---
license: other
license_name: polyform-small-business-1.0.0
license_link: https://polyformproject.org/licenses/small-business/1.0.0/
library_name: pytorch
pipeline_tag: image-classification
tags:
  - bio-inspired
  - neuroscience
  - lightweight
  - medical-imaging
  - edge-ai
  - retinal-ganglion-cells
  - fibonacci-strides
datasets:
  - kvasir-v2
  - cifar-10
  - cifar-100
  - imagenet-100
---

# MPKNet V6 - Bio-Inspired Visual Classification

A lightweight neural network inspired by the primate Lateral Geniculate Nucleus (LGN), implementing parallel **Magnocellular (M)**, **Parvocellular (P)**, and **Koniocellular (K)** pathways with Fibonacci-stride spatial sampling.

## Architecture

MPKNet V6 uses three parallel pathways with biologically-motivated stride ratios (2:3:5):

- **P pathway** (stride 2): Fine detail and edges, analogous to Parvocellular neurons (~80% of LGN)
- **K pathway** (stride 3): Context signals that generate gating modulation, analogous to Koniocellular neurons (~10% of LGN)
- **M pathway** (stride 5): Global structure and coarse features, analogous to Magnocellular neurons (~10% of LGN)

The **K-gating mechanism** dynamically modulates P and M pathways via learned sigmoid gates, inspired by cross-stream modulation in biological vision.

## Results

| Dataset | Classes | Accuracy | Parameters |
|---------|---------|----------|------------|
| Kvasir-v2 (GI endoscopy) | 8 | 89.2% | 0.21M |
| CIFAR-10 | 10 | 89.4% | 0.54M |
| CIFAR-100 | 100 | 58.8% | 0.22M |
| ImageNet-100 | 100 | 60.8% | 0.54M |

No pretraining. No augmentation. 161x fewer parameters than MobileNetV3-Small.

## Usage

```python
import torch
from mpknet_v6 import BinocularMPKNetV6
from mpknet_components import count_params

# Load model
model = BinocularMPKNetV6(num_classes=8, ch=48, use_stereo=True)
state_dict = torch.load("v6_kvasir_best.pth", map_location="cpu", weights_only=True)
model.load_state_dict(state_dict)
model.eval()

# Inference
x = torch.randn(1, 3, 224, 224)
with torch.no_grad():
    logits = model(x)
    pred = logits.argmax(dim=1)
```

## Files

- `v6_kvasir_best.pth` - Trained weights (Kvasir-v2, 8 classes, 2.1MB)
- `mpknet_v6.py` - Model architecture
- `mpknet_components.py` - Shared components (RGCLayer, BinocularPreMPK, StereoDisparity, StridedMonocularBlock)

## Citation

```
D.J. Lougen, "MPKNet: Bio-Inspired Visual Classification with Parallel LGN Pathways", 2025.
```

## License

PolyForm Small Business License 1.0.0 - Free for organizations with less than $100K revenue, non-profits, and education.

## Links

- [GitHub](https://github.com/DJLougen/MPKnet)