Upload Nauro H01 cortex network — connectome, model, and visualizations

.gitattributes

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README.md

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+---
+license: apache-2.0
+library_name: pytorch
+tags:
+  - neuroscience
+  - neural-network
+  - connectome
+  - brain
+  - h01
+  - cortex
+  - biology
+  - human-brain
+  - temporal-cortex
+  - brain-inspired
+pipeline_tag: other
+datasets:
+  - google/h01-release
+---
+
+# Nauro — H01 Human Cortex Neural Network
+
+A **neural network** whose units and connections are derived *exactly* from a
+nanometer-resolution reconstruction of **human temporal cortex**
+([H01 dataset](https://h01-release.storage.googleapis.com/data.html),
+Google/Harvard/Lichtman Lab).
+
+## What is this?
+
+This repository contains a fully initialized PyTorch `nn.Module` with
+**2,106 units** and **3,041 connections** arranged in the
+exact same pattern as real neurons and synapses in a full of 1 mm³ sample of
+a human brain.
+
+Every unit occupies its real 3-D soma position.  Every connection carries its
+measured synaptic strength.  Dale's law is enforced: excitatory neurons
+contribute only positive current, inhibitory neurons only negative.
+
+| Property | Value |
+|----------|-------|
+| Neurons | 2,106 |
+| Excitatory | 1,466 |
+| Inhibitory | 475 |
+| Synapses (non-zero) | 3,041 |
+| Connectivity density | 0.07% |
+| External inputs | 2,576,814 |
+| Volume | full of 1 mm³ |
+
+## Architecture
+
+| Parameter | Value |
+|-----------|-------|
+| Network type | Recurrent (connectome topology) |
+| Activation | relu |
+| Inhibitory gain | ×4 |
+| Weight scaling | 1 / mean_in_degree |
+| Dale's law | Enforced (frozen sign mask) |
+
+## Connectivity by cortical layer
+
+| Layer | Neurons | Exc | Inh | Connections | Density |
+|-------|---------|-----|-----|-------------|---------|
+| Layer 1 | 48 | 18 | 3 | 0 | 0.0000% |
+| Layer 2 | 120 | 66 | 43 | 51 | 0.3542% |
+| Layer 3 | 320 | 200 | 106 | 420 | 0.4102% |
+| Layer 4 | 1,002 | 748 | 196 | 1,367 | 0.1362% |
+| Layer 5 | 441 | 314 | 100 | 385 | 0.1980% |
+| Layer 6 | 106 | 80 | 13 | 112 | 0.9968% |
+| White matter | 3 | 0 | 0 | 1 | 11.1111% |
+| unclassified | 66 | 40 | 14 | 8 | 0.1837% |
+
+## Degree distribution
+
+| Metric | In-degree | Out-degree |
+|--------|-----------|------------|
+| Mean | 1.4 | 1.4 |
+| Std | 1.9 | 2.1 |
+| Median | 1.0 | 1.0 |
+| Max | 17 | 24 |
+
+## Network activity
+
+| Metric | Value |
+|--------|-------|
+| Steps | 100 |
+| Mean activation | nan |
+| Max activation | nan |
+| Active neurons | 0/2106 (0%) |
+
+![Simulation](simulation.png)
+![Brain comparison](brain_comparison.png)
+
+
+## Multi-angle views
+
+### Bottom Up
+| Biological | Simulated |
+|---|---|
+| ![bio_bottom_up](views/bio_bottom_up.png) | ![sim_bottom_up](views/sim_bottom_up.png) |
+
+### Front
+| Biological | Simulated |
+|---|---|
+| ![bio_front](views/bio_front.png) | ![sim_front](views/sim_front.png) |
+
+### Rear
+| Biological | Simulated |
+|---|---|
+| ![bio_rear](views/bio_rear.png) | ![sim_rear](views/sim_rear.png) |
+
+### Side
+| Biological | Simulated |
+|---|---|
+| ![bio_side](views/bio_side.png) | ![sim_side](views/sim_side.png) |
+
+### Top
+| Biological | Simulated |
+|---|---|
+| ![bio_top](views/bio_top.png) | ![sim_top](views/sim_top.png) |
+
+
+
+## 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"]  # (N, N) synapse counts
+meta    = np.load("metadata.npz", allow_pickle=True)
+edges   = np.load("edges.npz")["edges"]                   # (M, 3) [pre, post, type]
+
+print(f"{config['n_neurons']} neurons, {config['n_synapses']} synapses")
+print(f"Positions shape: {meta['positions'].shape}")
+print(f"Edge list shape: {edges.shape}")
+
+# Load the initialized network model
+state_dict = load_file("model.safetensors")
+```
+
+### Reconstruct the connectome from the 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
+```
+
+### Access neuron metadata
+
+```python
+import pandas as pd
+somas = pd.read_csv("somas_filtered.csv")
+print(somas[["celltype", "layer"]].value_counts())
+```
+
+## Files
+
+| File | Description | Size hint |
+|------|-------------|-----------|
+| `model.safetensors` | Full model state dict (weights, mask, positions) | ~NxN×4 bytes |
+| `connectome.safetensors` | Raw NxN weight matrix | ~NxN×4 bytes |
+| `edges.npz` | Filtered edge list `[pre, post, type]` | ~M×12 bytes |
+| `metadata.npz` | Positions, types, layers, segment IDs | small |
+| `somas_filtered.csv` | Filtered neuron table from H01 | ~1 MB |
+| `config.json` | All hyperparameters + build stats | small |
+| `layer_stats.json` | Per-layer connectivity statistics | small |
+| `camera_params.json` | Exact camera angles for all views | small |
+| `simulation.png` | Activation heatmap + mean activity + traces | ~300 KB |
+| `brain_comparison.png` | Side-by-side bio vs sim (main angle) | ~1 MB |
+| `views/` | Multi-angle 3D renders (bio + sim) | ~5 MB |
+
+## 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).

