---
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
- sequential
- flipflop
---

# threshold-jk-flipflop

JK flip-flop next-state logic as threshold circuit. Most versatile flip-flop with Set, Reset, Hold, and Toggle modes.

## Circuit

```
J      ──┐
K      ──┼──► JK-FF ──┬──► Q
Q_prev ──┘           └──► Qn
```

## Truth Table

| J | K | Q_prev | Q | Qn | Mode |
|---|---|--------|---|----|----|
| 0 | 0 | 0 | 0 | 1 | Hold |
| 0 | 0 | 1 | 1 | 0 | Hold |
| 0 | 1 | X | 0 | 1 | Reset |
| 1 | 0 | X | 1 | 0 | Set |
| 1 | 1 | 0 | 1 | 0 | Toggle |
| 1 | 1 | 1 | 0 | 1 | Toggle |

## Modes

```
J=0, K=0: Hold    - Q stays same
J=0, K=1: Reset   - Q becomes 0
J=1, K=0: Set     - Q becomes 1
J=1, K=1: Toggle  - Q flips
```

## Logic

```
Q  = J·~Q_prev + ~K·Q_prev
Qn = ~J·~Q_prev + K·Q_prev
```

## Architecture

| Component | Neurons |
|-----------|---------|
| Input inversions | 4 |
| AND gates | 4 |
| OR outputs | 2 |
| Passthroughs | 2 |

**Total: 12 neurons, 42 parameters, 3 layers**

## Advantage Over SR

Unlike SR flip-flop, JK has no invalid state. When both inputs are high, it toggles instead of producing undefined behavior.

## Usage

```python
from safetensors.torch import load_file

w = load_file('model.safetensors')

# Counter using toggle mode:
q = 0
for _ in range(4):
    q_next = compute(j=1, k=1, q_prev=q, w)
    q = q_next  # q toggles: 0,1,0,1,...
```

## Files

```
threshold-jk-flipflop/
├── model.safetensors
├── create_safetensors.py
├── config.json
└── README.md
```

## License

MIT