# Эксперимент: PPMI-Gated BCM Sparse Graph Encoder (#999)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built a NumPy MVP of PPMI computation, normalized gating, local BCM updates, threshold tracking, and exact-k binary SDR inference in ppmi_bcm_experiment.py. The mechanism manifested: zero PPMI produced exactly zero updates across 8 inputs, update norm scaled linearly with gate from 0 to 1 at relative error below 5e-15, and the BCM threshold closed form matched the iterative update within 9e-16 while all potentiation/depression sign cases matched prediction. The downstream toy result was not a win: SDR accuracy was 0.625 versus 0.825 for the dense random baseline, although storage was 32x smaller (4 versus 128 bytes/node).",
  "metrics": {
    "baseline": "Toy SBM dense 32-float random projection: accuracy=0.825, storage=128 bytes/node",
    "idea": "PPMI-gated BCM 32-column, k=4 binary SDR: accuracy=0.625, storage=4 bytes/node; zero-gate maximum update norm=0, gate-scaling relative error <5e-15, threshold closed-form maximum error=8.9e-16"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 ppmi_bcm_experiment.py",
  "files": [
    "ppmi_bcm_experiment.py",
    "results.json"
  ],
  "limitations": "Only a synthetic 80-node SBM and a dense random-projection baseline were tested; no Cora/PubMed, node2vec, GraphSAGE, link prediction, corruption robustness, streaming count-min sketch, GPU implementation, wall-clock scalability, or multi-seed statistical comparison was evaluated."
}