metadata
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
- sequential
- latch
threshold-sr-latch
SR (Set-Reset) latch next-state logic as threshold circuit. Computes next state given current inputs and previous state.
Circuit
S ──┐
R ──┼──► SR Latch ──┬──► Q
Q_prev ──┘ └──► Qn
Truth Table
| S | R | 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 | X | 0 | 0 | Invalid |
Classic NOR Implementation
S ──┬──►[NOR]──► Qn
│ ▲
│ │
│ └─────┐
│ │
R ──┴──►[NOR]──► Q
▲ │
│ │
└───────┘
Cross-coupled NOR gates create feedback loop.
Combinational Representation
This circuit models the next-state function:
- Input: S, R, and Q_prev (previous Q)
- Output: Q_next, Qn_next
True latch behavior requires feeding Q output back to Q_prev over time.
Architecture
| Component | Neurons |
|---|---|
| Input inversions | 6 |
| Hold logic | 2 |
| Output gates | 3 |
Total: 11 neurons, 39 parameters, 4 layers
Usage
from safetensors.torch import load_file
w = load_file('model.safetensors')
# Simulate latch over time:
q = 0
for s, r in [(1,0), (0,0), (0,1), (0,0)]:
q_next = compute_q(s, r, q, w)
q = q_next
Files
threshold-sr-latch/
├── model.safetensors
├── create_safetensors.py
├── config.json
└── README.md
License
MIT