__USER__
Awesome! I now want to get more intimate with the framework. I want to create a single neuron and see how a single tick travels, etc. Maybe even put breakpoints on critical places.

__GPT-5 Pro__

Love it—this is exactly the right time to “zoom in” and watch a spike move through the system. Below is a compact, engineer‑friendly **Microscope Mode** you can drop into your tree to observe a single tick, set breakpoints, and verify the call‑graph. I’ve shown the flow first, then ready‑to‑paste demo code for C++ and Java with **recommended breakpoint locations**.

------

## 1) What happens in a single tick (call‑graph)

**High level (Region API):**

```
Region.tick(port, value)
 ├─ resolve layers bound to `port` (inputPorts)
 ├─ for each bound Layer → Layer.forward(value)
 │    └─ for each Neuron
 │        ├─ Neuron.onInput(value)
 │        │    ├─ SlotEngine.selectOrCreateSlot(...)
 │        │    ├─ Weight.reinforce(modulationFactor)
 │        │    └─ threshold check → if fired → Neuron.fire(value)
 │        │         ├─ Excitatory: propagate to Synapses (Tract)
 │        │         ├─ Inhibitory: bus.inhibition pulse
 │        │         └─ Modulatory: bus.modulation pulse
 │        └─ ...
 ├─ Tracts deliver spikes to downstream Layers (if any)
 └─ Region.end-of-tick housekeeping: bus.decay(), metrics tally
```

The **authoritative Region API** surface (constructor, `addLayer`, `connectLayers`, `bindInput/Output`, `tick`, `prune`, accessors, and metrics types) is defined in the C++ header and mirrors the other language bindings. Keep this open while you inspect so you can correlate calls with behavior. 

The architectural “why” behind inhibition/modulation on a shared lateral bus and local (slot‑level) learning is captured in the **Theory of Change** doc; refer to it while you check bus state during a tick. 

------

## 2) C++ “single‑neuron” microscope

Create **`src/cpp/demo/single_neuron_demo.cpp`**:

```cpp
#include <iostream>
#include <vector>
#include "Region.h"
#include "Layer.h"

using namespace grownet;

static void printMetrics(const RegionMetrics& m) {
    std::cout << "deliveredEvents=" << m.deliveredEvents
              << " totalSlots="     << m.totalSlots
              << " totalSynapses="  << m.totalSynapses << std::endl;
}

int main() {
    // 1) Region and a single-layer with a single excitatory neuron
    Region region("dbg");                                // Region(name)
    int l0 = region.addLayer(/*E=*/1, /*I=*/0, /*M=*/0); // 1 excitatory neuron

    // Bind the input port "x" to our only layer so tick("x", v) drives it.
    region.bindInput("x", std::vector<int>{ l0 });

    std::cout << "=== Single layer, no tracts ===" << std::endl;
    auto m0 = region.tick("x", 0.80);                    // drive a single tick
    printMetrics(m0);                                    // expect deliveredEvents==0 (no outgoing tracts)

    // 2) Now add a second layer and a guaranteed tract so we can see delivery
    int l1 = region.addLayer(/*E=*/1, /*I=*/0, /*M=*/0);
    region.connectLayers(l0, l1, /*probability=*/1.0, /*feedback=*/false); // sure connect

    std::cout << "=== Two layers with tract ===" << std::endl;
    auto m1 = region.tick("x", 0.95);
    printMetrics(m1);                                    // deliveredEvents should increase

    return 0;
}
```

**Build target (CMake):** add to your CMakeLists

```cmake
add_executable(single_neuron_demo src/cpp/demo/single_neuron_demo.cpp)
target_link_libraries(single_neuron_demo PRIVATE grownet)
target_include_directories(single_neuron_demo PRIVATE ${CMAKE_SOURCE_DIR}/src/cpp)
```

### Set these CLion breakpoints

> (Right‑click gutter → Breakpoint; run **single_neuron_demo** with Debug)

