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

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.

DJLougen
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mpknet-v6