# Pruned Variant

Minimal subnetwork extracted from the full model via neuron ablation analysis.

## Architecture

| Layer | Full | Pruned |
|-------|------|--------|
| Hidden 1 | 32 | 11 |
| Hidden 2 | 16 | 3 |
| Parameters | 833 | 139 |

## Pruning Method

Systematic single-neuron ablation identified which neurons are critical (their removal degrades accuracy) versus redundant (their removal has no effect). Only 11 of 32 Layer-1 neurons and 3 of 16 Layer-2 neurons were critical. The minimal subnetwork retains only these neurons.

## Verification

Correctness is proven in Coq for all 256 inputs, identical to the full network. See `PrunedThresholdLogic.v` in the [GitHub repository](https://github.com/CharlesCNorton/threshold-logic-verified).

## Usage

```python
from model import PrunedThresholdNetwork

model = PrunedThresholdNetwork.from_safetensors('model.safetensors')
```