- **`Region.cpp`**
  - `Region::tick()` — entry/exit, watch `bus`, `inputPorts`, metrics. API reference: Region.tick signature. 
- **`Layer.cpp`**
  - `Layer::forward(double)` — confirms the layer fan‑in path.
- **`Neuron.cpp`**
  - `bool Neuron::onInput(double)` — inspect `haveLastInput`, `lastInputValue`, and which slot ID was chosen.
  - `void Neuron::fire(double)` (or `onOutput(...)` in your variant) — confirms spike decision.
- **`SlotEngine.cpp`**
  - `selectOrCreateSlot(...)` — see slot ID computation / expansion.
- **`Weight.cpp`**
  - `reinforce(double modulationFactor)` — watch `strength`, `hitCount`, threshold state, and how bus.modulation scales updates.
- **`Tract.cpp` / `Synapse.cpp`**
  - Delivery point (e.g., `Tract::onSourceFired` or `Synapse::deliver`) — confirms **deliveredEvents** increments.
- **`LateralBus.cpp`**
  - decay/updates — correlate inhibition/modulation pulses with neuron types. Conceptual flow detailed in the doc. 

**What to watch (Debugger → Variables):**

- `Region::bus`: `modulationFactor`, `inhibitionFactor` before/after `tick()`. (The Region exposes `pulseInhibition` / `pulseModulation` if you want to test pulses per spec. )
- `Neuron::slots`: size growth and the chosen slot’s `Weight.strength` & `theta`.
- `RegionMetrics`: `deliveredEvents`, `totalSlots`, `totalSynapses` after each tick. 

------

## 3) Java microscope (parity with the gold copy)

Create **`src/java/demo/SingleNeuronDemo.java`**:

```java
package demo;

import ai.nektron.grownet.Region;
import ai.nektron.grownet.RegionMetrics;

import java.util.List;

public class SingleNeuronDemo {
    public static void main(String[] args) {
        Region region = new Region("dbg");                 // Region(String)
        int l0 = region.addLayer(1, 0, 0);                 // 1 excitatory neuron
        region.bindInput("x", List.of(l0));

        System.out.println("=== Single layer, no tracts ===");
        RegionMetrics m0 = region.tick("x", 0.80);
        System.out.printf("delivered=%d slots=%d synapses=%d%n",
                m0.deliveredEvents, m0.totalSlots, m0.totalSynapses);

        int l1 = region.addLayer(1, 0, 0);
        region.connectLayers(l0, l1, 1.0, false);

        System.out.println("=== Two layers with tract ===");
        RegionMetrics m1 = region.tick("x", 0.95);
        System.out.printf("delivered=%d slots=%d synapses=%d%n",
                m1.deliveredEvents, m1.totalSlots, m1.totalSynapses);
    }
}
```

### IntelliJ breakpoints (mirror C++)

- `Region.tick(...)` — entry/exit.
- `Layer.forward(...)`.
- `Neuron.onInput(...)`, `Weight.reinforce(...)`, threshold decision, and `Neuron.onOutput(...)`/`fire(...)`.
- Tract/Synapse delivery site — confirm deliveries and metrics.

> The Java shape mirrors the C++ Region API/metrics (constructor, `addLayer`, `connectLayers`, `bindInput/Output`, `tick`, and metrics fields), per the master spec. 

------

## 4) Optional: minimal pulses test (inhibition/modulation)

Add to either demo before calling `tick()`:

```cpp
// C++
region.pulseModulation(1.5);  // temporarily scale learning for this tick
region.pulseInhibition(0.7);  // temporarily inhibit this tick
// Java
region.pulseModulation(1.5);
region.pulseInhibition(0.7);
```

Then step through `Weight.reinforce(...)` and verify the modulation factor; check where the inhibition level is read/applied during spike handling. This matches the bus semantics described in the Theory of Change. 

------

## 5) A quick checklist for “one‑tick sanity”

1. **No tracts:** `deliveredEvents == 0`; slots may grow to 1; one neuron evaluated.
2. **With a tract:** `deliveredEvents` increases when the source fires and `probability == 1.0`.
3. **Modulation pulse set:** the effective learning step inside `reinforce(...)` scales up for that tick.
4. **Inhibition pulse set:** downstream slot strengths (or firing condition, depending on your implementation site) visibly reduced in that tick.
5. **Bus decay/end‑tick:** bus state returns toward neutral after `tick()`.

