
+	NRuns   int `desc:"total number of runs"`
+	Testing bool `view:"-" desc:"true during test phase"`
+}
+
+// TheSim is the overall state for this simulation
+var TheSim Sim
+
+// KiT_Sim provides type information for Sim
+var KiT_Sim = kit.Types.AddType(&Sim{}, SimProps)
+
+// New creates a new Sim with default parameters.
+func (ss *Sim) New() {
+	ss.Net = &leabra.Network{}
+	ss.NumIntactSNc = 4
+	ss.D1Acq = 0.6
+	ss.D1Perf = 0.6
+	ss.D2Acq = 0.075
+	ss.D2Perf = 0.075
+	ss.DABurstVal = 1.0
+	ss.DADipVal = 0.0
+	ss.TonicDA = 0.026
+	ss.PMCBias = 3.0
+	ss.MaxEpochs = 30
+	ss.MaxCycles = 150
+	ss.TestEpochs = 30
+	ss.NTrialsPerEpoch = 100
+	ss.NRuns = 50
+	ss.NoiseStart = 1.0
+	ss.ViewOn = true
+	ss.TrainUpdt = leabra.Cycle
+	ss.TestUpdt = leabra.Cycle
+
+	ss.TrnEpcLog = &etable.Table{}
+	ss.TstEpcLog = &etable.Table{}
+	ss.TrnTrlLog = &etable.Table{}
+	ss.TstTrlLog = &etable.Table{}
+	ss.RunLog = &etable.Table{}
+	ss.RunStats = &etable.Table{}
+	ss.ValsTsr = &etensor.Float32{}
+}
+
+// Config configures all elements of the simulation.
+func (ss *Sim) Config() {
+	ss.ConfigEnv()
+	ss.ConfigNet(ss.Net)
+	ss.ConfigLogs()
+}
+
+////////////////////////////////////////////////////////////////////
+//  Environment
+
+// PSEnv implements the Probabilistic Selection task environment.
+type PSEnv struct {
+	Nm       string          `desc:"name of this environment"`
+	TrialCt  int             `desc:"current trial counter"`
+	EpochCt  int             `desc:"current epoch counter"`
+	TrialName string         `desc:"name of the current trial type"`
+	TrialType int            `desc:"0 = 8020_R1R2, 1 = 6040_R3R4"`
+	Order    []int           `view:"-" desc:"shuffled trial order"`
+}
+
+// Name returns the name of the environment
+func (ev *PSEnv) Name() string { return ev.Nm }
+
+// Init initializes the environment for a new run
+func (ev *PSEnv) Init(nTrials int) {
+	ev.TrialCt = 0
+	ev.EpochCt = 0
+	ev.Order = make([]int, nTrials)
+	for i := range ev.Order {
+		ev.Order[i] = i % 2 // alternating 8020 and 6040
+	}
+	ev.Shuffle()
+}
+
+// Shuffle randomizes the trial order
+func (ev *PSEnv) Shuffle() {
+	for i := len(ev.Order) - 1; i > 0; i-- {
+		j := rand.Intn(i + 1)
+		ev.Order[i], ev.Order[j] = ev.Order[j], ev.Order[i]
+	}
+}
+
+// Step advances to the next trial
+func (ev *PSEnv) Step() {
+	if ev.TrialCt >= len(ev.Order) {
+		ev.TrialCt = 0
+		ev.EpochCt++
+		ev.Shuffle()
+	}
+	ev.TrialType = ev.Order[ev.TrialCt]
+	if ev.TrialType == 0 {
+		ev.TrialName = "8020_R1R2"
+	} else {
+		ev.TrialName = "6040_R3R4"
+	}
+	ev.TrialCt++
+}
+
+// ConfigEnv configures the training and testing environments.
+func (ss *Sim) ConfigEnv() {
+	ss.TrainEnv.Nm = "TrainEnv"
+	ss.TrainEnv.Init(ss.NTrialsPerEpoch)
+	ss.TestEnv.Nm = "TestEnv"
+	ss.TestEnv.Init(ss.NTrialsPerEpoch)
+}
+
+////////////////////////////////////////////////////////////////////
+//  Network
+
+// ConfigNet builds the network according to BG_4s_inhib_PS_e7a.proj
+func (ss *Sim) ConfigNet(net *leabra.Network) {
+	net.InitName(net, "BG_PS")
+
+	// ---- Create layers ----
+	// Input: 6x3 (18 units: first 6 = S1, next 12 = S2/shared)
+	inp := net.AddLayer2D("Input", 6, 3, emer.Input)
+	// Context: 9x2 (18 units)
+	ctx := net.AddLayer2D("Context", 9, 2, emer.Input)
+	// Go (D1R MSN): 6x6 (36 units, 4 action pools of 9)
+	goLy := net.AddLayer2D("Go", 6, 6, emer.Hidden)
+	// NoGo (D2R MSN): 6x6 (36 units)
+	nogoLy := net.AddLayer2D("NoGo", 6, 6, emer.Hidden)
+	// Striatum Inhib: 4x4 (16 units)
+	strInhib := net.AddLayer2D("StriatumInhib", 4, 4, emer.Hidden)
+	// GPe: 2x2 (4 units)
+	gpe := net.AddLayer2D("GPe", 2, 2, emer.Hidden)
+	// GPi: 4x2 (8 units)
+	gpi := net.AddLayer2D("GPi", 4, 2, emer.Hidden)
+	// Thalamus: 2x2 (4 units)
+	thal := net.AddLayer2D("Thalamus", 2, 2, emer.Hidden)
+	// PMC: 4x2 (8 units: 2 per action channel)
+	pmc := net.AddLayer2D("PMC", 4, 2, emer.Hidden)
+	// Output: 4x1 (4 units)
+	out := net.AddLayer2D("Output", 4, 1, emer.Compare)
+	// SNc: 2x2 (4 units -- externally clamped)
+	snc := net.AddLayer2D("SNc", 2, 2, emer.Input)
+
+	// ---- Create projections ----
+	full := prjn.NewFull()
+	_ = full
+
+	// Input -> Go (D1 learning pathway)
+	net.ConnectLayers(inp, goLy, full, emer.Forward).SetClass("InputToGo")
+	// Input -> NoGo (D2 learning pathway)
+	net.ConnectLayers(inp, nogoLy, full, emer.Forward).SetClass("InputToNoGo")
+	// Context -> Go
+	net.ConnectLayers(ctx, goLy, full, emer.Forward).SetClass("ContextToGo")
+	// Context -> NoGo
+	net.ConnectLayers(ctx, nogoLy, full, emer.Forward).SetClass("ContextToNoGo")
+	// SNc -> Go (D1 dopamine modulation)
+	net.ConnectLayers(snc, goLy, full, emer.Forward).SetClass("SNcToGo")
+	// SNc -> NoGo (D2 dopamine modulation -- inhibitory)
+	net.ConnectLayers(snc, nogoLy, full, emer.Inhib).SetClass("SNcToNoGo")
+	// PMC -> Go (motor cortex feedback to Go)
+	net.ConnectLayers(pmc, goLy, full, emer.Forward).SetClass("PMCToGo")
+	// PMC -> NoGo (motor cortex feedback to NoGo)
+	net.ConnectLayers(pmc, nogoLy, full, emer.Forward).SetClass("PMCToNoGo")
+	// NoGo -> Go (lateral inhibition)
+	net.ConnectLayers(nogoLy, goLy, full, emer.Inhib).SetClass("NoGoToGo")
+	// StriatumInhib -> Go
+	net.ConnectLayers(strInhib, goLy, full, emer.Inhib).SetClass("StriatumInhibToGo")
+	// StriatumInhib -> NoGo
+	net.ConnectLayers(strInhib, nogoLy, full, emer.Inhib).SetClass("StriatumInhibToNoGo")
+
+	// Input -> StriatumInhib
