---
license: apache-2.0
library_name: pytorch
tags:
  - neuroscience
  - neural-network
  - connectome
  - brain
  - h01
  - cortex
  - biology
  - human-brain
  - temporal-cortex
  - brain-inspired
  - spiking-neural-network
  - reservoir-computing
pipeline_tag: other
datasets:
  - google/h01-release
---

# Nauro — H01 Human Cortex Connectome (Full)

The **complete** neuron-to-neuron connectivity matrix extracted from a
nanometer-resolution reconstruction of **human temporal cortex**
([H01 dataset](https://h01-release.storage.googleapis.com/data.html),
Google/Harvard/Lichtman Lab).

Built from all 166 Avro synapse shards (~32 GB raw data), filtered at
≥0.50 confidence. This is the full connectome — no spatial cropping.

## Summary

| Property | Value |
|----------|-------|
| Neurons | 16,087 |
| Excitatory | 10,531 (65.4%) |
| Inhibitory | 4,688 (29.1%) |
| Non-zero connections | 76,903 |
| Raw edges (pre-aggregation) | 116,611 |
| Connectivity density | 0.030% |
| Mean in-degree | 4.8 |
| Max in-degree | 70 |
| External inputs (total) | 27,022,313 |
| Volume | Full 1 mm³ |
| Cortical layers | L1–L6 + white matter |
| Build | All 166 GCS shards, min_confidence=0.50 |

## Connectivity by cortical layer

| Layer | Neurons | Exc | Inh | Internal connections | Density |
|-------|---------|-----|-----|---------------------|---------|
| Layer 1 | 827 | 85 | 586 | 55 | 0.008% |
| Layer 2 | 4,656 | 2,952 | 1,594 | 21,845 | 0.101% |
| Layer 3 | 2,692 | 1,673 | 965 | 11,018 | 0.152% |
| Layer 4 | 3,440 | 2,622 | 688 | 8,748 | 0.074% |
| Layer 5 | 2,313 | 1,665 | 505 | 6,252 | 0.117% |
| Layer 6 | 1,077 | 906 | 128 | 4,419 | 0.381% |
| White matter | 648 | 395 | 111 | 732 | 0.174% |

## Degree distribution

| Metric | In-degree | Out-degree |
|--------|-----------|------------|
| Mean | 4.8 | 4.8 |
| Std | 6.3 | 7.0 |
| Median | 3.0 | 2.0 |
| Max | 70 | 124 |

## Quick start

```python
import json, numpy as np, torch
from safetensors.torch import load_file

# Load everything
config  = json.load(open("config.json"))
weights = load_file("connectome.safetensors")["weights"]  # (16087, 16087)
meta    = np.load("metadata.npz", allow_pickle=True)
edges   = np.load("edges.npz")["edges"]                   # (116611, 3)

print(f"{config['n_neurons']} neurons, {config['n_synapses']} connections")
print(f"Weight matrix: {weights.shape}, density: {config['density']:.4%}")
```

### Load via HuggingFace Hub

```python
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import numpy as np

repo = "NathanRoll/h01-cortex-snn"
weights = load_file(hf_hub_download(repo, "connectome.safetensors"))["weights"]
meta = np.load(hf_hub_download(repo, "metadata.npz"), allow_pickle=True)
print(f"Loaded {weights.shape[0]} neurons")
```

### Reconstruct from edge list

```python
N = config["n_neurons"]
W = torch.zeros(N, N)
for pre, post, stype in edges:
    W[post, pre] += 1.0
# W[i, j] = number of synapses from neuron j → neuron i
```

## Files

| File | Description | Size |
|------|-------------|------|
| `connectome.safetensors` | Full 16,087×16,087 weight matrix | ~1 GB |
| `edges.npz` | Raw edge list `[pre, post, type]` | ~0.6 MB |
| `metadata.npz` | Positions, cell types, layers, segment IDs | ~0.3 MB |
| `somas_filtered.csv` | Neuron table (positions, types, layers) | ~1.1 MB |
| `config.json` | Build parameters + summary statistics | small |
| `layer_stats.json` | Per-layer connectivity statistics | small |

## Data source

The connectome data is from the
[H01 release](https://h01-release.storage.googleapis.com/data.html)
by Google Research and the Lichtman Laboratory at Harvard University.
The original 1.4 petabyte dataset was imaged via serial-section electron
microscopy at 4 nm × 4 nm × 33 nm resolution.

> Shapson-Coe, A. et al. "A petavoxel fragment of human cerebral cortex
> reconstructed at nanoscale resolution." *Science* 384, eadk4858 (2024).

## License

Apache 2.0. The underlying H01 data is subject to
[Google's release terms](https://h01-release.storage.googleapis.com/data.html).