------

## 6) If you want to instrument without a debugger

You can add temporary **guarded logs** (compile‑time toggles) near your breakpoints:

```cpp
// e.g., in Neuron.cpp
#ifdef GN_TRACE
std::cerr << "[Neuron " << neuronId
          << "] onInput=" << value
          << " chosenSlot=" << chosenId
          << " θ=" << slot.getThresholdValue()
          << " strength=" << slot.getStrengthValue()
          << " fired=" << fired << "\n";
#endif
```

Or Java:

```java
if (System.getProperty("gn.trace") != null) {
    System.err.printf("[Neuron %s] in=%.3f slot=%d theta=%.3f w=%.3f fired=%s%n",
        id, value, chosenId, slot.getThresholdValue(), slot.getStrengthValue(), fired);
}
```

This lets you compare console traces across languages with the **same stimulus** and confirm parity with the C++ API surface and behavior described in the design doc.

------

### That’s it

Drop the demo file(s) in, set the breakpoints above, and you’ll be able to “feel” how a single value walks the pipeline: **Region → Layer → Neuron → Slot/Weight → Bus/Tract → Metrics** (exactly the flow the spec calls out).

If you want, I can also pre-wire tiny **unit tests** that assert each of the “one‑tick sanity” items so you can run them as guardrails while you step in the debugger.


__USER__

Awesome! The breakpoint additions I want to focus on is Java and Python.  Java and C++ are related and Python Mojo. Java is my comfort zone. And I’ve used debugging in Java so many times and the same with Python.



__GPT-5 Pro__

Great—let’s make Java and Python debugging really smooth. Below is a focused “breakpoint plan” for **one-tick tracing** and day‑to‑day troubleshooting. I’ve organized it as:

1. what to run, 2) where to put breakpoints (Java first, then Python), 3) the *expected step‑by‑step call flow* for a single tick, 4) watches/expressions to add, and 5) a few conditional‐breakpoint tricks.

I cite the canonical Region API (for method names/shape), which both Java and Python mirror.  I also reference the design doc sections that explain inhibition/modulation through the bus so the state you’ll watch makes sense. 

------

## 1) What to run (tiny harnesses you can keep around)

### Java (IntelliJ IDEA)

Create a tiny main (you can keep it under `src/main/java/ai/nektron/grownet/debug/SingleNeuronDemo.java`) to drive a single value through one neuron in one layer:

```java
package ai.nektron.grownet.debug;

import ai.nektron.grownet.*;

public class SingleNeuronDemo {
    public static void main(String[] args) {
        Region region = new Region("dbg");                   // Region(...)
        int layerIdx = region.addLayer(1, 0, 0);            // 1 excitatory
        region.bindInput("x", java.util.List.of(layerIdx)); // bind to input port "x"
        // No outgoing tracts; we’re focusing on one neuron’s behavior
        // One tick with a test value:
        RegionMetrics m = region.tick("x", 0.42);
        System.out.println("delivered=" + m.getDeliveredEvents() +
                           " slots=" + m.getTotalSlots() +
                           " syn=" + m.getTotalSynapses());
    }
}
```

This hits the **exact Region API**—`Region(name)`, `addLayer(...)`, `bindInput(...)`, `tick(port, value)`—so your breakpoints land consistently. 

> Tip: Add a second run that wires a second layer and a tract, so you can see propagation:
>
> ```java
> int destIdx = region.addLayer(1, 0, 0);
> region.connectLayers(layerIdx, destIdx, /*prob=*/1.0, /*feedback=*/false);
> region.bindOutput("y", java.util.List.of(destIdx));
> region.tick("x", 0.42);
> ```

### Python (VS Code or PyCharm)

Keep a tiny script `scripts/single_neuron_demo.py`:

```python
from region import Region

def main():
    region = Region("dbg")
    layer_idx = region.add_layer(1, 0, 0)         # mirrors Java
    region.bind_input("x", [layer_idx])
    # single tick
    metrics = region.tick("x", 0.42)
    print(metrics)

if __name__ == "__main__":
    main()
```

> In Python we used underscore method names (per your convention), but your **public API** mirrors the C++/Java shape (method names may map internally to `_add_layer`, `_tick`, etc.). Keep breakpoints on the *underscored* implementations.