+	net.ConnectLayers(inp, strInhib, full, emer.Forward).SetClass("FFtoInhib")
+	// Context -> StriatumInhib
+	net.ConnectLayers(ctx, strInhib, full, emer.Forward).SetClass("FFtoInhib")
+	// Go -> StriatumInhib (feedback)
+	net.ConnectLayers(goLy, strInhib, full, emer.Forward).SetClass("FBtoInhib")
+	// NoGo -> StriatumInhib (feedback)
+	net.ConnectLayers(nogoLy, strInhib, full, emer.Forward).SetClass("FBtoInhib")
+	// SNc -> StriatumInhib
+	net.ConnectLayers(snc, strInhib, full, emer.Forward).SetClass("SNcToGo")
+	// PMC -> StriatumInhib
+	net.ConnectLayers(pmc, strInhib, full, emer.Forward).SetClass("PMCToGo")
+	// StriatumInhib self-inhibition
+	net.ConnectLayers(strInhib, strInhib, full, emer.Inhib).SetClass("InhibInhib")
+
+	// NoGo -> GPe (inhibitory)
+	net.ConnectLayers(nogoLy, gpe, full, emer.Inhib).SetClass("NoGoToGPe")
+	// Go -> GPi (direct pathway, inhibitory)
+	net.ConnectLayers(goLy, gpi, full, emer.Inhib).SetClass("GoToGPi")
+	// GPe -> GPi (indirect pathway, inhibitory)
+	net.ConnectLayers(gpe, gpi, full, emer.Inhib).SetClass("GPeToGPi")
+	// GPi -> Thalamus (inhibitory)
+	net.ConnectLayers(gpi, thal, full, emer.Inhib).SetClass("GPiToThalamus")
+	// Thalamus -> PMC
+	net.ConnectLayers(thal, pmc, full, emer.Forward).SetClass("ThalamusToPMC")
+	// PMC -> Thalamus (recurrent)
+	net.ConnectLayers(pmc, thal, full, emer.Forward).SetClass("PMCToThalamus")
+	// Input -> PMC (prepotent response bias)
+	net.ConnectLayers(inp, pmc, full, emer.Forward).SetClass("InputToPMC")
+	// Context -> PMC
+	net.ConnectLayers(ctx, pmc, full, emer.Forward).SetClass("ContextToPMC")
+	// PMC -> Output
+	net.ConnectLayers(pmc, out, full, emer.Forward).SetClass("PMCToOutput")
+
+	// ---- Layout ----
+	// Layer positions set by default layout
+
+	net.Defaults()
+	ss.SetParams("Network", false)
+	net.Build()
+	net.InitWts()
+}
+
+////////////////////////////////////////////////////////////////////
+//  Params
+
+// SetParams sets parameters from the ParamSets
+func (ss *Sim) SetParams(sheet string, setMsg bool) {
+	for _, ps := range ParamSets {
+		if ps.Name != "Base" {
+			continue
+		}
+		sh, ok := ps.Sheets[sheet]
+		if !ok {
+			continue
+		}
+		sh.Apply(ss.Net, setMsg)
+	}
+}
+
+////////////////////////////////////////////////////////////////////
+//  Init
+
+// Init restarts the simulation.
+func (ss *Sim) Init() {
+	rand.Seed(time.Now().UnixNano())
+	ss.ConfigEnv()
+	ss.Time.Reset()
+	ss.Net.InitWts()
+	ss.Epoch = 0
+	ss.Trial = 0
+	ss.StopNow = false
+	ss.UpdateView(true)
+}
+
+// NewRndSeed sets a new random seed
+func (ss *Sim) NewRndSeed() {
+	rand.Seed(time.Now().UnixNano())
+}
+
+////////////////////////////////////////////////////////////////////
+//  Running
+
+// Stop stops the simulation
+func (ss *Sim) Stop() {
+	ss.StopNow = true
+}
+
+// Stopped is called when a run method stops running -- updates the IsRunning flag and toolbar
+func (ss *Sim) Stopped() {
+	ss.IsRunning = false
+	if ss.Win != nil {
+		vp := ss.Win.WinViewport2D()
+		vp.BlockUpdates()
+		if ss.ToolBar != nil {
+			ss.ToolBar.UpdateActions()
+		}
+		vp.UnblockUpdates()
+		vp.SetNeedsFullRender()
+	}
+}
+
+// ApplyInputs applies the stimulus pattern for the current trial.
+func (ss *Sim) ApplyInputs() {
+	net := ss.Net
+	var ev *PSEnv
+	if ss.Testing {
+		ev = &ss.TestEnv
+	} else {
+		ev = &ss.TrainEnv
+	}
+	ss.TrialName = ev.TrialName
+
+	// Input layer: 6x3 = 18 units
+	inp := net.LayerByName("Input").(*leabra.Layer)
+	ctx := net.LayerByName("Context").(*leabra.Layer)
+	snc := net.LayerByName("SNc").(*leabra.Layer)
+	pmc := net.LayerByName("PMC").(*leabra.Layer)
+
+	// Clear all inputs
+	inp.SetType(emer.Input)
+	ctx.SetType(emer.Input)
+	snc.SetType(emer.Input)
+
+	// Input patterns from the old proj file:
+	// 8020_R1R2: Input=[0,0,0,0,0,0, 1,1,1,1,1,1, x,x,x,x,x,x] -- S1 in second row
+	// 6040_R3R4: Input=[1,1,1,1,1,1, x,x,x,x,x,x, 0,0,0,0,0,0] -- S2 in first row
+	inpPat := make([]float32, 18)
+	ctxPat := make([]float32, 18)
+
+	if ev.TrialType == 0 { // 8020_R1R2
+		// S1: units 6-11 active (second block of 6)
+		for i := 6; i < 12; i++ {
+			inpPat[i] = 1.0
+		}
+		// Context for S1: first 3 of 18
+		for i := 0; i < 6; i++ {
+			ctxPat[i] = 1.0
+		}
+	} else { // 6040_R3R4
+		// S2: units 12-17 active (third block of 6) -- note: the proj file has different layout
+		// Actually from proj: row0=[1,1,1,1,1,1, 0,0,0,0,0,0] for 6040
+		for i := 0; i < 6; i++ {
+			inpPat[i] = 1.0
+		}
+		for i := 6; i < 12; i++ {
+			ctxPat[i] = 1.0
+		}
+	}
+
+	// Apply Input pattern
+	inpTsr := etensor.NewFloat32([]int{6, 3}, nil, nil)
+	copy(inpTsr.Values, inpPat)
+	inp.ApplyExt(inpTsr)
+
+	// Apply Context pattern
+	ctxTsr := etensor.NewFloat32([]int{9, 2}, nil, nil)
+	copy(ctxTsr.Values, ctxPat)
+	ctx.ApplyExt(ctxTsr)
+
+	// Apply tonic DA to SNc at start of minus phase
+	sncTsr := etensor.NewFloat32([]int{2, 2}, nil, nil)
+	for i := 0; i < 4; i++ {
+		sncTsr.Values[i] = float32(ss.TonicDA)
+	}
+	snc.ApplyExt(sncTsr)
+
+	// Apply PMC bias: bias the two candidate responses for this trial
+	// 8020: bias units 0,1,4,5 (R1,R2 channels -- pairs of PMC units per action)
+	// 6040: bias units 2,3,6,7 (R3,R4 channels)
+	// Clear PMC bias from prior trials (resetting external input)
+	for i := 0; i < 8; i++ {
+		pmc.Neurons[i].Ext = 0
+	}
+
+	// Store trial info
+	ss.Action = 0
+	ss.Gated = false
+	ss.Rewarded = false
+	ss.DASignal = ss.TonicDA
+}
+
+// ApplyPMCBias applies bias weights to candidate PMC units.
