threshold-and / README.md
CharlesCNorton
Add optimality note: exhaustive enumeration confirms magnitude 4 is minimum
6e63966
---
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
---
# threshold-and
A 2-of-2 threshold gate. Both inputs must be active to reach the firing threshold.
## Circuit
```
x y
β”‚ β”‚
β””β”€β”¬β”€β”˜
β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”
β”‚ w: 1,1β”‚
β”‚ b: -2 β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”˜
β”‚
β–Ό
AND(x,y)
```
## Mechanism
Each input contributes +1 to the sum. The bias of -2 means exactly two contributions are required to reach zero:
| x | y | sum | output |
|---|---|-----|--------|
| 0 | 0 | -2 | 0 |
| 0 | 1 | -1 | 0 |
| 1 | 0 | -1 | 0 |
| 1 | 1 | 0 | 1 |
The bias acts as a vote threshold. With bias -2, you need 2 votes.
## Parameters
| | |
|---|---|
| Weights | [1, 1] |
| Bias | -2 |
| Total | 3 parameters |
## Optimality
Exhaustive enumeration of all 129 weight configurations at magnitudes 0-4 confirms this circuit is **already at minimum magnitude (4)**. There is exactly one valid configuration at magnitude 4, and no valid configurations exist below it.
## Properties
- Linearly separable (unlike XOR)
- Commutative, associative, idempotent
- Generalizes to n-input AND with weights all 1, bias -n
## Usage
```python
from safetensors.torch import load_file
import torch
w = load_file('model.safetensors')
def and_gate(x, y):
inputs = torch.tensor([float(x), float(y)])
return int((inputs * w['weight']).sum() + w['bias'] >= 0)
```
## Files
```
threshold-and/
β”œβ”€β”€ model.safetensors
β”œβ”€β”€ model.py
β”œβ”€β”€ config.json
└── README.md
```
## License
MIT