Here’s a concise, GrowNet‑native view of **2D bins**—what they are, how they’re computed, and how you’d actually use them without introducing “DL pooling.”

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## TL;DR

- A **2D bin** is a **deterministic spatial bucket** on the input image plane (or any 2‑D lattice) that groups nearby pixels/neurons.
- Bins are **topological** (geometry only). They don’t learn; the **neurons you connect into each bin do the learning** via their normal slot/weight dynamics.
- You can use bins two ways:
  1. **Structural binning (preferred):** wire many fine‑grid inputs into a smaller set of **bin neurons** (coarse layer).
  2. **Readout binning (optional):** summarize the current 2‑D frame for logging/visualization (max/avg/sum) — this does **not** affect learning.

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## What a 2D bin is (in GrowNet terms)

- Given an **InputLayer2D(H×W)**, choose **bin height** `Bh` and **bin width** `Bw` (and optionally stride `Sh`, `Sw`).

- Each pixel `(r, c)` maps to a **bin index**:

  ```
  bin_rows = ceil(H / Bh)
  bin_cols = ceil(W / Bw)
  bin_id(r, c) = (r // Bh) * bin_cols + (c // Bw)     # non-overlapping, Sh=Bh, Sw=Bw
  ```

- The **bin neurons** live in a normal `Layer` (no special class needed). Their IDs or names can encode `(bin_row, bin_col)`.

- **Learning stays local**: the receiving bin neuron’s slots/weights “summarize” its neighborhood through repeated spikes from all members mapped into that bin.

> **Difference from pooling:** bins aren’t an operator inside the tick; they’re just **wiring topology**. Any “averaging” emerges from neuron thresholds, modulation, and repeated inputs—not from a fixed reduction function.

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## Two usage patterns

### 1) Structural binning (recommended)

Create a coarser layer with one neuron per bin and **connect** each input pixel’s neuron to its bin neuron.

**Sketch (Python)** — uses existing primitives only:

```python
from region import Region
from input_layer_2d import InputLayer2D

H, W = 64, 64
Bh, Bw = 8, 8                        # bin size
bin_rows = (H + Bh - 1) // Bh
bin_cols = (W + Bw - 1) // Bw
num_bins = bin_rows * bin_cols

region = Region("spatial_bins")
in_idx = region.add_input_layer_2d(H, W, gain=1.0, epsilon_fire=0.01)
bin_layer_idx = region.add_layer(excitatory_count=num_bins, inhibitory_count=0, modulatory_count=0)

src = region.layers[in_idx]
dst = region.layers[bin_layer_idx]

# Deterministic wiring: many-to-one, pixel -> its bin neuron
for r in range(H):
    for c in range(W):
        src_idx = src.index(r, c)
        bin_id = (r // Bh) * bin_cols + (c // Bw)
        src_neuron = src.get_neurons()[src_idx]
        dst_neuron = dst.get_neurons()[bin_id]
        src_neuron.connect(dst_neuron, feedback=False)   # local fan-in, neuron learns as usual

region.bind_input("pixels", [in_idx])
# Now when you tick_2d, each bin neuron aggregates its neighborhood via normal slot/weight updates.
```

**Why this fits GrowNet:**

- No special layers.
- **Aggregation is learned** (thresholds, slot selection, weight reinforcement).
- You can make multiple bin layers at different scales (a *pyramid*) and wire them in parallel.

### 2) Readout binning (optional utility)

Sometimes you just want a coarse **summary of the current output frame** (for metrics or UI). This is a **non‑learning** operation you compute *after* the tick on `OutputLayer2D.get_frame()` (or similar).

- Example operations: max/avg/sum within each bin window.
- This doesn’t change connectivity or training; it’s a convenience for monitoring.

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## Design knobs & trade‑offs

- **Bin size (`Bh`, `Bw`)**
   Bigger bins → fewer neurons downstream (cheaper), but coarser spatial locality.
   Smaller bins → more neurons, sharper locality.
- **Stride (`Sh`, `Sw`)**
  - **Non‑overlapping** (`Sh=Bh`, `Sw=Bw`): each pixel goes to exactly one bin (simpler).
  - **Overlapping** (`Sh < Bh` or `Sw < Bw`): a pixel can drive multiple bin neurons (robust but denser wiring).
- **Edge handling**
   If `H` or `W` isn’t divisible by `Bh`, `Bw`, the bottom/right bins are smaller. The mapping above already handles that via `ceil`.
- **Multi‑scale bins**
   Build several bin layers with different `(Bh, Bw)` and wire them to different downstream modules. This gives you scale sensitivity without any “pooling layer.”
- **Sparsity & gating**
   You can gate wiring per bin (e.g., only connect bins whose recent activity exceeds a threshold) to save compute or adapt focus dynamically.

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## Metrics & invariants

- With **non‑overlapping** bins, each source neuron maps to exactly one bin.
- Wiring cost scales as `O(H×W)` connections into `O(num_bins)` targets, instead of `O(H×W×all_targets)`.
- **RegionMetrics** (slots/synapses) will reflect the coarser fan‑in; `deliveredEvents` is unchanged (edge‑driven), because binning is structural, not a tick‑time operator.

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## When to use 2D bins

- You want **locality‑preserving reduction** (fewer neurons downstream) while keeping a purely **neuro‑local**, slot‑based learning story.
- You want **pyramids/multi‑scale** features without introducing convolution or pooling layers.
- You want **interpretable topology**: each bin neuron owns a clear spatial neighborhood.

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### Quick checklist

-  Pick `Bh`, `Bw` (and optional `Sh`, `Sw`).
-  Create a bin layer with `bin_rows * bin_cols` excitatory neurons.
-  Wire each pixel’s input neuron → its bin neuron (deterministic mapping).
-  (Optional) Add a second/third bin layer at larger scales.
-  Bind input and tick as usual; learning happens in the bin neurons’ slots/weights.

If you’d like, I can turn this into a small helper (e.g., `wire_bins_2d(region, src_layer_idx, Bh, Bw, Sh=None, Sw=None)`) that does the deterministic mapping/wiring for you while keeping everything else as-is.