+// In the original model this was done via bias weight manipulation.
+// Here we approximate by adding extra excitatory input to candidate units.
+func (ss *Sim) ApplyPMCBias() {
+	pmc := ss.Net.LayerByName("PMC").(*leabra.Layer)
+	var ev *PSEnv
+	if ss.Testing {
+		ev = &ss.TestEnv
+	} else {
+		ev = &ss.TrainEnv
+	}
+	if ev.TrialType == 0 { // 8020_R1R2: R1(units 0,4) and R2(units 1,5) are candidates
+		pmc.Neurons[0].Ext = float32(ss.PMCBias)
+		pmc.Neurons[1].Ext = float32(ss.PMCBias)
+		pmc.Neurons[4].Ext = float32(ss.PMCBias)
+		pmc.Neurons[5].Ext = float32(ss.PMCBias)
+	} else { // 6040_R3R4: R3(units 2,6) and R4(units 3,7)
+		pmc.Neurons[2].Ext = float32(ss.PMCBias)
+		pmc.Neurons[3].Ext = float32(ss.PMCBias)
+		pmc.Neurons[6].Ext = float32(ss.PMCBias)
+		pmc.Neurons[7].Ext = float32(ss.PMCBias)
+	}
+}
+
+// DetermineAction reads PMC minus-phase activations to determine selected action.
+// Action selection is based on summed activation of paired PMC units.
+// PMC has 8 units: pairs (0,4)=R1, (1,5)=R2, (2,6)=R3, (3,7)=R4
+func (ss *Sim) DetermineAction() {
+	pmc := ss.Net.LayerByName("PMC").(*leabra.Layer)
+	out := ss.Net.LayerByName("Output").(*leabra.Layer)
+
+	// Read minus-phase activations from PMC
+	actM := make([]float64, 8)
+	for i := 0; i < 8; i++ {
+		actM[i] = float64(pmc.Neurons[i].ActM)
+	}
+
+	// Sum paired units for each action
+	actionActs := [4]float64{
+		actM[0] + actM[4], // R1
+		actM[1] + actM[5], // R2
+		actM[2] + actM[6], // R3
+		actM[3] + actM[7], // R4
+	}
+
+	// Check if Output layer has any activation (indicates gating occurred)
+	maxOut := float32(0)
+	for i := 0; i < len(out.Neurons); i++ {
+		if out.Neurons[i].Act > maxOut {
+			maxOut = out.Neurons[i].Act
+		}
+	}
+
+	if maxOut > 0 {
+		// Find the winning action
+		maxAct := actionActs[0]
+		maxIdx := 0
+		for i := 1; i < 4; i++ {
+			if actionActs[i] > maxAct {
+				maxAct = actionActs[i]
+				maxIdx = i
+			}
+		}
+		ss.Action = maxIdx + 1 // 1-4
+		ss.Gated = true
+	} else {
+		// No gating -- response determined purely by noise/cortical dynamics
+		// Still pick the max as the "selected" action
+		maxAct := actionActs[0]
+		maxIdx := 0
+		for i := 1; i < 4; i++ {
+			if actionActs[i] > maxAct {
+				maxAct = actionActs[i]
+				maxIdx = i
+			}
+		}
+		ss.Action = maxIdx + 1
+		ss.Gated = false
+	}
+
+	// Clamp the winning action into PMC for plus phase (bias weight = 10)
+	// This ensures plus-phase reflects the chosen action
+	for i := 0; i < 8; i++ {
+		pmc.Neurons[i].Ext = 0
+	}
+	winIdx := ss.Action - 1
+	pmc.Neurons[winIdx].Ext = 10
+	pmc.Neurons[winIdx+4].Ext = 10
+}
+
+// DeliverReward determines reward based on selected action and trial type,
+// then sets SNc DA values accordingly.
+func (ss *Sim) DeliverReward() {
+	var ev *PSEnv
+	if ss.Testing {
+		ev = &ss.TestEnv
+	} else {
+		ev = &ss.TrainEnv
+	}
+
+	snc := ss.Net.LayerByName("SNc").(*leabra.Layer)
+	k := float64(ss.NumIntactSNc) / 4.0
+
+	var rewarded bool
+	var daVal float64
+
+	if strings.Contains(ev.TrialName, "8020") {
+		switch ss.Action {
+		case 1: // R1: 80% reward
+			if rand.Intn(10) > 1 { // >1 means 80% chance
+				rewarded = true
+			}
+		case 2: // R2: 20% reward (80% punishment)
+			if rand.Intn(10) > 7 { // >7 means 20% chance
+				rewarded = true
+			}
+		default: // R3/R4 during 8020 = neutral
+			daVal = ss.TonicDA
+		}
+	} else if strings.Contains(ev.TrialName, "6040") {
+		switch ss.Action {
+		case 3: // R3: 60% reward
+			if rand.Intn(10) > 3 { // values 4-9 = 60% chance, matching original proj IntZeroN(10)>3
+				rewarded = true
+			}
+		case 4: // R4: 40% reward
+			if rand.Intn(10) > 5 { // >5 means 40% chance
+				rewarded = true
+			}
+		default: // R1/R2 during 6040 = neutral
+			daVal = ss.TonicDA
+		}
+	}
+
+	if ss.Action >= 1 && ss.Action <= 4 {
+		if ev.TrialType == 0 && (ss.Action == 1 || ss.Action == 2) {
+			if rewarded {
+				daVal = ss.DABurstVal
+			} else {
+				daVal = ss.DADipVal
+			}
+		} else if ev.TrialType == 1 && (ss.Action == 3 || ss.Action == 4) {
+			if rewarded {
+				daVal = ss.DABurstVal
+			} else {
+				daVal = ss.DADipVal
+			}
+		} else {
+			daVal = ss.TonicDA
+		}
+	}
+
+	ss.Rewarded = rewarded
+	ss.DASignal = daVal
+
+	// Apply DA to SNc units
+	nSNc := len(snc.Neurons)
+	sncTsr := etensor.NewFloat32([]int{2, 2}, nil, nil)
+	for i := 0; i < nSNc; i++ {
+		if i < int(k*float64(nSNc)) {
+			sncTsr.Values[i] = float32(daVal)
+		} else {
+			sncTsr.Values[i] = 0 // damaged units
+		}
+	}
+	snc.ApplyExt(sncTsr)
+}
+
+// NoiseMultiplier returns the noise multiplier for the current epoch
+// based on the noise schedule from the original model.
+// Schedule: epoch 0-25: 1.0, 25-80: ramp 1.0->0.5, 80-100: ramp 0.5->0.2
+func (ss *Sim) NoiseMultiplier() float64 {
+	ep := ss.Epoch
+	if ep <= 25 {
+		return 1.0
+	} else if ep <= 80 {
+		return 1.0 - float64(ep-25)*0.5/55.0
+	} else if ep <= 100 {
+		return 0.5 - float64(ep-80)*0.3/20.0
+	}
+	return 0.2
+}
+
+// Counters returns a string of current counter values for display.
+func (ss *Sim) Counters() string {
+	return fmt.Sprintf("Epoch:\t%d\tTrial:\t%d\tCycle:\t%d\tAction:\t%d", ss.Epoch, ss.Trial, ss.Time.Cycle, ss.Action)
+}
+
+// AlphaCyc runs one alpha-cycle (trial) of settling.
+// Uses the standard quarter-based leabra settling:
+// Q1-Q3 = minus phase, Q4 = plus phase.
+// After minus phase (end of Q3), we determine action and deliver DA.