------

## 2) Where to put breakpoints

### Java — primary breakpoints (put these first)

1. **`Region.tick(String port, double value)`**
    *First entry into one‑tick processing and final aggregation of `RegionMetrics`.*
    Break here to start each trace. Step into the tick. 
2. **`Layer.forward(double value)`**
    *The per‑layer fan‑out—drives each neuron’s input for this tick.*
3. **`Neuron.onInput(double value)`** (base or specific subclass)
    *Where slot selection happens; last/next input bookkeeping; returns `fired`.*
    Set **Conditional Breakpoint**: `fired == true`.
4. **Slot/Weight logic** (names vary slightly by your Java file; use both if present):
   - `Weight.reinforce(double modulationFactor)`
   - `Weight.updateThreshold(double value)`
      These are your “learning happens here” and “did we cross θ?” lines.
5. **`Neuron.onOutput(double amplitude)`**
    *When a spike is emitted.* If you have excitatory/inhibitory/modulatory subclasses, also breakpoint their overrides:
   - `ExcitatoryNeuron.onOutput(double)` → should lead to tract delivery
   - `InhibitoryNeuron.onOutput(double)` → look for bus inhibition pulse
   - `ModulatoryNeuron.onOutput(double)` → look for bus modulation pulse
6. **`Tract.onSourceFired(Neuron who, double value)`** (or similar deliver method)
    *Confirms fan‑out from source layer to destination layer(s).*
7. **`Layer.propagateFrom(int sourceIndex, double value)`** (for 2D layers too)
    *Confirms value arrival into destination layer before neuron input.*
8. **Bus/Region housekeeping**
   - `LateralBus.setInhibitionFactor(...)`, `setModulationFactor(...)`, `decay()`
   - `Region.pulseInhibition(...)`, `pulseModulation(...)`
      You’ll see pulses when inhibitory/modulatory neurons fire, and decay at end of tick. Theory/intent is in the doc. 

### Python — primary breakpoints (underscored implementations)

File names reflect your Python layout (each neuron in its own file; 2D layers in `input_layer_2d.py` and `output_layer_2d.py`):

1. **`region.py` → `_tick(self, port: str, value: float)`**
2. **`layer.py` → `_forward(self, value: float)`**
3. **`neuron_base.py` → `_on_input(self, value: float)`**
   - set condition: `self._fired_last is True` (or equivalent)
   - also breakpoint `_select_or_create_slot(...)` if you split that logic
4. **`weight.py` → `_reinforce(self, modulation: float)` and `_update_threshold(self, value: float)`**
5. **`neuron_exc.py` / `neuron_inh.py` / `neuron_mod.py` → `_on_output(self, amplitude: float)`**
   - Excitatory → will trigger propagation
   - Inhibitory/Modulatory → will pulse the bus (`lateral_bus.py`)
6. **`tract.py` → `_on_source_fired(self, who, value)`** (or your deliver method)
7. **`layer.py` (2D variants)**
   - `input_layer_2d.py` → `_propagate_from(self, source_index: int, value: float)` (often a no‑op for inputs)
   - `output_layer_2d.py` → `_propagate_from(...)`, `_end_tick(...)` (frame update)

------

## 3) What you should see (single‑tick call flow)

**Excitatory‑only, one layer** (no tracts):

```
Region.tick("x", v)
  -> for each bound input layer L:
       L.forward(v)
         -> for each neuron n in L:
              n.onInput(v)
                - slot selected (via %Δ or last-seen policy)
                - weight.reinforce(bus.modulation)
                - threshold check (θ update)
                - if fired: n.onOutput(v)  (E: no bus pulse by itself)
  -> bus.decay() / metrics aggregation
```

**Two layers with a tract (E→E)**:

```
Region.tick("x", v)
  -> L_src.forward(v)
    -> n_src.onInput(v) ... n_src.onOutput(v)
      -> Tract.onSourceFired(n_src, v)
         -> L_dst.propagateFrom(index, v)
           -> n_dst.onInput(v) ... n_dst.onOutput(v)
  -> bus.decay() / metrics aggregation
```

> Inhibitory/Modulatory spikes will set `bus.inhibitionLevel` or `bus.modulationFactor` during the tick, which affects downstream slots’ reinforcement and/or strengths (exact semantics described in the theory doc). 

