Connectome-inspired sparse SNN: MNIST (96.83%) + Fashion-MNIST (87.07%) checkpoints, ~2 orders lower energy

README.md

@@ -0,0 +1,107 @@
+---
+tags:
+- spiking-neural-network
+- snn
+- sparse
+- brain-inspired
+- connectome
+- drosophila
+- mnist
+- fashion-mnist
+- image-classification
+- awareliquid
+pipeline_tag: image-classification
+---
+
+# Sparse-SNN — connectome-inspired sparse spiking networks
+
+**Sparse spiking neural networks whose structural masks are derived from the
+statistical laws of the real *Drosophila* connectome** — matching dense MLP
+accuracy at ~2 orders of magnitude lower energy.
+
+Two trained classifiers: MNIST and Fashion-MNIST. Architecture: `784 → 800 → 10`
+with 5% connection density, LIF neurons and surrogate-gradient training
+(straight-through estimator with sigmoid gradient).
+
+## Files
+
+| File | Description |
+|---|---|
+| `sparse_snn_mnist.pt` | MNIST checkpoint — **96.83%** test accuracy (firing rate 7.9%) |
+| `sparse_snn_fashion.pt` | Fashion-MNIST checkpoint — **87.07%** test accuracy (firing rate 8.2%) |
+| `train_results.json` | Raw metrics from the training runs |
+
+Each checkpoint contains the `state_dict` (weights + mask buffer), the E/I sign
+matrix, the full config, and metrics.
+
+## Measured results (these checkpoints)
+
+| Task | Dense MLP (documented) | Sparse SNN (this checkpoint) | Gap | Energy saving* |
+|---|---|---|---|---|
+| MNIST | 98.32% | **96.83%** | 1.5 pts | **~112×** |
+| Fashion-MNIST | 87.56% | **87.07%** | 0.5 pts | **~107×** |
+
+\* Energy estimate using the repository's 45nm CMOS model (Horowitz 2014:
+MAC = 3.7 pJ, addition = 0.9 pJ) with this checkpoint's measured firing rate —
+event-driven spike ops are additions only. This is a **model-based estimate,
+not measured on hardware**. The project's documented headline range across runs
+is 105–106×.
+
+## What the research found (and disproved)
+
+1. **Disproved**: the MaleCNS connectome topology as a *static* structure
+   carries **no measurable advantage** over random / structured-sparse graphs
+   (5 experiments: classification, temporal, robustness, sample-efficiency,
+   plasticity — all indistinguishable).
+2. **Extracted** — the topology's *statistical laws* that DO matter:
+   sparsity **0.09%**, long-tail degree distribution (scale-free, max/mean ≈ 75),
+   strong small-worldness (clustering 6.65× random, path length 2.39),
+   **E/I ratio 60/40**.
+3. **Built**: a trainable sparse SNN from these laws → near-lossless accuracy
+   at ~105–112× energy savings.
+
+## Usage
+
+Requires the model code from the repository (the class is small and
+self-contained):
+
+```python
+import torch
+from sparse_snn import SparseSNN   # github.com/AwareLiquid/human-brain-simulation
+
+ckpt = torch.load("sparse_snn_mnist.pt", map_location="cpu", weights_only=False)
+cfg = ckpt["config"]
+model = SparseSNN(cfg["in_dim"], cfg["hid_dim"], cfg["out_dim"],
+                  ckpt["state_dict"]["mask"], ckpt["sign"],
+                  T=cfg["T"], decay=cfg["decay"], threshold=cfg["threshold"])
+model.load_state_dict(ckpt["state_dict"])
+model.eval()
+# inputs: flattened 28x28 grayscale in [0, 1]
+```
+
+Loading was verified to reproduce the saved accuracy bit-exactly
+(`acc=0.9683` MNIST / `acc=0.8707` Fashion on reload).
+
+## Honest boundaries
+
+- The accuracy gap vs a dense MLP is small but real (1.5 pts MNIST, 0.5 pts
+  Fashion) — "near-lossless", not lossless.
+- The connectome's *static topology* gave no advantage; only its statistical
+  laws transferred. Do not attribute the result to "copying the fly brain".
+- Energy figures are analytic (45nm CMOS), not measured on neuromorphic
+  hardware.
+- Recurrent spiking training remains an open problem in this line (sMNIST from
+  scratch reaches only ~70%; conversion methods do better but lose efficiency).
+- Masks are generated with a fixed seed (`mask_seed=0`) — rerun the code in the
+  repository to regenerate the exact structure.
+
+## Related
+
+- Code & full experiment report: [AwareLiquid/human-brain-simulation](https://github.com/AwareLiquid/human-brain-simulation)
+- [AwareLiquid/M1](https://github.com/AwareLiquid/M1) — MT-LNN liquid architecture
+- [awareliquid.ai](https://awareliquid.ai) — benchmarks and retractions
+
+## License
+
+MIT. MaleCNS connectome data is CC-BY 4.0 (HHMI Janelia / Cambridge / Google
+Research, male-cns.janelia.org) — not redistributed here.

sparse_snn_fashion.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c19547d6cfb35b0fd06f77b6c9e9a78d6854b062d3105a33d39b9fb1b0b34525
+size 7561291

sparse_snn_mnist.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:01c9ee1fb29fd831e049717dac6939ed70ab49c793db8546440a5e68a082471f
+size 7561271

train_results.json

@@ -0,0 +1,16 @@
+{
+ "mnist": {
+  "test_acc": 0.9682999849319458,
+  "firing_rate": 0.07921598106622696,
+  "train_seconds": 51.9,
+  "train_samples": 60000,
+  "device": "cuda"
+ },
+ "fashion": {
+  "test_acc": 0.8707000017166138,
+  "firing_rate": 0.08240412175655365,
+  "train_seconds": 61.7,
+  "train_samples": 60000,
+  "device": "cuda"
+ }
+}