+func (ss *Sim) AlphaCyc() {
+	net := ss.Net
+	viewUpdt := ss.TrainUpdt
+	if ss.Testing {
+		viewUpdt = ss.TestUpdt
+	}
+
+	// Update prior weight changes at start
+	if !ss.Testing {
+		net.WtFmDWt()
+	}
+
+	net.AlphaCycInit(true) // true = learning
+	ss.Time.AlphaCycStart()
+
+	// Run all 4 quarters
+	for qtr := 0; qtr < 4; qtr++ {
+		for cyc := 0; cyc < ss.Time.CycPerQtr; cyc++ {
+			net.Cycle(&ss.Time)
+			ss.Time.CycleInc()
+			if ss.ViewOn {
+				switch viewUpdt {
+				case leabra.Cycle:
+					ss.UpdateView(!ss.Testing)
+				case leabra.FastSpike:
+					if (cyc+1)%10 == 0 {
+						ss.UpdateView(!ss.Testing)
+					}
+				}
+			}
+		}
+
+		// After Q3 (minus phase complete), determine action and set DA for plus phase
+		if qtr == 2 {
+			ss.DetermineAction()
+			if !ss.Testing {
+				ss.DeliverReward()
+			} else {
+				snc := net.LayerByName("SNc").(*leabra.Layer)
+				sncTsr := etensor.NewFloat32([]int{2, 2}, nil, nil)
+				for i := 0; i < 4; i++ {
+					sncTsr.Values[i] = float32(ss.TonicDA)
+				}
+				snc.ApplyExt(sncTsr)
+				ss.DASignal = ss.TonicDA
+				ss.Rewarded = false
+			}
+		}
+
+		net.QuarterFinal(&ss.Time)
+		ss.Time.QuarterInc()
+		if ss.ViewOn {
+			switch {
+			case viewUpdt <= leabra.Quarter:
+				ss.UpdateView(!ss.Testing)
+			case viewUpdt == leabra.Phase:
+				if qtr >= 2 {
+					ss.UpdateView(!ss.Testing)
+				}
+			}
+		}
+	}
+
+	// Learning (only during training)
+	if !ss.Testing {
+		net.DWt()
+		// Apply DA modulation to learning:
+		// Go pathway weights are modulated by DA (D1 rule)
+		// NoGo pathway weights are modulated by -DA (D2 rule)
+		ss.ApplyDAModulation()
+	}
+
+	if ss.ViewOn && viewUpdt == leabra.AlphaCycle {
+		ss.UpdateView(!ss.Testing)
+	}
+}
+
+// ApplyDAModulation modulates the weight changes in Go and NoGo pathways
+// based on the DA signal, implementing the D1/D2 learning rule.
+func (ss *Sim) ApplyDAModulation() {
+	net := ss.Net
+	da := ss.DASignal - ss.TonicDA // phasic DA = total - tonic
+
+	goLy := net.LayerByName("Go").(*leabra.Layer)
+	nogoLy := net.LayerByName("NoGo").(*leabra.Layer)
+
+	// Modulate Go (D1) pathway: positive DA -> more learning
+	for pi := 0; pi < goLy.NRecvPrjns(); pi++ {
+		pj := goLy.RecvPrjn(pi).(*leabra.Prjn)
+		if pj.Off {
+			continue
+		}
+		clNm := pj.Cls
+		if strings.Contains(clNm, "Input") || strings.Contains(clNm, "Context") {
+			// DA-modulated learning pathways
+			for si := range pj.Syns {
+				if da > 0 {
+					pj.Syns[si].DWt *= float32(1.0 + da)
+				} else {
+					pj.Syns[si].DWt *= float32(math.Max(0, 1.0+da))
+				}
+			}
+		}
+	}
+
+	// Modulate NoGo (D2) pathway: negative DA -> more learning (reversed)
+	for pi := 0; pi < nogoLy.NRecvPrjns(); pi++ {
+		pj := nogoLy.RecvPrjn(pi).(*leabra.Prjn)
+		if pj.Off {
+			continue
+		}
+		clNm := pj.Cls
+		if strings.Contains(clNm, "Input") || strings.Contains(clNm, "Context") {
+			for si := range pj.Syns {
+				if da < 0 {
+					pj.Syns[si].DWt *= float32(1.0 + math.Abs(da))
+				} else {
+					pj.Syns[si].DWt *= float32(math.Max(0, 1.0-da))
+				}
+			}
+		}
+	}
+}
+
+// TrainTrial runs one training trial.
+func (ss *Sim) TrainTrial() {
+	ss.Testing = false
+	ss.TrainEnv.Step()
+	ss.TrialName = ss.TrainEnv.TrialName
+	ss.ApplyInputs()
+	ss.ApplyPMCBias()
+	ss.AlphaCyc()
+	ss.LogTrnTrl(ss.TrnTrlLog)
+}
+
+// TrainEpoch runs one training epoch.
+func (ss *Sim) TrainEpoch() {
+	ss.StopNow = false
+	for trl := 0; trl < ss.NTrialsPerEpoch; trl++ {
+		ss.Trial = trl
+		ss.TrainTrial()
+		if ss.StopNow {
+			break
+		}
+	}
+	ss.LogTrnEpc(ss.TrnEpcLog)
+	ss.Epoch++
+	ss.Stopped()
+}
+
+// Train runs the full training procedure.
+func (ss *Sim) Train() {
+	ss.IsRunning = true
+	ss.StopNow = false
+	// Set D1/D2 strength for acquisition
+	ss.SetDAStrength(ss.D1Acq, ss.D2Acq)
+	for ep := 0; ep < ss.MaxEpochs; ep++ {
+		ss.TrainEpoch()
+		if ss.StopNow {
+			break
+		}
+	}
+	ss.Stopped()
+}
+
+// TestTrial runs one test trial.
+func (ss *Sim) TestTrial() {
+	ss.Testing = true
+	ss.TestEnv.Step()
+	ss.TrialName = ss.TestEnv.TrialName
+	ss.ApplyInputs()
+	ss.ApplyPMCBias()
+	ss.AlphaCyc()
+	ss.LogTstTrl(ss.TstTrlLog)
+}
+
+// TestEpoch runs one test epoch.
+func (ss *Sim) TestEpoch() {
+	ss.StopNow = false
+	for trl := 0; trl < ss.NTrialsPerEpoch; trl++ {
+		ss.Trial = trl
+		ss.TestTrial()
+		if ss.StopNow {
+			break
+		}
+	}
+	ss.LogTstEpc(ss.TstEpcLog)
+	ss.Stopped()
+}
+
+// Test runs the test phase.
+func (ss *Sim) Test() {
+	ss.IsRunning = true
+	ss.StopNow = false
+	// Set D1/D2 strength for performance
+	ss.SetDAStrength(ss.D1Perf, ss.D2Perf)
+	for ep := 0; ep < ss.TestEpochs; ep++ {
+		ss.TestEpoch()
+		if ss.StopNow {
+			break
+		}
+	}
+	ss.Stopped()
+}
+
+// TrainAndTest runs the full train+test cycle.
+func (ss *Sim) TrainAndTest() {
+	ss.Train()
+	if !ss.StopNow {
+		ss.Test()
+	}
+}
+
+// RunBatch runs multiple networks (for batch statistics).
+func (ss *Sim) RunBatch() {
+	ss.IsRunning = true
+	ss.StopNow = false
+	for run := 0; run < ss.NRuns; run++ {
+		ss.Run = run
+		ss.Init()
+		ss.TrainAndTest()
+		ss.LogRun(ss.RunLog)
+		if ss.StopNow {
+			break
+		}
+	}
+	ss.Stopped()
+}
+
+// SetDAStrength sets the D1 and D2 projection strengths.