------

## 4) Watches & log‑on‑demand

Add these **watches** (Java & Python analogues):

- **Neuron**: `haveLastInput`, `lastInputValue`, `firedLast`
- **Slot/Weight**:
  - `strength` (clamped −1…+1), `hitCount`
  - `theta` (threshold), `emaRate`, `seenFirst` (or `firstSeen`)
- **Bus**: `inhibitionFactor/level`, `modulationFactor/level`
- **RegionMetrics** (at return from tick): `deliveredEvents`, `totalSlots`, `totalSynapses`. 

**Optional trace toggles** (if you like printf‑style logging for quick diffs):

- Java: add a `static boolean TRACE = false;` in `Neuron`/`Weight` and guard short `System.out.printf(...)` lines at key points (`onInput`, `onOutput`, `reinforce`, threshold updates). Toggle per class when needed.
- Python: add a module‑level `_TRACE = False` and guarded `print(...)` in `_on_input`, `_on_output`, `_reinforce`, `_update_threshold`.

Keep them *off* during normal work; enable only around the area you’re inspecting.

------

## 5) Handy conditional breakpoints

**Java (IntelliJ):**

- In `Neuron.onInput(double value)`:
   Condition: `fired == true || Math.abs(value) > 0.5`
- In `Weight.updateThreshold(double value)`:
   Condition: `Math.abs(theta - value) < 1e-6` *(watch “knife‑edge” firings)*
- In `Tract.onSourceFired(...)`:
   Condition: `who.getId().equals("E0")` *(trace just the first neuron)*

**Python (VS Code/PyCharm):**

- In `weight.py::_update_threshold(...)`:
   Condition: `abs(self._theta - value) < 1e-6`
- In `neuron_base.py::_on_input(...)`:
   Condition: `self._fired_last`

------

## 6) Mapping (Java ↔ Python)

| Concept             | Java (methods)                                               | Python (underscored impl)                                    |
| ------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| Region step         | `Region.tick(port, value)`                                   | `Region.tick(port, value)` → `_tick`                         |
| Drive layer         | `Layer.forward(value)`                                       | `Layer._forward(value)`                                      |
| Select slot + learn | `Neuron.onInput(value)` + `Weight.reinforce/updateThreshold` | `NeuronBase._on_input(value)` + `Weight._reinforce/_update_threshold` |
| Fire                | `Neuron.onOutput(amplitude)`                                 | `NeuronBase._on_output(amplitude)` (overridden in `neuron_exc/_inh/_mod`) |
| Deliver along tract | `Tract.onSourceFired(...)`                                   | `Tract._on_source_fired(...)`                                |
| Bus pulses/decay    | `LateralBus.set*/decay()`                                    | `LateralBus._set*/_decay()`                                  |
| Output frame flush  | `OutputLayer2D.endTick()`                                    | `OutputLayer2D._end_tick()`                                  |

------

## 7) If something “doesn’t fire”

- Check **slot selection** (did we create/select the intended slot ID?). Set a breakpoint where the slot ID is computed and watch the map size.
- Confirm **bus factors** (inhibition/modulation) aren’t suppressing reinforcement or strength; watch those variables live during the tick. 
- On two‑layer traces, verify **tract wiring** (break in `connectLayers(...)` once during setup, then `onSourceFired(...)` during run). The API shape is listed here if you want to cross‑check calls. 

------

If you’d like, I can also generate the tiny Java and Python “single‑neuron” demo files directly in the tree (with the exact package/imports you’re using) so you can just right‑click → **Debug** and follow the path above.