camera_params.json

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+{
+  "front": {
+    "elevation": 15.0,
+    "azimuth": -60.0
+  },
+  "top": {
+    "elevation": 80.0,
+    "azimuth": -60.0
+  },
+  "side": {
+    "elevation": 15.0,
+    "azimuth": 0.0
+  },
+  "rear": {
+    "elevation": 15.0,
+    "azimuth": 120.0
+  },
+  "bottom_up": {
+    "elevation": -30.0,
+    "azimuth": -60.0
+  }
+}

config.json

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+{
+  "gcs_bucket": "h01-release",
+  "somas_path": "data/20210601/c3/tables/somas.csv",
+  "synapse_export_prefix": "data/20210729/c3/synapses/exported/export",
+  "n_synapse_shards": 166,
+  "data_dir": "data",
+  "min_confidence": 0.5,
+  "neuron_types": [
+    "PYRAMIDAL",
+    "INTERNEURON",
+    "SPINY_STELLATE",
+    "SPINY_ATYPICAL",
+    "SPINY_NONPYRAMIDAL",
+    "UNCLASSIFIED_NEURON"
+  ],
+  "excitatory_types": [
+    "PYRAMIDAL",
+    "SPINY_STELLATE",
+    "SPINY_ATYPICAL",
+    "SPINY_NONPYRAMIDAL"
+  ],
+  "inhibitory_types": [
+    "INTERNEURON"
+  ],
+  "inhibitory_gain": 4.0,
+  "activation": "relu",
+  "crop_fraction": null,
+  "n_steps": 100,
+  "input_scale": 0.1,
+  "device": "cuda",
+  "n_neurons": 2106,
+  "n_synapses": 3041,
+  "n_excitatory": 1466,
+  "n_inhibitory": 475,
+  "density": 0.0006856455890960481,
+  "n_external_inputs": 2576814,
+  "n_edges_raw": 4885
+}

connectome.safetensors

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edges.npz

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layer_stats.json

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+{
+  "layers": [
+    {
+      "layer": "Layer 1",
+      "n_neurons": 48,
+      "n_excitatory": 18,
+      "n_inhibitory": 3,
+      "internal_connections": 0,
+      "density": 0.0
+    },
+    {
+      "layer": "Layer 2",
+      "n_neurons": 120,
+      "n_excitatory": 66,
+      "n_inhibitory": 43,
+      "internal_connections": 51,
+      "density": 0.003542
+    },
+    {
+      "layer": "Layer 3",
+      "n_neurons": 320,
+      "n_excitatory": 200,
+      "n_inhibitory": 106,
+      "internal_connections": 420,
+      "density": 0.004102
+    },
+    {
+      "layer": "Layer 4",
+      "n_neurons": 1002,
+      "n_excitatory": 748,
+      "n_inhibitory": 196,
+      "internal_connections": 1367,
+      "density": 0.001362
+    },
+    {
+      "layer": "Layer 5",
+      "n_neurons": 441,
+      "n_excitatory": 314,
+      "n_inhibitory": 100,
+      "internal_connections": 385,
+      "density": 0.00198
+    },
+    {
+      "layer": "Layer 6",
+      "n_neurons": 106,
+      "n_excitatory": 80,
+      "n_inhibitory": 13,
+      "internal_connections": 112,
+      "density": 0.009968
+    },
+    {
+      "layer": "White matter",
+      "n_neurons": 3,
+      "n_excitatory": 0,
+      "n_inhibitory": 0,
+      "internal_connections": 1,
+      "density": 0.111111
+    },
+    {
+      "layer": "unclassified",
+      "n_neurons": 66,
+      "n_excitatory": 40,
+      "n_inhibitory": 14,
+      "internal_connections": 8,
+      "density": 0.001837
+    }
+  ],
+  "degrees": {
+    "in_degree_mean": 1.4,
+    "in_degree_std": 1.9,
+    "in_degree_max": 17,
+    "in_degree_median": 1.0,
+    "out_degree_mean": 1.4,
+    "out_degree_std": 2.1,
+    "out_degree_max": 24,
+    "out_degree_median": 1.0
+  }
+}

metadata.npz

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views/camera_params.json

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+{
+  "front": {
+    "elevation": 15.0,
+    "azimuth": -60.0
+  },
+  "top": {
+    "elevation": 80.0,
+    "azimuth": -60.0
+  },
+  "side": {
+    "elevation": 15.0,
+    "azimuth": 0.0
+  },
+  "rear": {
+    "elevation": 15.0,
+    "azimuth": 120.0
+  },
+  "bottom_up": {
+    "elevation": -30.0,
+    "azimuth": -60.0
+  }
+}