+func (ss *Sim) SetDAStrength(d1, d2 float64) {
+	net := ss.Net
+	goLy := net.LayerByName("Go").(*leabra.Layer)
+	nogoLy := net.LayerByName("NoGo").(*leabra.Layer)
+
+	// Set SNc->Go (D1) wt_scale.abs
+	for pi := 0; pi < goLy.NRecvPrjns(); pi++ {
+		pj := goLy.RecvPrjn(pi).(*leabra.Prjn)
+		if strings.Contains(pj.Cls, "SNcToGo") {
+			pj.WtScale.Abs = float32(d1)
+		}
+	}
+	// Set SNc->NoGo (D2) wt_scale.abs
+	for pi := 0; pi < nogoLy.NRecvPrjns(); pi++ {
+		pj := nogoLy.RecvPrjn(pi).(*leabra.Prjn)
+		if strings.Contains(pj.Cls, "SNcToNoGo") {
+			pj.WtScale.Abs = float32(d2)
+		}
+	}
+}
+
+////////////////////////////////////////////////////////////////////
+//  Logging
+
+// ConfigLogs sets up all log tables.
+func (ss *Sim) ConfigLogs() {
+	ss.ConfigTrnTrlLog(ss.TrnTrlLog)
+	ss.ConfigTrnEpcLog(ss.TrnEpcLog)
+	ss.ConfigTstTrlLog(ss.TstTrlLog)
+	ss.ConfigTstEpcLog(ss.TstEpcLog)
+	ss.ConfigRunLog(ss.RunLog)
+}
+
+// ConfigTrnTrlLog configures the training trial log.
+func (ss *Sim) ConfigTrnTrlLog(dt *etable.Table) {
+	dt.SetMetaData("name", "TrnTrlLog")
+	dt.SetMetaData("desc", "Record of each training trial")
+	dt.SetMetaData("read-only", "true")
+	sch := etable.Schema{
+		{"Epoch", etensor.INT64, nil, nil},
+		{"Trial", etensor.INT64, nil, nil},
+		{"TrialName", etensor.STRING, nil, nil},
+		{"Action", etensor.INT64, nil, nil},
+		{"Gated", etensor.BOOL, nil, nil},
+		{"Rewarded", etensor.BOOL, nil, nil},
+		{"DA", etensor.FLOAT64, nil, nil},
+	}
+	dt.SetFromSchema(sch, 0)
+}
+
+// LogTrnTrl logs one training trial.
+func (ss *Sim) LogTrnTrl(dt *etable.Table) {
+	row := dt.Rows
+	dt.SetNumRows(row + 1)
+	dt.SetCellFloat("Epoch", row, float64(ss.Epoch))
+	dt.SetCellFloat("Trial", row, float64(ss.Trial))
+	dt.SetCellString("TrialName", row, ss.TrialName)
+	dt.SetCellFloat("Action", row, float64(ss.Action))
+	if ss.Gated {
+		dt.SetCellFloat("Gated", row, 1)
+	} else {
+		dt.SetCellFloat("Gated", row, 0)
+	}
+	if ss.Rewarded {
+		dt.SetCellFloat("Rewarded", row, 1)
+	} else {
+		dt.SetCellFloat("Rewarded", row, 0)
+	}
+	dt.SetCellFloat("DA", row, ss.DASignal)
+}
+
+// ConfigTstTrlLog configures the testing trial log.
+func (ss *Sim) ConfigTstTrlLog(dt *etable.Table) {
+	dt.SetMetaData("name", "TstTrlLog")
+	dt.SetMetaData("desc", "Record of each test trial")
+	dt.SetMetaData("read-only", "true")
+	sch := etable.Schema{
+		{"Epoch", etensor.INT64, nil, nil},
+		{"Trial", etensor.INT64, nil, nil},
+		{"TrialName", etensor.STRING, nil, nil},
+		{"Action", etensor.INT64, nil, nil},
+		{"Gated", etensor.BOOL, nil, nil},
+	}
+	dt.SetFromSchema(sch, 0)
+}
+
+// LogTstTrl logs one test trial.
+func (ss *Sim) LogTstTrl(dt *etable.Table) {
+	row := dt.Rows
+	dt.SetNumRows(row + 1)
+	dt.SetCellFloat("Epoch", row, float64(ss.Epoch))
+	dt.SetCellFloat("Trial", row, float64(ss.Trial))
+	dt.SetCellString("TrialName", row, ss.TrialName)
+	dt.SetCellFloat("Action", row, float64(ss.Action))
+	if ss.Gated {
+		dt.SetCellFloat("Gated", row, 1)
+	} else {
+		dt.SetCellFloat("Gated", row, 0)
+	}
+}
+
+// ConfigTrnEpcLog configures the training epoch log.
+func (ss *Sim) ConfigTrnEpcLog(dt *etable.Table) {
+	dt.SetMetaData("name", "TrnEpcLog")
+	dt.SetMetaData("desc", "Record per training epoch")
+	dt.SetMetaData("read-only", "true")
+	sch := etable.Schema{
+		{"Epoch", etensor.INT64, nil, nil},
+		{"PctR1_8020", etensor.FLOAT64, nil, nil},
+		{"PctR3_6040", etensor.FLOAT64, nil, nil},
+		{"PctGated", etensor.FLOAT64, nil, nil},
+		{"PctRewarded", etensor.FLOAT64, nil, nil},
+	}
+	dt.SetFromSchema(sch, 0)
+}
+
+// LogTrnEpc computes and logs training epoch summary stats.
+func (ss *Sim) LogTrnEpc(dt *etable.Table) {
+	row := dt.Rows
+	dt.SetNumRows(row + 1)
+	dt.SetCellFloat("Epoch", row, float64(ss.Epoch))
+
+	trl := ss.TrnTrlLog
+	nRows := trl.Rows
+	startRow := nRows - ss.NTrialsPerEpoch
+	if startRow < 0 {
+		startRow = 0
+	}
+
+	// Count action selections for this epoch
+	var n8020, nR1, n6040, nR3, nGated, nRew, nTotal int
+	for r := startRow; r < nRows; r++ {
+		tn := trl.CellString("TrialName", r)
+		act := int(trl.CellFloat("Action", r))
+		gated := trl.CellFloat("Gated", r) > 0.5
+		rew := trl.CellFloat("Rewarded", r) > 0.5
+		nTotal++
+		if gated {
+			nGated++
+		}
+		if rew {
+			nRew++
+		}
+		if strings.Contains(tn, "8020") {
+			n8020++
+			if act == 1 && gated {
+				nR1++
+			}
+		} else if strings.Contains(tn, "6040") {
+			n6040++
+			if act == 3 && gated {
+				nR3++
+			}
+		}
+	}
+
+	pctR1 := 0.0
+	if n8020 > 0 {
+		pctR1 = float64(nR1) / float64(n8020)
+	}
+	pctR3 := 0.0
+	if n6040 > 0 {
+		pctR3 = float64(nR3) / float64(n6040)
+	}
+	pctGated := 0.0
+	if nTotal > 0 {
+		pctGated = float64(nGated) / float64(nTotal)
+	}
+	pctRew := 0.0
+	if nTotal > 0 {
+		pctRew = float64(nRew) / float64(nTotal)
+	}
+
+	dt.SetCellFloat("PctR1_8020", row, pctR1)
+	dt.SetCellFloat("PctR3_6040", row, pctR3)
+	dt.SetCellFloat("PctGated", row, pctGated)
+	dt.SetCellFloat("PctRewarded", row, pctRew)
+
+	if ss.TrnEpcPlot != nil {
+		ss.TrnEpcPlot.GoUpdate()
+	}
+}
+
+// ConfigTstEpcLog configures the testing epoch log.
+func (ss *Sim) ConfigTstEpcLog(dt *etable.Table) {
+	dt.SetMetaData("name", "TstEpcLog")
+	dt.SetMetaData("desc", "Record per test epoch")
+	dt.SetMetaData("read-only", "true")
+	sch := etable.Schema{
+		{"Epoch", etensor.INT64, nil, nil},
+		{"ChooseA", etensor.FLOAT64, nil, nil},
+		{"AvoidB", etensor.FLOAT64, nil, nil},
+		{"PctGated", etensor.FLOAT64, nil, nil},
+	}
+	dt.SetFromSchema(sch, 0)
+}
+
+// LogTstEpc computes and logs test epoch summary statistics.
+// Choose-A: select R1 over R3/R4 when both S1+S2 present
+// Avoid-B: avoid R2 compared to R3/R4
+// For simplicity in this version, we compute:
+// Choose-A ~ fraction choosing R1 on 8020 trials
+// Avoid-B ~ fraction avoiding R2 on 8020 trials (choosing anything else)
+func (ss *Sim) LogTstEpc(dt *etable.Table) {
+	row := dt.Rows
+	dt.SetNumRows(row + 1)
+
+	trl := ss.TstTrlLog
+	nRows := trl.Rows
+	startRow := nRows - ss.NTrialsPerEpoch
+	if startRow < 0 {
+		startRow = 0
+	}
+
+	var n8020, nR1, nNotR2, n8020Gated, nGated, nTotal int
+	for r := startRow; r < nRows; r++ {
+		tn := trl.CellString("TrialName", r)
+		act := int(trl.CellFloat("Action", r))
+		gated := trl.CellFloat("Gated", r) > 0.5
+		nTotal++
+		if gated {
+			nGated++
+		}
+		if strings.Contains(tn, "8020") && gated {
+			n8020++
+			n8020Gated++
+			if act == 1 {
+				nR1++
+			}
+			if act != 2 {
+				nNotR2++
+			}
+		}
+	}
+
+	chooseA := 0.0
+	if n8020 > 0 {
+		chooseA = float64(nR1) / float64(n8020)
+	}
+	avoidB := 0.0
+	if n8020 > 0 {
+		avoidB = float64(nNotR2) / float64(n8020)
+	}
+	pctGated := 0.0
+	if nTotal > 0 {
+		pctGated = float64(nGated) / float64(nTotal)
+	}
+
+	dt.SetCellFloat("Epoch", row, float64(ss.Epoch))
+	dt.SetCellFloat("ChooseA", row, chooseA)
+	dt.SetCellFloat("AvoidB", row, avoidB)
+	dt.SetCellFloat("PctGated", row, pctGated)
+
+	if ss.TstEpcPlot != nil {
+		ss.TstEpcPlot.GoUpdate()
+	}
+}
+
+// ConfigRunLog configures the run-level log.
+func (ss *Sim) ConfigRunLog(dt *etable.Table) {
+	dt.SetMetaData("name", "RunLog")
+	dt.SetMetaData("desc", "Summary per run")
+	dt.SetMetaData("read-only", "true")
+	sch := etable.Schema{
+		{"Run", etensor.INT64, nil, nil},
+		{"ChooseA", etensor.FLOAT64, nil, nil},
+		{"AvoidB", etensor.FLOAT64, nil, nil},
+		{"PctGated", etensor.FLOAT64, nil, nil},
+	}
+	dt.SetFromSchema(sch, 0)
+}
+
+// LogRun logs summary statistics for one completed run.
+func (ss *Sim) LogRun(dt *etable.Table) {
+	row := dt.Rows
+	dt.SetNumRows(row + 1)
+	dt.SetCellFloat("Run", row, float64(ss.Run))
+
+	// Average the test epoch stats
+	tst := ss.TstEpcLog
+	if tst.Rows == 0 {
+		return
+	}
+	ix := etable.NewIdxView(tst)
+	chooseA := agg.Mean(ix, "ChooseA")[0]
+	avoidB := agg.Mean(ix, "AvoidB")[0]
+	pctGated := agg.Mean(ix, "PctGated")[0]
+	dt.SetCellFloat("ChooseA", row, chooseA)
+	dt.SetCellFloat("AvoidB", row, avoidB)
+	dt.SetCellFloat("PctGated", row, pctGated)
+
+	if ss.RunPlot != nil {
+		ss.RunPlot.GoUpdate()
+	}
+}
+
+////////////////////////////////////////////////////////////////////
+//  GUI
+
+// UpdateView updates the network view if available.
+func (ss *Sim) UpdateView(train bool) {
+	if ss.NetView != nil && ss.NetView.IsVisible() {
+		ss.NetView.Record(ss.Counters(), ss.Time.CycleTot)
+		ss.NetView.GoUpdate()
+	}
+}
+
+// ConfigGui creates and configures the main GUI window.
+func (ss *Sim) ConfigGui() *gi.Window {
+	width := 1600
+	height := 1200
+
+	gi.SetAppName("bg_action")
+	gi.SetAppAbout("BG Action Selection Model -- Probabilistic Selection Task\nTranslated from BG_4s_inhib_PS_e7a.proj (emergent 7)")
+
+	win := gi.NewMainWindow("bg_action", "BG Action Selection", width, height)
+	ss.Win = win
+
+	vp := win.WinViewport2D()
+	updt := vp.UpdateStart()
+
+	mfr := win.SetMainFrame()
+
+	tbar := gi.AddNewToolBar(mfr, "ToolBar")
+	tbar.SetStretchMaxWidth()
+	ss.ToolBar = tbar
+
+	split := gi.AddNewSplitView(mfr, "Split")
+	split.Dim = 0 // X dimension
+	split.SetStretchMaxWidth()
+	split.SetStretchMaxHeight()
+
+	sv := giv.AddNewStructView(split, "sv")
+	sv.SetStruct(ss)
+
+	tv := gi.AddNewTabView(split, "tv")
+
+	nv := tv.AddNewTab(netview.KiT_NetView, "NetView").(*netview.NetView)
+	ss.NetView = nv
+	nv.Var = "Act"
+	nv.SetNet(ss.Net)
+
+	plt := tv.AddNewTab(eplot.KiT_Plot2D, "TrnEpcPlot").(*eplot.Plot2D)
+	ss.TrnEpcPlot = plt
+	plt.SetTable(ss.TrnEpcLog)
+	plt.Params.Title = "Training Epoch"
+	plt.Params.XAxisCol = "Epoch"
+	plt.SetColParams("PctR1_8020", eplot.On, eplot.FixMin, 0, eplot.FixMax, 1)
+	plt.SetColParams("PctR3_6040", eplot.On, eplot.FixMin, 0, eplot.FixMax, 1)
+	plt.SetColParams("PctGated", eplot.On, eplot.FixMin, 0, eplot.FixMax, 1)
+
+	plt2 := tv.AddNewTab(eplot.KiT_Plot2D, "TstEpcPlot").(*eplot.Plot2D)
+	ss.TstEpcPlot = plt2
+	plt2.SetTable(ss.TstEpcLog)
+	plt2.Params.Title = "Test Epoch"
+	plt2.Params.XAxisCol = "Epoch"
+	plt2.SetColParams("ChooseA", eplot.On, eplot.FixMin, 0, eplot.FixMax, 1)
+	plt2.SetColParams("AvoidB", eplot.On, eplot.FixMin, 0, eplot.FixMax, 1)
+
+	plt3 := tv.AddNewTab(eplot.KiT_Plot2D, "RunPlot").(*eplot.Plot2D)
+	ss.RunPlot = plt3
+	plt3.SetTable(ss.RunLog)
+	plt3.Params.Title = "Run Summary"
+	plt3.Params.XAxisCol = "Run"
+	plt3.SetColParams("ChooseA", eplot.On, eplot.FixMin, 0, eplot.FixMax, 1)
+	plt3.SetColParams("AvoidB", eplot.On, eplot.FixMin, 0, eplot.FixMax, 1)
+
+	split.SetSplits(.2, .8)
+
+	tbar.AddAction(gi.ActOpts{Label: "Init", Icon: "update", Tooltip: "Initialize network and environment", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(!ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		ss.Init()
+		vp.SetNeedsFullRender()
+	})
+	tbar.AddAction(gi.ActOpts{Label: "Train", Icon: "run", Tooltip: "Run full training", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(!ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		if !ss.IsRunning {
+			ss.IsRunning = true
+			tbar.UpdateActions()
+			go ss.Train()
+		}
+	})
+	tbar.AddAction(gi.ActOpts{Label: "Stop", Icon: "stop", Tooltip: "Stop running", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		ss.Stop()
+	})
+	tbar.AddAction(gi.ActOpts{Label: "Step Trial", Icon: "step-fwd", Tooltip: "Advances one training trial at a time, with per-cycle view updates.", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(!ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		if !ss.IsRunning {
+			ss.IsRunning = true
+			ss.TrainTrial()
+			ss.IsRunning = false
+			vp.SetNeedsFullRender()
+		}
+	})
+	tbar.AddAction(gi.ActOpts{Label: "Step Epoch", Icon: "fast-fwd", Tooltip: "Advances one epoch of training.", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(!ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		if !ss.IsRunning {
+			ss.IsRunning = true
+			tbar.UpdateActions()
+			go ss.TrainEpoch()
+		}
+	})
+
+	tbar.AddSeparator("test")
+
+	tbar.AddAction(gi.ActOpts{Label: "Test Trial", Icon: "step-fwd", Tooltip: "Runs one test trial with per-cycle view updates.", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(!ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		if !ss.IsRunning {
+			ss.IsRunning = true
+			ss.TestTrial()
+			ss.IsRunning = false
+			vp.SetNeedsFullRender()
+		}
+	})
+	tbar.AddAction(gi.ActOpts{Label: "Test", Icon: "fast-fwd", Tooltip: "Run full test phase", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(!ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		if !ss.IsRunning {
+			ss.IsRunning = true
+			tbar.UpdateActions()
+			go ss.Test()
+		}
+	})
+
+	tbar.AddSeparator("adv")
+
+	tbar.AddAction(gi.ActOpts{Label: "Train+Test", Icon: "run", Tooltip: "Run full train and test", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(!ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		if !ss.IsRunning {
+			ss.IsRunning = true
+			tbar.UpdateActions()
+			go ss.TrainAndTest()
+		}
+	})
+	tbar.AddAction(gi.ActOpts{Label: "Batch", Icon: "run", Tooltip: "Run batch of networks", UpdateFunc: func(act *gi.Action) {
+		act.SetActiveStateUpdt(!ss.IsRunning)
+	}}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		if !ss.IsRunning {
+			ss.IsRunning = true
+			tbar.UpdateActions()
+			go ss.RunBatch()
+		}
+	})
+
+	tbar.AddSeparator("misc")
+
+	tbar.AddAction(gi.ActOpts{Label: "New Seed", Icon: "new", Tooltip: "New random seed"}, win.This(), func(recv, send ki.Ki, sig int64, data interface{}) {
+		ss.NewRndSeed()
+	})
+
+	vp.UpdateEndNoSig(updt)
+
+	// main menu
+	appnm := gi.AppName()
+	mmen := win.MainMenu
+	mmen.ConfigMenus([]string{appnm, "File", "Edit", "Window"})
+	amen := win.MainMenu.ChildByName(appnm, 0).(*gi.Action)
+	amen.Menu.AddAppMenu(win)
+	emen := win.MainMenu.ChildByName("Edit", 1).(*gi.Action)
+	emen.Menu.AddCopyCutPaste(win)
+
+	win.MainMenuUpdated()
+	return win
+}
+
+// SimProps defines the properties for the Sim type in the GUI.
+var SimProps = ki.Props{
+	"ToolBar": ki.PropSlice{},
+}
+
+////////////////////////////////////////////////////////////////////
+//  Command-line args
+
+// CmdArgs handles command-line arguments for batch mode.
+func (ss *Sim) CmdArgs() {
+	var nogui bool
+	var nruns int
+	var nepochs int
+
+	nogui = true
+	nruns = ss.NRuns
+	nepochs = ss.MaxEpochs
+
+	if len(os.Args) > 1 {
+		for i := 1; i < len(os.Args); i++ {
+			arg := os.Args[i]
+			switch arg {
+			case "-runs":
+				if i+1 < len(os.Args) {
+					n, err := strconv.Atoi(os.Args[i+1])
+					if err == nil {
+						nruns = n
+					}
+					i++
+				}
+			case "-epochs":
+				if i+1 < len(os.Args) {
+					n, err := strconv.Atoi(os.Args[i+1])
+					if err == nil {
+						nepochs = n
+					}
+					i++
+				}
+			case "-pd":
+				ss.NumIntactSNc = 2
+			}
+		}
+	}
+
+	_ = nogui
+	ss.NRuns = nruns
+	ss.MaxEpochs = nepochs
+	ss.Init()
+	ss.RunBatch()
+
+	// Print summary
+	fmt.Printf("Batch complete: %d runs, %d epochs\n", nruns, nepochs)
+	if ss.RunLog.Rows > 0 {
+		rix := etable.NewIdxView(ss.RunLog)
+		chooseA := agg.Mean(rix, "ChooseA")[0]
+		avoidB := agg.Mean(rix, "AvoidB")[0]
+		pctGated := agg.Mean(rix, "PctGated")[0]
+		fmt.Printf("Mean ChooseA: %.3f  AvoidB: %.3f  PctGated: %.3f\n", chooseA, avoidB, pctGated)
+	}
+
+	// Save logs
+	ss.TrnEpcLog.SaveCSV(gi.FileName("bg_action_trn_epc.csv"), ',', true)
+	ss.TstEpcLog.SaveCSV(gi.FileName("bg_action_tst_epc.csv"), ',', true)
+	ss.RunLog.SaveCSV(gi.FileName("bg_action_run.csv"), ',', true)
+}
+
+// Ensure all imports are used
+var (
+	_ = bytes.NewBuffer
+	_ = fmt.Println
+	_ = log.Println
+	_ = math.Abs
+	_ = rand.Intn
+	_ = os.Exit
+	_ = strconv.Atoi
+	_ = strings.Contains
+	_ = time.Now
+	_ = split.GroupBy
+)
diff --git a/ch8/bg_action/go.mod b/ch8/bg_action/go.mod
new file mode 100644
index 00000000..a635e8f0
--- /dev/null
+++ b/ch8/bg_action/go.mod
@@ -0,0 +1,65 @@
+module bg_action
+
+go 1.24.13
+
+require (
+	github.com/emer/emergent v1.3.53
+	github.com/emer/etable v1.1.24
+	github.com/emer/leabra v1.2.10
+	github.com/goki/gi v1.3.25
+	github.com/goki/ki v1.1.17
+)
+
+require (
+	git.sr.ht/~sbinet/gg v0.3.1 // indirect
+	github.com/BurntSushi/freetype-go v0.0.0-20160129220410-b763ddbfe298 // indirect
+	github.com/BurntSushi/graphics-go v0.0.0-20160129215708-b43f31a4a966 // indirect
+	github.com/BurntSushi/xgb v0.0.0-20210121224620-deaf085860bc // indirect
+	github.com/BurntSushi/xgbutil v0.0.0-20190907113008-ad855c713046 // indirect
+	github.com/Masterminds/vcs v1.13.3 // indirect
+	github.com/ajstarks/svgo v0.0.0-20211024235047-1546f124cd8b // indirect
+	github.com/akutz/sortfold v0.2.1 // indirect
+	github.com/alecthomas/chroma/v2 v2.9.1 // indirect
+	github.com/anthonynsimon/bild v0.13.0 // indirect
+	github.com/antonmedv/expr v1.15.3 // indirect
+	github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40 // indirect
+	github.com/aymerick/douceur v0.2.0 // indirect
+	github.com/c2h5oh/datasize v0.0.0-20220606134207-859f65c6625b // indirect
+	github.com/dlclark/regexp2 v1.10.0 // indirect
+	github.com/fatih/camelcase v1.0.0 // indirect
+	github.com/fsnotify/fsnotify v1.7.0 // indirect
+	github.com/gabriel-vasile/mimetype v1.4.3 // indirect
+	github.com/go-fonts/liberation v0.3.0 // indirect
+	github.com/go-gl/glfw/v3.3/glfw v0.0.0-20221017161538-93cebf72946b // indirect
+	github.com/go-gl/mathgl v1.1.0 // indirect
+	github.com/go-latex/latex v0.0.0-20230307184459-12ec69307ad9 // indirect
+	github.com/go-pdf/fpdf v0.7.0 // indirect
+	github.com/goki/freetype v1.0.1 // indirect
+	github.com/goki/go-difflib v1.2.1 // indirect
+	github.com/goki/gosl v1.0.17 // indirect
+	github.com/goki/kigen v1.0.2 // indirect
+	github.com/goki/mat32 v1.0.18 // indirect
+	github.com/goki/pi v1.0.28 // indirect
+	github.com/goki/prof v1.0.1 // indirect
+	github.com/goki/vci v1.0.2 // indirect
+	github.com/goki/vgpu v1.0.34 // indirect
+	github.com/goki/vulkan v1.0.7 // indirect
+	github.com/golang/freetype v0.0.0-20170609003504-e2365dfdc4a0 // indirect
+	github.com/gorilla/css v1.0.0 // indirect
+	github.com/h2non/filetype v1.1.3 // indirect
+	github.com/iancoleman/strcase v0.3.0 // indirect
+	github.com/jinzhu/copier v0.4.0 // indirect
+	github.com/mitchellh/go-homedir v1.1.0 // indirect
+	github.com/srwiley/rasterx v0.0.0-20220730225603-2ab79fcdd4ef // indirect
+	github.com/srwiley/scanx v0.0.0-20190309010443-e94503791388 // indirect
+	golang.org/x/exp v0.0.0-20231006140011-7918f672742d // indirect
+	golang.org/x/image v0.13.0 // indirect
+	golang.org/x/mod v0.13.0 // indirect
+	golang.org/x/net v0.17.0 // indirect
+	golang.org/x/sys v0.13.0 // indirect
+	golang.org/x/text v0.13.0 // indirect
+	golang.org/x/tools v0.14.0 // indirect
+	golang.org/x/xerrors v0.0.0-20220907171357-04be3eba64a2 // indirect
+	gonum.org/v1/gonum v0.12.0 // indirect
+	gonum.org/v1/plot v0.12.0 // indirect
+)
diff --git a/ch8/bg_action/go.sum b/ch8/bg_action/go.sum
new file mode 100644
index 00000000..b381bcb7
--- /dev/null
+++ b/ch8/bg_action/go.sum
@@ -0,0 +1,371 @@
+cloud.google.com/go v0.26.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
+cloud.google.com/go v0.34.0/go.mod h1:aQUYkXzVsufM+DwF1aE+0xfcU+56JwCaLick0ClmMTw=
+dmitri.shuralyov.com/gpu/mtl v0.0.0-20190408044501-666a987793e9/go.mod h1:H6x//7gZCb22OMCxBHrMx7a5I7Hp++hsVxbQ4BYO7hU=
+gioui.org v0.0.0-20210308172011-57750fc8a0a6/go.mod h1:RSH6KIUZ0p2xy5zHDxgAM4zumjgTw83q2ge/PI+yyw8=
+git.sr.ht/~sbinet/gg v0.3.1 h1:LNhjNn8DerC8f9DHLz6lS0YYul/b602DUxDgGkd/Aik=
+git.sr.ht/~sbinet/gg v0.3.1/go.mod h1:KGYtlADtqsqANL9ueOFkWymvzUvLMQllU5Ixo+8v3pc=
+github.com/BurntSushi/freetype-go v0.0.0-20160129220410-b763ddbfe298 h1:1qlsVAQJXZHsaM8b6OLVo6muQUQd4CwkH/D3fnnbHXA=
+github.com/BurntSushi/freetype-go v0.0.0-20160129220410-b763ddbfe298/go.mod h1:D+QujdIlUNfa0igpNMk6UIvlb6C252URs4yupRUV4lQ=
+github.com/BurntSushi/graphics-go v0.0.0-20160129215708-b43f31a4a966 h1:lTG4HQym5oPKjL7nGs+csTgiDna685ZXjxijkne828g=
+github.com/BurntSushi/graphics-go v0.0.0-20160129215708-b43f31a4a966/go.mod h1:Mid70uvE93zn9wgF92A/r5ixgnvX8Lh68fxp9KQBaI0=
+github.com/BurntSushi/toml v0.3.1/go.mod h1:xHWCNGjB5oqiDr8zfno3MHue2Ht5sIBksp03qcyfWMU=
+github.com/BurntSushi/xgb v0.0.0-20160522181843-27f122750802/go.mod h1:IVnqGOEym/WlBOVXweHU+Q+/VP0lqqI8lqeDx9IjBqo=
+github.com/BurntSushi/xgb v0.0.0-20210121224620-deaf085860bc h1:7D+Bh06CRPCJO3gr2F7h1sriovOZ8BMhca2Rg85c2nk=
+github.com/BurntSushi/xgb v0.0.0-20210121224620-deaf085860bc/go.mod h1:IVnqGOEym/WlBOVXweHU+Q+/VP0lqqI8lqeDx9IjBqo=
+github.com/BurntSushi/xgbutil v0.0.0-20190907113008-ad855c713046 h1:O/r2Sj+8QcMF7V5IcmiE2sMFV2q3J47BEirxbXJAdzA=
+github.com/BurntSushi/xgbutil v0.0.0-20190907113008-ad855c713046/go.mod h1:uw9h2sd4WWHOPdJ13MQpwK5qYWKYDumDqxWWIknEQ+k=
+github.com/Masterminds/vcs v1.13.3 h1:IIA2aBdXvfbIM+yl/eTnL4hb1XwdpvuQLglAix1gweE=
+github.com/Masterminds/vcs v1.13.3/go.mod h1:TiE7xuEjl1N4j016moRd6vezp6e6Lz23gypeXfzXeW8=
+github.com/ajstarks/deck v0.0.0-20200831202436-30c9fc6549a9/go.mod h1:JynElWSGnm/4RlzPXRlREEwqTHAN3T56Bv2ITsFT3gY=
+github.com/ajstarks/deck/generate v0.0.0-20210309230005-c3f852c02e19/go.mod h1:T13YZdzov6OU0A1+RfKZiZN9ca6VeKdBdyDV+BY97Tk=
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+github.com/akutz/sortfold v0.2.1 h1:u9x3FC6oM+6gZKEVNRnmVafJgappwrv9YqpELQCYViI=
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+github.com/emer/etable v1.1.24 h1:lxcZFalEERTQJpbAR0nNfyM4+K/ku2H+Nl6y4cmDHyE=
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